code: 
  
  """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
  from transformers.utils import logging
  
  
  logging.set_verbosity_info()
  _A						=     logging.get_logger(__name__)
  
  
  
  
  def 				a__	(	)  ->   Tuple:
        UpperCAmelCase__ :       Any						=						DetaConfig(
            num_queries=9_00 ,  encoder_ffn_dim=20_48 ,  decoder_ffn_dim=20_48 ,  num_feature_levels=5 ,  assign_first_stage=lowerCAmelCase ,  with_box_refine=lowerCAmelCase ,  two_stage=lowerCAmelCase ,  )
  
        # set labels
        UpperCAmelCase__ :       Tuple						=						91
        UpperCAmelCase__ :       Optional[int]						=						"""huggingface/label-files"""
        UpperCAmelCase__ :       str						=						"""coco-detection-id2label.json"""
        UpperCAmelCase__ :       Optional[int]						=						json.load(open(cached_download(hf_hub_url(lowerCAmelCase ,  lowerCAmelCase ,  repo_type="""dataset"""						)						) ,  """r"""						)						)
        UpperCAmelCase__ :       Optional[Any]						=						{int(lowerCAmelCase						): v for k, v in idalabel.items()}
        UpperCAmelCase__ :       str						=						idalabel
        UpperCAmelCase__ :       List[str]						=						{v: k for k, v in idalabel.items()}
  
        return config
  
  
  
  
  def 				a__	(	lowerCAmelCase						)  ->   List[str]:
        UpperCAmelCase__ :       Optional[Any]						=						[]
  
        # stem
        # fmt: off
        rename_keys.append(("""backbone.0.body.conv1.weight""", """model.backbone.model.embedder.embedder.convolution.weight""")						)
        rename_keys.append(("""backbone.0.body.bn1.weight""", """model.backbone.model.embedder.embedder.normalization.weight""")						)
        rename_keys.append(("""backbone.0.body.bn1.bias""", """model.backbone.model.embedder.embedder.normalization.bias""")						)
        rename_keys.append(("""backbone.0.body.bn1.running_mean""", """model.backbone.model.embedder.embedder.normalization.running_mean""")						)
        rename_keys.append(("""backbone.0.body.bn1.running_var""", """model.backbone.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"""model.backbone.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"""model.backbone.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"""model.backbone.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"""model.backbone.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"""model.backbone.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"""model.backbone.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"""model.backbone.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"""model.backbone.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"""model.backbone.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"""model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.running_var""",
                              )						)
      # 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__	(	lowerCAmelCase ,  lowerCAmelCase ,  lowerCAmelCase						)  ->   Any:
        UpperCAmelCase__ :       Optional[Any]						=						dct.pop(lowerCAmelCase						)
        UpperCAmelCase__ :       Any						=						val
  
  
  
  
  def 				a__	(	lowerCAmelCase ,  lowerCAmelCase						)  ->   int:
        # transformer decoder self-attention layers
        UpperCAmelCase__ :       str						=						config.d_model
        for i in range(config.decoder_layers						):
              # read in weights + bias of input projection layer of self-attention
              UpperCAmelCase__ :       Any						=						state_dict.pop(F"""transformer.decoder.layers.{i}.self_attn.in_proj_weight"""						)
              UpperCAmelCase__ :       Optional[int]						=						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__ :       int						=						in_proj_weight[:hidden_size, :]
              UpperCAmelCase__ :       str						=						in_proj_bias[:hidden_size]
              UpperCAmelCase__ :       Dict						=						in_proj_weight[
                  hidden_size : hidden_size * 2, :
              ]
              UpperCAmelCase__ :       List[Any]						=						in_proj_bias[hidden_size : hidden_size * 2]
              UpperCAmelCase__ :       List[str]						=						in_proj_weight[-hidden_size:, :]
              UpperCAmelCase__ :       List[Any]						=						in_proj_bias[-hidden_size:]
  
  
  
  
  def 				a__	(	)  ->   List[str]:
        UpperCAmelCase__ :       Union[str, Any]						=						"""http://images.cocodataset.org/val2017/000000039769.jpg"""
        UpperCAmelCase__ :       List[str]						=						Image.open(requests.get(lowerCAmelCase ,  stream=lowerCAmelCase						).raw						)
  
        return im
  
  
  
  
  @torch.no_grad()
  def 				a__	(	lowerCAmelCase ,  lowerCAmelCase ,  lowerCAmelCase						)  ->   Tuple:
        UpperCAmelCase__ :       List[str]						=						get_deta_config()
  
        # load original state dict
        if model_name == "deta-resnet-50":
              UpperCAmelCase__ :       Optional[int]						=						"""adet_checkpoint0011.pth"""
        elif model_name == "deta-resnet-50-24-epochs":
              UpperCAmelCase__ :       List[str]						=						"""adet_2x_checkpoint0023.pth"""
        else:
              raise ValueError(F"""Model name {model_name} not supported"""						)
        UpperCAmelCase__ :       Union[str, Any]						=						hf_hub_download(repo_id="""nielsr/deta-checkpoints""" ,  filename=lowerCAmelCase						)
        UpperCAmelCase__ :       List[Any]						=						torch.load(lowerCAmelCase ,  map_location="""cpu"""						)["""model"""]
  
        # rename keys
        UpperCAmelCase__ :       List[Any]						=						create_rename_keys(lowerCAmelCase						)
        for src, dest in rename_keys:
              rename_key(lowerCAmelCase ,  lowerCAmelCase ,  lowerCAmelCase						)
        read_in_decoder_q_k_v(lowerCAmelCase ,  lowerCAmelCase						)
  
        # 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__ :       str						=						state_dict.pop(lowerCAmelCase						)
                    UpperCAmelCase__ :       Union[str, Any]						=						val
              if "input_proj" in key:
                    UpperCAmelCase__ :       Optional[int]						=						state_dict.pop(lowerCAmelCase						)
                    UpperCAmelCase__ :       List[Any]						=						val
              if "level_embed" in key or "pos_trans" in key or "pix_trans" in key or "enc_output" in key:
                    UpperCAmelCase__ :       Optional[Any]						=						state_dict.pop(lowerCAmelCase						)
                    UpperCAmelCase__ :       Dict						=						val
  
      # finally, create HuggingFace model and load state dict
        UpperCAmelCase__ :       List[str]						=						DetaForObjectDetection(lowerCAmelCase						)
        model.load_state_dict(lowerCAmelCase						)
        model.eval()
  
        UpperCAmelCase__ :       List[Any]						=						"""cuda""" if torch.cuda.is_available() else """cpu"""
        model.to(lowerCAmelCase						)
  
        # load image processor
        UpperCAmelCase__ :       str						=						DetaImageProcessor(format="""coco_detection"""						)
  
        # verify our conversion on image
        UpperCAmelCase__ :       str						=						prepare_img()
        UpperCAmelCase__ :       Any						=						processor(images=lowerCAmelCase ,  return_tensors="""pt"""						)
        UpperCAmelCase__ :       str						=						encoding["""pixel_values"""]
        UpperCAmelCase__ :       int						=						model(pixel_values.to(lowerCAmelCase						)						)
  
        # verify logits
        if model_name == "deta-resnet-50":
              UpperCAmelCase__ :       int						=						torch.tensor(
                  [[-7.3978, -2.5406, -4.1668], [-8.2684, -3.9933, -3.8096], [-7.0515, -3.7973, -5.8516]]						)
              UpperCAmelCase__ :       List[Any]						=						torch.tensor([[0.5043, 0.4973, 0.9998], [0.2542, 0.5489, 0.4748], [0.5490, 0.2765, 0.0570]]						)
        elif model_name == "deta-resnet-50-24-epochs":
              UpperCAmelCase__ :       str						=						torch.tensor(
                  [[-7.1688, -2.4857, -4.8669], [-7.8630, -3.8154, -4.2674], [-7.2730, -4.1865, -5.5323]]						)
              UpperCAmelCase__ :       Optional[int]						=						torch.tensor([[0.5021, 0.4971, 0.9994], [0.2546, 0.5486, 0.4731], [0.1686, 0.1986, 0.2142]]						)
  
        assert torch.allclose(outputs.logits[0, :3, :3] ,  expected_logits.to(lowerCAmelCase						) ,  atol=1E-4						)
        assert torch.allclose(outputs.pred_boxes[0, :3, :3] ,  expected_boxes.to(lowerCAmelCase						) ,  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(lowerCAmelCase						).mkdir(exist_ok=lowerCAmelCase						)
              model.save_pretrained(lowerCAmelCase						)
              processor.save_pretrained(lowerCAmelCase						)
  
        # 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						=     argparse.ArgumentParser()
  
      parser.add_argument(
          """--model_name""",
          type=str,
          default="""deta-resnet-50""",
          choices=["""deta-resnet-50""", """deta-resnet-50-24-epochs"""],
          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						=     parser.parse_args()
      convert_deta_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)
  
  
style_context: 
  
  """simple docstring"""
  import argparse
  import time
  
  import torch
  import transformers
  from measures_util import end_measure, log_measures, start_measure
  from transformers import AutoConfig, AutoModelForCausalLM, AutoModelForSeqaSeqLM, AutoTokenizer
  
  from accelerate.utils import compute_module_sizes
  
  
  _A						=     {
      """gpt-j-6b""": {"""is_causal""": True, """model""": """sgugger/sharded-gpt-j-6B""", """tokenizer""": """EleutherAI/gpt-j-6B"""},
      """gpt-neox""": {"""is_causal""": True, """model""": """EleutherAI/gpt-neox-20b"""},
      """opt""": {"""is_causal""": True, """model""": """facebook/opt-30b"""},
      """T0pp""": {"""is_causal""": False, """model""": """bigscience/T0pp""", """model_revision""": """sharded"""},
  }
  
  _A						=     [
      """Hello, my name is""",
      """Are unicorns real? Unicorns are""",
      """For the first time in several years,""",
      """My name is Julien and I am""",
      """The goal of life is""",
      """Whenever I'm sad, I like to""",
  ]
  
  
  
  
  def 				a__	(	)  ->   Optional[Any]:
        UpperCAmelCase__ :       int						=						argparse.ArgumentParser(description="""Run and time generations on a big model using Accelerate."""						)
        parser.add_argument("""model_name""" ,  type=lowerCAmelCase ,  default=lowerCAmelCase ,  help="""The name of the model to try."""						)
        parser.add_argument(
            """--tokenizer_name""" ,  type=lowerCAmelCase ,  default=lowerCAmelCase ,  help="""The name of the tokenizer (if different from the model."""						)
        parser.add_argument("""--is_causal""" ,  type=lowerCAmelCase ,  default=lowerCAmelCase ,  help="""Whether or not the model is causal."""						)
        parser.add_argument(
            """--model_revision""" ,  type=lowerCAmelCase ,  default=lowerCAmelCase ,  help="""The revision to use for the model checkpoint."""						)
        parser.add_argument("""--torch_dtype""" ,  type=lowerCAmelCase ,  default=lowerCAmelCase ,  help="""The dtype for the model."""						)
        parser.add_argument("""--disk_offload""" ,  action="""store_true"""						)
  
        UpperCAmelCase__ :       Union[str, Any]						=						parser.parse_args()
  
        # Sanitize args
        if args.model_name in DEFAULT_MODELS:
              UpperCAmelCase__ :       Any						=						DEFAULT_MODELS[args.model_name]
              UpperCAmelCase__ :       Tuple						=						defaults["""model"""]
              if args.tokenizer_name is None:
                    UpperCAmelCase__ :       Optional[Any]						=						defaults.get("""tokenizer""" ,  args.model_name						)
              if args.is_causal is None:
                    UpperCAmelCase__ :       List[Any]						=						defaults["""is_causal"""]
              if args.model_revision is None:
                    UpperCAmelCase__ :       Optional[int]						=						defaults.get("""model_revision""" ,  """main"""						)
  
        if args.is_causal is None:
              raise ValueError("""Could not infer the default for `--is_causal`, pass either True or False for it."""						)
        if args.tokenizer_name is None:
              UpperCAmelCase__ :       str						=						args.model_name
        if args.model_revision is None:
              UpperCAmelCase__ :       List[str]						=						"""main"""
  
        return args
  
  
  
  
  def 				a__	(	)  ->   List[Any]:
        transformers.utils.logging.set_verbosity_error()
        UpperCAmelCase__ :       Optional[int]						=						parse_args()
  
        if args.torch_dtype is None:
              UpperCAmelCase__ :       List[str]						=						AutoConfig.from_pretrained(args.model_name						)
              UpperCAmelCase__ :       Optional[Any]						=						getattr(lowerCAmelCase ,  """torch_dtype""" ,  torch.floataa						)
        else:
              UpperCAmelCase__ :       Union[str, Any]						=						getattr(lowerCAmelCase ,  args.torch_dtype						)
        UpperCAmelCase__ :       List[Any]						=						AutoModelForCausalLM if args.is_causal else AutoModelForSeqaSeqLM
        UpperCAmelCase__ :       List[str]						=						{
            """torch_dtype""": torch_dtype,
            """revision""": args.model_revision,
        }
        if args.disk_offload:
              UpperCAmelCase__ :       List[str]						=						"""tmp_offload"""
              UpperCAmelCase__ :       int						=						True
  
        UpperCAmelCase__ :       Any						=						start_measure()
        UpperCAmelCase__ :       Optional[int]						=						model_cls.from_pretrained(args.model_name ,  device_map="""auto""" ,  **lowerCAmelCase						)
        UpperCAmelCase__ :       Any						=						end_measure(lowerCAmelCase						)
        log_measures(lowerCAmelCase ,  """Model loading"""						)
  
        UpperCAmelCase__ :       List[str]						=						compute_module_sizes(lowerCAmelCase						)
        UpperCAmelCase__ :       Dict						=						{v: 0 for v in model.hf_device_map.values()}
        for module, device in model.hf_device_map.items():
              device_size[device] += module_sizes[module]
        UpperCAmelCase__ :       Optional[Any]						=						"""\n""".join([F"""- {device}: {size // 2**20}MiB""" for device, size in device_size.items()]						)
        print(F"""\nTheoretical use:\n{message}"""						)
  
        UpperCAmelCase__ :       Tuple						=						AutoTokenizer.from_pretrained(args.tokenizer_name						)
  
        UpperCAmelCase__ :       List[str]						=						start_measure()
        UpperCAmelCase__ :       List[Any]						=						[]
        UpperCAmelCase__ :       Tuple						=						[]
        UpperCAmelCase__ :       Optional[Any]						=						[]
        for prompt in PROMPTS:
              UpperCAmelCase__ :       Any						=						tokenizer(lowerCAmelCase ,  return_tensors="""pt"""						).to(0						)
              UpperCAmelCase__ :       List[str]						=						inputs["""input_ids"""][0].tolist()
              UpperCAmelCase__ :       Dict						=						time.time()
              UpperCAmelCase__ :       List[str]						=						model.generate(inputs["""input_ids"""]						)
              UpperCAmelCase__ :       str						=						time.time()
              UpperCAmelCase__ :       str						=						outputs[0].tolist()
              UpperCAmelCase__ :       Optional[Any]						=						len(lowerCAmelCase						) if outputs[: len(lowerCAmelCase						)] != tokens else len(lowerCAmelCase						) - len(lowerCAmelCase						)
              UpperCAmelCase__ :       Tuple						=						after_generate - before_generate
  
              UpperCAmelCase__ :       Optional[Any]						=						tokenizer.decode(lowerCAmelCase ,  skip_special_tokens=lowerCAmelCase						)
              texts_outs.append(lowerCAmelCase						)
              generation_times.append(lowerCAmelCase						)
              gen_tokens.append(lowerCAmelCase						)
              print(F"""Prompt: {prompt}\nGeneration {text_out}\nIn {generation_time:.2f}s for {num_gen_tokens} tokens\n"""						)
  
        UpperCAmelCase__ :       int						=						end_measure(lowerCAmelCase						)
        log_measures(lowerCAmelCase ,  """Model generation"""						)
  
        UpperCAmelCase__ :       Any						=						[gen / tok for gen, tok in zip(lowerCAmelCase ,  lowerCAmelCase						)]
        UpperCAmelCase__ :       List[Any]						=						sum(lowerCAmelCase						) / len(lowerCAmelCase						)
        print(F"""Average time of generation per token: {avg_gen:.2f}s"""						)
        print(F"""First generation (avg time per token): {generation_times_per_token[0]:.2f}s"""						)
        UpperCAmelCase__ :       Any						=						sum(generation_times_per_token[1:]						) / (len(lowerCAmelCase						) - 1)
        print(F"""Average time of generation per token (excluding the first): {avg_gen:.2f}s"""						)
  
  
  if __name__ == "__main__":
      main()
  
  