




'''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()
lowercase						:   Union[str, Any]      						=							logging.get_logger(__name__)




def 		a_(			)					->Tuple:






       '''simple docstring'''




       lowercase	:		Dict    =   DetaConfig(
           num_queries=900 ,					encoder_ffn_dim=2048 ,					decoder_ffn_dim=2048 ,					num_feature_levels=5 ,					assign_first_stage=_lowerCAmelCase ,					with_box_refine=_lowerCAmelCase ,					two_stage=_lowerCAmelCase ,					)

       # set labels
       lowercase	:		str    =   91
       lowercase	:		Dict    =   """huggingface/label-files"""
       lowercase	:		Dict    =   """coco-detection-id2label.json"""
       lowercase	:		Tuple    =   json.load(open(cached_download(hf_hub_url(_lowerCAmelCase ,					_lowerCAmelCase ,					repo_type='''dataset'''   )   ) ,					'''r'''   )   )
       lowercase	:		Optional[int]    =   {int(_lowerCAmelCase   ): v for k, v in idalabel.items()}
       lowercase	:		Union[str, Any]    =   idalabel
       lowercase	:		Optional[int]    =   {v: k for k, v in idalabel.items()}

       return config




def 		a_(			lowerCamelCase   )					->str:






       '''simple docstring'''




       lowercase	:		int    =   []

       # 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   )					->Union[str, Any]:






       '''simple docstring'''




       lowercase	:		Optional[Any]    =   dct.pop(_lowerCAmelCase   )
       lowercase	:		Optional[int]    =   val




def 		a_(			lowerCamelCase ,					lowerCamelCase   )					->Optional[Any]:






       '''simple docstring'''




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




def 		a_(			)					->List[str]:






       '''simple docstring'''




       lowercase	:		Union[str, Any]    =   """http://images.cocodataset.org/val2017/000000039769.jpg"""
       lowercase	:		Any    =   Image.open(requests.get(_lowerCAmelCase ,					stream=_lowerCAmelCase   ).raw   )

       return im




@torch.no_grad()
def 		a_(			lowerCamelCase ,					lowerCamelCase ,					lowerCamelCase   )					->Tuple:






       '''simple docstring'''




       lowercase	:		int    =   get_deta_config()

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

       # rename keys
       lowercase	:		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:
                     lowercase	:		Any    =   state_dict.pop(_lowerCAmelCase   )
                     lowercase	:		str    =   val
              if "input_proj" in key:
                     lowercase	:		List[Any]    =   state_dict.pop(_lowerCAmelCase   )
                     lowercase	:		Optional[Any]    =   val
              if "level_embed" in key or "pos_trans" in key or "pix_trans" in key or "enc_output" in key:
                     lowercase	:		Tuple    =   state_dict.pop(_lowerCAmelCase   )
                     lowercase	:		Any    =   val

    # finally, create HuggingFace model and load state dict
       lowercase	:		Dict    =   DetaForObjectDetection(_lowerCAmelCase   )
       model.load_state_dict(_lowerCAmelCase   )
       model.eval()

       lowercase	:		int    =   """cuda""" if torch.cuda.is_available() else """cpu"""
       model.to(_lowerCAmelCase   )

       # load image processor
       lowercase	:		Dict    =   DetaImageProcessor(format='''coco_detection'''   )

       # verify our conversion on image
       lowercase	:		Dict    =   prepare_img()
       lowercase	:		Optional[int]    =   processor(images=_lowerCAmelCase ,					return_tensors='''pt'''   )
       lowercase	:		Tuple    =   encoding["""pixel_values"""]
       lowercase	:		Union[str, Any]    =   model(pixel_values.to(_lowerCAmelCase   )   )

       # verify logits
       if model_name == "deta-resnet-50":
              lowercase	:		Union[str, 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]]   )
              lowercase	:		Tuple    =   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":
              lowercase	:		str    =   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]]   )
              lowercase	:		List[str]    =   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(_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__":
      lowercase						:   Union[str, Any]      						=							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.'''
      )
      lowercase						:   List[str]      						=							parser.parse_args()
      convert_deta_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)





from urllib.error import HTTPError

from bsa import BeautifulSoup
from requests import exceptions, get

__lowerCAmelCase    :  Optional[int]										= 'https://www.wellrx.com/prescriptions/{0}/{1}/?freshSearch=true'

def   a_					(_lowerCAmelCase  :   str			,	_lowerCAmelCase  :   str       )->				list | None:

    try:
        # Has user provided both inputs?
        if not drug_name or not zip_code:
            return None

        snake_case:						List[str]         =   BASE_URL.format(_lowerCAmelCase			,	_lowerCAmelCase       )
        snake_case:						Optional[Any]         =   get(_lowerCAmelCase       )

        # Is the response ok?
        response.raise_for_status()

        # Scrape the data using bs4
        snake_case:						Tuple         =   BeautifulSoup(response.text			,	"""html.parser"""       )

        # This list will store the name and price.
        snake_case:						List[Any]         =   []

        # Fetch all the grids that contains the items.
        snake_case:						Any         =   soup.find_all("""div"""			,	{"""class""": """grid-x pharmCard"""}       )
        if grid_list and len(_lowerCAmelCase       ) > 0:
            for grid in grid_list:
                # Get the pharmacy price.
                snake_case:						Union[str, Any]         =   grid.find("""p"""			,	{"""class""": """list-title"""}       ).text

                # Get price of the drug.
                snake_case:						Optional[Any]         =   grid.find("""span"""			,	{"""p""", """price price-large"""}       ).text

                pharmacy_price_list.append(
                    {
                        """pharmacy_name""": pharmacy_name,
                        """price""": price,
                    }       )

        return pharmacy_price_list

    except (HTTPError, exceptions.RequestException, ValueError):
        return None


if __name__ == "__main__":
    # Enter a drug name and a zip code
    __lowerCAmelCase    :  Union[str, Any]										= input('Enter drug name: ').strip()
    __lowerCAmelCase    :  Tuple										= input('Enter zip code: ').strip()

    __lowerCAmelCase    :  list | None										= fetch_pharmacy_and_price_list(
        drug_name, zip_code
    )

    if pharmacy_price_list:
        print(F"""\nSearch results for {drug_name} at location {zip_code}:""")
        for pharmacy_price in pharmacy_price_list:
            __lowerCAmelCase    :  int										= pharmacy_price['pharmacy_name']
            __lowerCAmelCase    :  List[str]										= pharmacy_price['price']

            print(F"""Pharmacy: {name} Price: {price}""")
    else:
        print(F"""No results found for {drug_name}""")


