






'''simple docstring'''

import argparse
import json
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, ResNetConfig
from transformers.utils import logging


logging.set_verbosity_info()
SCREAMING_SNAKE_CASE__											=     logging.get_logger(__name__)







def 					lowercase__				(   __UpperCamelCase					)->				int:
      # initialize config
      if "resnet-50" in model_name:
            UpperCamelCase       						=      ResNetConfig.from_pretrained("""microsoft/resnet-50"""					)
      elif "resnet-101" in model_name:
            UpperCamelCase       						=      ResNetConfig.from_pretrained("""microsoft/resnet-101"""					)
      else:
            raise ValueError("""Model name should include either resnet50 or resnet101"""					)

      UpperCamelCase       						=      DetrConfig(use_timm_backbone=__UpperCamelCase	,		backbone_config=__UpperCamelCase					)

      # set label attributes
      UpperCamelCase       						=      """panoptic""" in model_name
      if is_panoptic:
            UpperCamelCase       						=      250
      else:
            UpperCamelCase       						=      91
            UpperCamelCase       						=      """huggingface/label-files"""
            UpperCamelCase       						=      """coco-detection-id2label.json"""
            UpperCamelCase       						=      json.load(open(hf_hub_download(__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, is_panoptic







def 					lowercase__				(   __UpperCamelCase					)->				List[Any]:
      # here we list all keys to be renamed (original name on the left, our name on the right)
      UpperCamelCase       						=      []

      # stem
      # fmt: off
      rename_keys.append(("""backbone.0.body.conv1.weight""", """backbone.conv_encoder.model.embedder.embedder.convolution.weight""")					)
      rename_keys.append(("""backbone.0.body.bn1.weight""", """backbone.conv_encoder.model.embedder.embedder.normalization.weight""")					)
      rename_keys.append(("""backbone.0.body.bn1.bias""", """backbone.conv_encoder.model.embedder.embedder.normalization.bias""")					)
      rename_keys.append(("""backbone.0.body.bn1.running_mean""", """backbone.conv_encoder.model.embedder.embedder.normalization.running_mean""")					)
      rename_keys.append(("""backbone.0.body.bn1.running_var""", """backbone.conv_encoder.model.embedder.embedder.normalization.running_var""")					)
      # stages
      for stage_idx in range(len(config.backbone_config.depths					)					):
            for layer_idx in range(config.backbone_config.depths[stage_idx]					):
                  # shortcut
                  if layer_idx == 0:
                        rename_keys.append(
                            (
                                F"backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.0.weight",
                                F"backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.convolution.weight",
                            )					)
                        rename_keys.append(
                            (
                                F"backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.weight",
                                F"backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.weight",
                            )					)
                        rename_keys.append(
                            (
                                F"backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.bias",
                                F"backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.bias",
                            )					)
                        rename_keys.append(
                            (
                                F"backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.running_mean",
                                F"backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.running_mean",
                            )					)
                        rename_keys.append(
                            (
                                F"backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.running_var",
                                F"backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.running_var",
                            )					)
                  # 3 convs
                  for i in range(3					):
                        rename_keys.append(
                            (
                                F"backbone.0.body.layer{stage_idx + 1}.{layer_idx}.conv{i+1}.weight",
                                F"backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.convolution.weight",
                            )					)
                        rename_keys.append(
                            (
                                F"backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.weight",
                                F"backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.weight",
                            )					)
                        rename_keys.append(
                            (
                                F"backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.bias",
                                F"backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.bias",
                            )					)
                        rename_keys.append(
                            (
                                F"backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.running_mean",
                                F"backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.running_mean",
                            )					)
                        rename_keys.append(
                            (
                                F"backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.running_var",
                                F"backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.running_var",
                            )					)
    # fmt: on

      for i in range(config.encoder_layers					):
            # 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"""),
          ]					)

      return rename_keys







def 					lowercase__				(   __UpperCamelCase	,		__UpperCamelCase	,		__UpperCamelCase					)->				Optional[int]:
      UpperCamelCase       						=      state_dict.pop(__UpperCamelCase					)
      UpperCamelCase       						=      val







def 					lowercase__				(   __UpperCamelCase	,		__UpperCamelCase=False					)->				Optional[Any]:
      UpperCamelCase       						=      """"""
      if is_panoptic:
            UpperCamelCase       						=      """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)
            UpperCamelCase       						=      state_dict.pop(F"{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight"					)
            UpperCamelCase       						=      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
            UpperCamelCase       						=      in_proj_weight[:256, :]
            UpperCamelCase       						=      in_proj_bias[:256]
            UpperCamelCase       						=      in_proj_weight[256:512, :]
            UpperCamelCase       						=      in_proj_bias[256:512]
            UpperCamelCase       						=      in_proj_weight[-256:, :]
            UpperCamelCase       						=      in_proj_bias[-256:]
      # 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
            UpperCamelCase       						=      state_dict.pop(F"{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight"					)
            UpperCamelCase       						=      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
            UpperCamelCase       						=      in_proj_weight[:256, :]
            UpperCamelCase       						=      in_proj_bias[:256]
            UpperCamelCase       						=      in_proj_weight[256:512, :]
            UpperCamelCase       						=      in_proj_bias[256:512]
            UpperCamelCase       						=      in_proj_weight[-256:, :]
            UpperCamelCase       						=      in_proj_bias[-256:]
            # read in weights + bias of input projection layer of cross-attention
            UpperCamelCase       						=      state_dict.pop(
                F"{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight"					)
            UpperCamelCase       						=      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
            UpperCamelCase       						=      in_proj_weight_cross_attn[:256, :]
            UpperCamelCase       						=      in_proj_bias_cross_attn[:256]
            UpperCamelCase       						=      in_proj_weight_cross_attn[256:512, :]
            UpperCamelCase       						=      in_proj_bias_cross_attn[256:512]
            UpperCamelCase       						=      in_proj_weight_cross_attn[-256:, :]
            UpperCamelCase       						=      in_proj_bias_cross_attn[-256:]







def 					lowercase__				(   )->				Optional[Any]:
      UpperCamelCase       						=      """http://images.cocodataset.org/val2017/000000039769.jpg"""
      UpperCamelCase       						=      Image.open(requests.get(__UpperCamelCase	,		stream=__UpperCamelCase					).raw					)

      return im







@torch.no_grad()
def 					lowercase__				(   __UpperCamelCase	,		__UpperCamelCase=None	,		__UpperCamelCase=False					)->				Optional[Any]:
      UpperCamelCase						,UpperCamelCase       						=      get_detr_config(__UpperCamelCase					)

      # load original model from torch hub
      UpperCamelCase       						=      {
          """detr-resnet-50""": """detr_resnet50""",
          """detr-resnet-101""": """detr_resnet101""",
      }
      logger.info(F"Converting model {model_name}..."					)
      UpperCamelCase       						=      torch.hub.load("""facebookresearch/detr"""	,		model_name_to_original_name[model_name]	,		pretrained=__UpperCamelCase					).eval()
      UpperCamelCase       						=      detr.state_dict()
      # rename keys
      for src, dest in create_rename_keys(__UpperCamelCase					):
            if is_panoptic:
                  UpperCamelCase       						=      """detr.""" + src
            rename_key(__UpperCamelCase	,		__UpperCamelCase	,		__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
      UpperCamelCase       						=      """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"""					)
                  ):
                        UpperCamelCase       						=      state_dict.pop(__UpperCamelCase					)
                        UpperCamelCase       						=      val
                  elif "class_labels_classifier" in key or "bbox_predictor" in key:
                        UpperCamelCase       						=      state_dict.pop(__UpperCamelCase					)
                        UpperCamelCase       						=      val
                  elif key.startswith("""bbox_attention"""					) or key.startswith("""mask_head"""					):
                        continue
                  else:
                        UpperCamelCase       						=      state_dict.pop(__UpperCamelCase					)
                        UpperCamelCase       						=      val
            else:
                  if not key.startswith("""class_labels_classifier"""					) and not key.startswith("""bbox_predictor"""					):
                        UpperCamelCase       						=      state_dict.pop(__UpperCamelCase					)
                        UpperCamelCase       						=      val

    # finally, create HuggingFace model and load state dict
      UpperCamelCase       						=      DetrForSegmentation(__UpperCamelCase					) if is_panoptic else DetrForObjectDetection(__UpperCamelCase					)
      model.load_state_dict(__UpperCamelCase					)
      model.eval()

      # verify our conversion on an image
      UpperCamelCase       						=      """coco_panoptic""" if is_panoptic else """coco_detection"""
      UpperCamelCase       						=      DetrImageProcessor(format=__UpperCamelCase					)

      UpperCamelCase       						=      processor(images=prepare_img()	,		return_tensors="""pt"""					)
      UpperCamelCase       						=      encoding["""pixel_values"""]

      UpperCamelCase       						=      detr(__UpperCamelCase					)
      UpperCamelCase       						=      model(__UpperCamelCase					)

      assert torch.allclose(outputs.logits	,		original_outputs["""pred_logits"""]	,		atol=1E-3					)
      assert torch.allclose(outputs.pred_boxes	,		original_outputs["""pred_boxes"""]	,		atol=1E-3					)
      if is_panoptic:
            assert torch.allclose(outputs.pred_masks	,		original_outputs["""pred_masks"""]	,		atol=1E-4					)
      print("""Looks ok!"""					)

      if pytorch_dump_folder_path is not None:
            # 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					)
            processor.save_pretrained(__UpperCamelCase					)

      if push_to_hub:
            # Upload model and image processor to the hub
            logger.info("""Uploading PyTorch model and image processor to the hub..."""					)
            model.push_to_hub(F"nielsr/{model_name}"					)
            processor.push_to_hub(F"nielsr/{model_name}"					)


if __name__ == "__main__":
     SCREAMING_SNAKE_CASE__											=     argparse.ArgumentParser()

     parser.add_argument(
         '--model_name',
         default='detr-resnet-50',
         type=str,
         choices=['detr-resnet-50', 'detr-resnet-101'],
         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.'
     )
     parser.add_argument('--push_to_hub', action='store_true', help='Whether to push the model to the hub or not.')
     SCREAMING_SNAKE_CASE__											=     parser.parse_args()
     convert_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)








'''simple docstring'''

from typing import TYPE_CHECKING

from ...utils import _LazyModule


SCREAMING_SNAKE_CASE__											=     {'tokenization_wav2vec2_phoneme': ['Wav2Vec2PhonemeCTCTokenizer']}


if TYPE_CHECKING:
     from .tokenization_wavaveca_phoneme import WavaVecaPhonemeCTCTokenizer
else:
     import sys

     SCREAMING_SNAKE_CASE__											=     _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
