


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()
__lowerCamelCase   	=  logging.get_logger(__name__)



def 			_a      (    ):
     a_     :  int				      =     DetaConfig(
         num_queries=9_0_0	,	encoder_ffn_dim=2_0_4_8	,	decoder_ffn_dim=2_0_4_8	,	num_feature_levels=5	,	assign_first_stage=__UpperCamelCase	,	with_box_refine=__UpperCamelCase	,	two_stage=__UpperCamelCase	,	)

     # set labels
     a_     :  Union[str, Any]				      =     9_1
     a_     :  int				      =     """huggingface/label-files"""
     a_     :  List[str]				      =     """coco-detection-id2label.json"""
     a_     :  int				      =     json.load(open(cached_download(hf_hub_url(__UpperCamelCase	,	__UpperCamelCase	,	repo_type="""dataset"""			)			)	,	"""r"""			)			)
     a_     :  Any				      =     {int(__UpperCamelCase			): v for k, v in idalabel.items()}
     a_     :  int				      =     idalabel
     a_     :  str				      =     {v: k for k, v in idalabel.items()}

     return config



def 			_a      (    __UpperCamelCase			):
     a_     :  List[str]				      =     []

     # 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      (    __UpperCamelCase	,	__UpperCamelCase	,	__UpperCamelCase			):
     a_     :  Any				      =     dct.pop(__UpperCamelCase			)
     a_     :  Union[str, Any]				      =     val



def 			_a      (    __UpperCamelCase	,	__UpperCamelCase			):
     # transformer decoder self-attention layers
     a_     :  Union[str, Any]				      =     config.d_model
     for i in range(config.decoder_layers			):
          # read in weights + bias of input projection layer of self-attention
          a_     :  Dict				      =     state_dict.pop(F'''transformer.decoder.layers.{i}.self_attn.in_proj_weight'''			)
          a_     :  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
          a_     :  List[Any]				      =     in_proj_weight[:hidden_size, :]
          a_     :  Optional[int]				      =     in_proj_bias[:hidden_size]
          a_     :  Union[str, Any]				      =     in_proj_weight[
              hidden_size : hidden_size * 2, :
          ]
          a_     :  Dict				      =     in_proj_bias[hidden_size : hidden_size * 2]
          a_     :  List[str]				      =     in_proj_weight[-hidden_size:, :]
          a_     :  Optional[int]				      =     in_proj_bias[-hidden_size:]



def 			_a      (    ):
     a_     :  Optional[int]				      =     """http://images.cocodataset.org/val2017/000000039769.jpg"""
     a_     :  Optional[Any]				      =     Image.open(requests.get(__UpperCamelCase	,	stream=__UpperCamelCase			).raw			)

     return im



@torch.no_grad()
def 			_a      (    __UpperCamelCase	,	__UpperCamelCase	,	__UpperCamelCase			):
     a_     :  Any				      =     get_deta_config()

     # load original state dict
     if model_name == "deta-resnet-50":
          a_     :  Optional[int]				      =     """adet_checkpoint0011.pth"""
     elif model_name == "deta-resnet-50-24-epochs":
          a_     :  List[str]				      =     """adet_2x_checkpoint0023.pth"""
     else:
          raise ValueError(F'''Model name {model_name} not supported'''			)
     a_     :  Optional[int]				      =     hf_hub_download(repo_id="""nielsr/deta-checkpoints"""	,	filename=__UpperCamelCase			)
     a_     :  Union[str, Any]				      =     torch.load(__UpperCamelCase	,	map_location="""cpu"""			)["""model"""]

     # rename keys
     a_     :  List[Any]				      =     create_rename_keys(__UpperCamelCase			)
     for src, dest in rename_keys:
          rename_key(__UpperCamelCase	,	__UpperCamelCase	,	__UpperCamelCase			)
     read_in_decoder_q_k_v(__UpperCamelCase	,	__UpperCamelCase			)

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

    # finally, create HuggingFace model and load state dict
     a_     :  Union[str, Any]				      =     DetaForObjectDetection(__UpperCamelCase			)
     model.load_state_dict(__UpperCamelCase			)
     model.eval()

     a_     :  List[str]				      =     """cuda""" if torch.cuda.is_available() else """cpu"""
     model.to(__UpperCamelCase			)

     # load image processor
     a_     :  List[str]				      =     DetaImageProcessor(format="""coco_detection"""			)

     # verify our conversion on image
     a_     :  Tuple				      =     prepare_img()
     a_     :  Tuple				      =     processor(images=__UpperCamelCase	,	return_tensors="""pt"""			)
     a_     :  str				      =     encoding["""pixel_values"""]
     a_     :  Any				      =     model(pixel_values.to(__UpperCamelCase			)			)

     # verify logits
     if model_name == "deta-resnet-50":
          a_     :  List[Any]				      =     torch.tensor(
              [[-7.39_78, -2.54_06, -4.16_68], [-8.26_84, -3.99_33, -3.80_96], [-7.05_15, -3.79_73, -5.85_16]]			)
          a_     :  str				      =     torch.tensor([[0.50_43, 0.49_73, 0.99_98], [0.25_42, 0.54_89, 0.47_48], [0.54_90, 0.27_65, 0.05_70]]			)
     elif model_name == "deta-resnet-50-24-epochs":
          a_     :  Dict				      =     torch.tensor(
              [[-7.16_88, -2.48_57, -4.86_69], [-7.86_30, -3.81_54, -4.26_74], [-7.27_30, -4.18_65, -5.53_23]]			)
          a_     :  Union[str, Any]				      =     torch.tensor([[0.50_21, 0.49_71, 0.99_94], [0.25_46, 0.54_86, 0.47_31], [0.16_86, 0.19_86, 0.21_42]]			)

     assert torch.allclose(outputs.logits[0, :3, :3]	,	expected_logits.to(__UpperCamelCase			)	,	atol=1E-4			)
     assert torch.allclose(outputs.pred_boxes[0, :3, :3]	,	expected_boxes.to(__UpperCamelCase			)	,	atol=1E-4			)
     print("""Everything ok!"""			)

     if pytorch_dump_folder_path:
          # Save model and processor
          logger.info(F'''Saving PyTorch model and processor to {pytorch_dump_folder_path}...'''			)
          Path(__UpperCamelCase			).mkdir(exist_ok=__UpperCamelCase			)
          model.save_pretrained(__UpperCamelCase			)
          processor.save_pretrained(__UpperCamelCase			)

     # Push to hub
     if push_to_hub:
          print("""Pushing model and processor to hub..."""			)
          model.push_to_hub(F'''jozhang97/{model_name}'''			)
          processor.push_to_hub(F'''jozhang97/{model_name}'''			)


if __name__ == "__main__":
       __lowerCamelCase   	=  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.'''
       )
       __lowerCamelCase   	=  parser.parse_args()
       convert_deta_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)







"""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


snake_case__		:  Optional[Any]     					=					{
    '''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'''},
}

snake_case__		:  Any     					=					[
    '''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   _snake_case    (    ):
      lowerCAmelCase       :						int							= argparse.ArgumentParser(description='''Run and time generations on a big model using Accelerate.'''      )
      parser.add_argument('''model_name'''			, type=_snake_case			, default=_snake_case			, help='''The name of the model to try.'''      )
      parser.add_argument(
          '''--tokenizer_name'''			, type=_snake_case			, default=_snake_case			, help='''The name of the tokenizer (if different from the model.'''      )
      parser.add_argument('''--is_causal'''			, type=_snake_case			, default=_snake_case			, help='''Whether or not the model is causal.'''      )
      parser.add_argument(
          '''--model_revision'''			, type=_snake_case			, default=_snake_case			, help='''The revision to use for the model checkpoint.'''      )
      parser.add_argument('''--torch_dtype'''			, type=_snake_case			, default=_snake_case			, help='''The dtype for the model.'''      )
      parser.add_argument('''--disk_offload'''			, action='''store_true'''      )

      lowerCAmelCase       :						Dict							= parser.parse_args()

      # Sanitize args
      if args.model_name in DEFAULT_MODELS:
            lowerCAmelCase       :						Optional[int]							= DEFAULT_MODELS[args.model_name]
            lowerCAmelCase       :						Union[str, Any]							= defaults['''model''']
            if args.tokenizer_name is None:
                  lowerCAmelCase       :						Union[str, Any]							= defaults.get('''tokenizer'''			, args.model_name      )
            if args.is_causal is None:
                  lowerCAmelCase       :						List[Any]							= defaults['''is_causal''']
            if args.model_revision is None:
                  lowerCAmelCase       :						Tuple							= 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:
            lowerCAmelCase       :						Union[str, Any]							= args.model_name
      if args.model_revision is None:
            lowerCAmelCase       :						List[Any]							= '''main'''

      return args


def   _snake_case    (    ):
      transformers.utils.logging.set_verbosity_error()
      lowerCAmelCase       :						Union[str, Any]							= parse_args()

      if args.torch_dtype is None:
            lowerCAmelCase       :						List[str]							= AutoConfig.from_pretrained(args.model_name      )
            lowerCAmelCase       :						Optional[Any]							= getattr(_snake_case			, '''torch_dtype'''			, torch.floataa      )
      else:
            lowerCAmelCase       :						Optional[int]							= getattr(_snake_case			, args.torch_dtype      )
      lowerCAmelCase       :						Tuple							= AutoModelForCausalLM if args.is_causal else AutoModelForSeqaSeqLM
      lowerCAmelCase       :						str							= {
          '''torch_dtype''': torch_dtype,
          '''revision''': args.model_revision,
      }
      if args.disk_offload:
            lowerCAmelCase       :						Optional[int]							= '''tmp_offload'''
            lowerCAmelCase       :						Optional[int]							= True

      lowerCAmelCase       :						str							= start_measure()
      lowerCAmelCase       :						List[str]							= model_cls.from_pretrained(args.model_name			, device_map='''auto'''			, **_snake_case      )
      lowerCAmelCase       :						Tuple							= end_measure(_snake_case      )
      log_measures(_snake_case			, '''Model loading'''      )

      lowerCAmelCase       :						str							= compute_module_sizes(_snake_case      )
      lowerCAmelCase       :						Tuple							= {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]
      lowerCAmelCase       :						int							= '''\n'''.join([f'''- {device}: {size // 2**20}MiB''' for device, size in device_size.items()]      )
      print(f'''\nTheoretical use:\n{message}'''      )

      lowerCAmelCase       :						int							= AutoTokenizer.from_pretrained(args.tokenizer_name      )

      lowerCAmelCase       :						List[str]							= start_measure()
      lowerCAmelCase       :						Union[str, Any]							= []
      lowerCAmelCase       :						int							= []
      lowerCAmelCase       :						Dict							= []
      for prompt in PROMPTS:
            lowerCAmelCase       :						str							= tokenizer(_snake_case			, return_tensors='''pt'''      ).to(0      )
            lowerCAmelCase       :						int							= inputs['''input_ids'''][0].tolist()
            lowerCAmelCase       :						Optional[Any]							= time.time()
            lowerCAmelCase       :						Optional[int]							= model.generate(inputs['''input_ids''']      )
            lowerCAmelCase       :						Any							= time.time()
            lowerCAmelCase       :						str							= outputs[0].tolist()
            lowerCAmelCase       :						int							= len(_snake_case      ) if outputs[: len(_snake_case      )] != tokens else len(_snake_case      ) - len(_snake_case      )
            lowerCAmelCase       :						Tuple							= after_generate - before_generate

            lowerCAmelCase       :						Tuple							= tokenizer.decode(_snake_case			, skip_special_tokens=_snake_case      )
            texts_outs.append(_snake_case      )
            generation_times.append(_snake_case      )
            gen_tokens.append(_snake_case      )
            print(f'''Prompt: {prompt}\nGeneration {text_out}\nIn {generation_time:.2f}s for {num_gen_tokens} tokens\n'''      )

      lowerCAmelCase       :						List[Any]							= end_measure(_snake_case      )
      log_measures(_snake_case			, '''Model generation'''      )

      lowerCAmelCase       :						str							= [gen / tok for gen, tok in zip(_snake_case			, _snake_case      )]
      lowerCAmelCase       :						Any							= sum(_snake_case      ) / len(_snake_case      )
      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'''      )
      lowerCAmelCase       :						List[Any]							= sum(generation_times_per_token[1:]      ) / (len(_snake_case      ) - 1)
      print(f'''Average time of generation per token (excluding the first): {avg_gen:.2f}s'''      )


if __name__ == "__main__":
 main()


