


"""simple docstring"""






import argparse
import os

import align
import numpy as np
import requests
import tensorflow as tf
import torch
from PIL import Image
from tokenizer import Tokenizer

from transformers import (
    AlignConfig,
    AlignModel,
    AlignProcessor,
    BertConfig,
    BertTokenizer,
    EfficientNetConfig,
    EfficientNetImageProcessor,
)
from transformers.utils import logging


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




def 						_UpperCamelCase			(  A			):
   UpperCamelCase_     =tf.image.resize(A    ,       (346, 346)			)
   UpperCamelCase_     =tf.image.crop_to_bounding_box(A    ,       (346 - 289) // 2    ,       (346 - 289) // 2    ,       289    ,       289			)
   return image




def 						_UpperCamelCase			(  ):
   UpperCamelCase_     =EfficientNetConfig.from_pretrained("google/efficientnet-b7"			)
   UpperCamelCase_     =289
   UpperCamelCase_     =640
   UpperCamelCase_     ={"0": "LABEL_0", "1": "LABEL_1"}
   UpperCamelCase_     ={"LABEL_0": 0, "LABEL_1": 1}
   UpperCamelCase_     =[]

   UpperCamelCase_     =BertConfig()
   UpperCamelCase_     =AlignConfig.from_text_vision_configs(
       text_config=A    ,       vision_config=A    ,       projection_dim=640			)
   return config




def 						_UpperCamelCase			(  ):
   UpperCamelCase_     ="http://images.cocodataset.org/val2017/000000039769.jpg"
   UpperCamelCase_     =Image.open(requests.get(A    ,       stream=A			).raw			)
   return im




def 						_UpperCamelCase			(  ):
   UpperCamelCase_     =EfficientNetImageProcessor(
       do_center_crop=A    ,       rescale_factor=1 / 127.5    ,       rescale_offset=A    ,       do_normalize=A    ,       include_top=A    ,       resample=Image.BILINEAR    ,       )
   UpperCamelCase_     =BertTokenizer.from_pretrained("bert-base-uncased"			)
   UpperCamelCase_     =64
   UpperCamelCase_     =AlignProcessor(image_processor=A    ,       tokenizer=A			)
   return processor




def 						_UpperCamelCase			(  A			):
   # EfficientNet image encoder
   UpperCamelCase_     =[v.split("_"			)[0].split("block"			)[1] for v in original_param_names if v.startswith("block"			)]
   UpperCamelCase_     =list(set(A			)			)
   UpperCamelCase_     =sorted(A			)
   UpperCamelCase_     =len(A			)
   UpperCamelCase_     ={b: str(A			) for b, i in zip(A    ,       range(A			)			)}

   UpperCamelCase_     =[]
   rename_keys.append(("stem_conv/kernel:0", "embeddings.convolution.weight")			)
   rename_keys.append(("stem_bn/gamma:0", "embeddings.batchnorm.weight")			)
   rename_keys.append(("stem_bn/beta:0", "embeddings.batchnorm.bias")			)
   rename_keys.append(("stem_bn/moving_mean:0", "embeddings.batchnorm.running_mean")			)
   rename_keys.append(("stem_bn/moving_variance:0", "embeddings.batchnorm.running_var")			)

   for b in block_names:
      UpperCamelCase_     =block_name_mapping[b]
      rename_keys.append((f"""block{b}_expand_conv/kernel:0""", f"""encoder.blocks.{hf_b}.expansion.expand_conv.weight""")			)
      rename_keys.append((f"""block{b}_expand_bn/gamma:0""", f"""encoder.blocks.{hf_b}.expansion.expand_bn.weight""")			)
      rename_keys.append((f"""block{b}_expand_bn/beta:0""", f"""encoder.blocks.{hf_b}.expansion.expand_bn.bias""")			)
      rename_keys.append(
          (f"""block{b}_expand_bn/moving_mean:0""", f"""encoder.blocks.{hf_b}.expansion.expand_bn.running_mean""")			)
      rename_keys.append(
          (f"""block{b}_expand_bn/moving_variance:0""", f"""encoder.blocks.{hf_b}.expansion.expand_bn.running_var""")			)
      rename_keys.append(
          (f"""block{b}_dwconv/depthwise_kernel:0""", f"""encoder.blocks.{hf_b}.depthwise_conv.depthwise_conv.weight""")			)
      rename_keys.append((f"""block{b}_bn/gamma:0""", f"""encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.weight""")			)
      rename_keys.append((f"""block{b}_bn/beta:0""", f"""encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.bias""")			)
      rename_keys.append(
          (f"""block{b}_bn/moving_mean:0""", f"""encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.running_mean""")			)
      rename_keys.append(
          (f"""block{b}_bn/moving_variance:0""", f"""encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.running_var""")			)

      rename_keys.append((f"""block{b}_se_reduce/kernel:0""", f"""encoder.blocks.{hf_b}.squeeze_excite.reduce.weight""")			)
      rename_keys.append((f"""block{b}_se_reduce/bias:0""", f"""encoder.blocks.{hf_b}.squeeze_excite.reduce.bias""")			)
      rename_keys.append((f"""block{b}_se_expand/kernel:0""", f"""encoder.blocks.{hf_b}.squeeze_excite.expand.weight""")			)
      rename_keys.append((f"""block{b}_se_expand/bias:0""", f"""encoder.blocks.{hf_b}.squeeze_excite.expand.bias""")			)
      rename_keys.append(
          (f"""block{b}_project_conv/kernel:0""", f"""encoder.blocks.{hf_b}.projection.project_conv.weight""")			)
      rename_keys.append((f"""block{b}_project_bn/gamma:0""", f"""encoder.blocks.{hf_b}.projection.project_bn.weight""")			)
      rename_keys.append((f"""block{b}_project_bn/beta:0""", f"""encoder.blocks.{hf_b}.projection.project_bn.bias""")			)
      rename_keys.append(
          (f"""block{b}_project_bn/moving_mean:0""", f"""encoder.blocks.{hf_b}.projection.project_bn.running_mean""")			)
      rename_keys.append(
          (f"""block{b}_project_bn/moving_variance:0""", f"""encoder.blocks.{hf_b}.projection.project_bn.running_var""")			)

   UpperCamelCase_     ={}
   for item in rename_keys:
      if item[0] in original_param_names:
         UpperCamelCase_     ="vision_model." + item[1]

    # BERT text encoder
   UpperCamelCase_     =[]
   UpperCamelCase_     ="tf_bert_model/bert"
   UpperCamelCase_     ="text_model"
   for i in range(12			):
      rename_keys.append(
          (
              f"""{old}/encoder/layer_._{i}/attention/self/query/kernel:0""",
              f"""{new}.encoder.layer.{i}.attention.self.query.weight""",
          )			)
      rename_keys.append(
          (
              f"""{old}/encoder/layer_._{i}/attention/self/query/bias:0""",
              f"""{new}.encoder.layer.{i}.attention.self.query.bias""",
          )			)
      rename_keys.append(
          (
              f"""{old}/encoder/layer_._{i}/attention/self/key/kernel:0""",
              f"""{new}.encoder.layer.{i}.attention.self.key.weight""",
          )			)
      rename_keys.append(
          (
              f"""{old}/encoder/layer_._{i}/attention/self/key/bias:0""",
              f"""{new}.encoder.layer.{i}.attention.self.key.bias""",
          )			)
      rename_keys.append(
          (
              f"""{old}/encoder/layer_._{i}/attention/self/value/kernel:0""",
              f"""{new}.encoder.layer.{i}.attention.self.value.weight""",
          )			)
      rename_keys.append(
          (
              f"""{old}/encoder/layer_._{i}/attention/self/value/bias:0""",
              f"""{new}.encoder.layer.{i}.attention.self.value.bias""",
          )			)
      rename_keys.append(
          (
              f"""{old}/encoder/layer_._{i}/attention/output/dense/kernel:0""",
              f"""{new}.encoder.layer.{i}.attention.output.dense.weight""",
          )			)
      rename_keys.append(
          (
              f"""{old}/encoder/layer_._{i}/attention/output/dense/bias:0""",
              f"""{new}.encoder.layer.{i}.attention.output.dense.bias""",
          )			)
      rename_keys.append(
          (
              f"""{old}/encoder/layer_._{i}/attention/output/LayerNorm/gamma:0""",
              f"""{new}.encoder.layer.{i}.attention.output.LayerNorm.weight""",
          )			)
      rename_keys.append(
          (
              f"""{old}/encoder/layer_._{i}/attention/output/LayerNorm/beta:0""",
              f"""{new}.encoder.layer.{i}.attention.output.LayerNorm.bias""",
          )			)
      rename_keys.append(
          (
              f"""{old}/encoder/layer_._{i}/intermediate/dense/kernel:0""",
              f"""{new}.encoder.layer.{i}.intermediate.dense.weight""",
          )			)
      rename_keys.append(
          (
              f"""{old}/encoder/layer_._{i}/intermediate/dense/bias:0""",
              f"""{new}.encoder.layer.{i}.intermediate.dense.bias""",
          )			)
      rename_keys.append(
          (f"""{old}/encoder/layer_._{i}/output/dense/kernel:0""", f"""{new}.encoder.layer.{i}.output.dense.weight""")			)
      rename_keys.append(
          (f"""{old}/encoder/layer_._{i}/output/dense/bias:0""", f"""{new}.encoder.layer.{i}.output.dense.bias""")			)
      rename_keys.append(
          (f"""{old}/encoder/layer_._{i}/output/LayerNorm/gamma:0""", f"""{new}.encoder.layer.{i}.output.LayerNorm.weight""")			)
      rename_keys.append(
          (f"""{old}/encoder/layer_._{i}/output/LayerNorm/beta:0""", f"""{new}.encoder.layer.{i}.output.LayerNorm.bias""")			)

   rename_keys.append((f"""{old}/embeddings/word_embeddings/weight:0""", f"""{new}.embeddings.word_embeddings.weight""")			)
   rename_keys.append(
       (f"""{old}/embeddings/position_embeddings/embeddings:0""", f"""{new}.embeddings.position_embeddings.weight""")			)
   rename_keys.append(
       (f"""{old}/embeddings/token_type_embeddings/embeddings:0""", f"""{new}.embeddings.token_type_embeddings.weight""")			)
   rename_keys.append((f"""{old}/embeddings/LayerNorm/gamma:0""", f"""{new}.embeddings.LayerNorm.weight""")			)
   rename_keys.append((f"""{old}/embeddings/LayerNorm/beta:0""", f"""{new}.embeddings.LayerNorm.bias""")			)

   rename_keys.append((f"""{old}/pooler/dense/kernel:0""", f"""{new}.pooler.dense.weight""")			)
   rename_keys.append((f"""{old}/pooler/dense/bias:0""", f"""{new}.pooler.dense.bias""")			)
   rename_keys.append(("dense/kernel:0", "text_projection.weight")			)
   rename_keys.append(("dense/bias:0", "text_projection.bias")			)
   rename_keys.append(("dense/bias:0", "text_projection.bias")			)
   rename_keys.append(("temperature:0", "temperature")			)

   for item in rename_keys:
      if item[0] in original_param_names:
         UpperCamelCase_     =item[1]
   return key_mapping




def 						_UpperCamelCase			(  A    ,       A    ,       A			):
   list(hf_params.keys()			)

   for key, value in tf_params.items():
      if key not in key_mapping:
         continue

      UpperCamelCase_     =key_mapping[key]
      if "_conv" in key and "kernel" in key:
         UpperCamelCase_     =torch.from_numpy(A			).permute(3    ,       2    ,       0    ,       1			)
      elif "embeddings" in key:
         UpperCamelCase_     =torch.from_numpy(A			)
      elif "depthwise_kernel" in key:
         UpperCamelCase_     =torch.from_numpy(A			).permute(2    ,       3    ,       0    ,       1			)
      elif "kernel" in key:
         UpperCamelCase_     =torch.from_numpy(np.transpose(A			)			)
      elif "temperature" in key:
         UpperCamelCase_     =value
      elif "bn/gamma" or "bn/beta" in key:
         UpperCamelCase_     =torch.from_numpy(np.transpose(A			)			).squeeze()
      else:
         UpperCamelCase_     =torch.from_numpy(A			)

      # Replace HF parameters with original TF model parameters
      hf_params[hf_key].copy_(A			)




@torch.no_grad()
def 						_UpperCamelCase			(  A    ,       A    ,       A    ,       A			):
   UpperCamelCase_     =64
   UpperCamelCase_     =Tokenizer(A			)
   UpperCamelCase_     =align.Align("efficientnet-b7"    ,       "bert-base"    ,       640    ,       A    ,       tok.get_vocab_size()			)
   original_model.compile()
   original_model.load_weights(A			)

   UpperCamelCase_     =original_model.trainable_variables
   UpperCamelCase_     =original_model.non_trainable_variables
   UpperCamelCase_     ={param.name: param.numpy() for param in tf_params}
   for param in tf_non_train_params:
      UpperCamelCase_     =param.numpy()
   UpperCamelCase_     =list(tf_params.keys()			)

   # Load HuggingFace model
   UpperCamelCase_     =get_align_config()
   UpperCamelCase_     =AlignModel(A			).eval()
   UpperCamelCase_     =hf_model.state_dict()

   # Create src-to-dst parameter name mapping dictionary
   print("Converting parameters..."			)
   UpperCamelCase_     =rename_keys(A			)
   replace_params(A    ,       A    ,       A			)

   # Initialize processor
   UpperCamelCase_     =get_processor()
   UpperCamelCase_     =processor(
       images=prepare_img()    ,       text="A picture of a cat"    ,       padding="max_length"    ,       max_length=64    ,       return_tensors="pt"			)

   # HF model inference
   hf_model.eval()
   with torch.no_grad():
      UpperCamelCase_     =hf_model(**A			)

   UpperCamelCase_     =outputs.image_embeds.detach().numpy()
   UpperCamelCase_     =outputs.text_embeds.detach().numpy()

   # Original model inference
   UpperCamelCase_     =False
   UpperCamelCase_     =EfficientNetImageProcessor(
       do_center_crop=A    ,       do_rescale=A    ,       do_normalize=A    ,       include_top=A    ,       resample=Image.BILINEAR    ,       )
   UpperCamelCase_     =tf_image_processor(images=prepare_img()    ,       return_tensors="tf"    ,       data_format="channels_last"			)["pixel_values"]
   UpperCamelCase_     =tok(tf.constant(["A picture of a cat"]			)			)

   UpperCamelCase_     =original_model.image_encoder(A    ,       training=A			)
   UpperCamelCase_     =original_model.text_encoder(A    ,       training=A			)

   UpperCamelCase_     =tf.nn.la_normalize(A    ,       axis=-1			)
   UpperCamelCase_     =tf.nn.la_normalize(A    ,       axis=-1			)

   # Check whether original and HF model outputs match  -> np.allclose
   if not np.allclose(A    ,       A    ,       atol=1e-3			):
      raise ValueError("The predicted image features are not the same."			)
   if not np.allclose(A    ,       A    ,       atol=1e-3			):
      raise ValueError("The predicted text features are not the same."			)
   print("Model outputs match!"			)

   if save_model:
      # Create folder to save model
      if not os.path.isdir(A			):
         os.mkdir(A			)
      # Save converted model and image processor
      hf_model.save_pretrained(A			)
      processor.save_pretrained(A			)

   if push_to_hub:
      # Push model and image processor to hub
      print("Pushing converted ALIGN to the hub..."			)
      processor.push_to_hub("align-base"			)
      hf_model.push_to_hub("align-base"			)


if __name__ == "__main__":
    A_			  =    argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        "--checkpoint_path",
        default="./weights/model-weights",
        type=str,
        help="Path to the pretrained TF ALIGN checkpoint.",
    )
    parser.add_argument(
        "--pytorch_dump_folder_path",
        default="hf_model",
        type=str,
        help="Path to the output PyTorch model directory.",
    )
    parser.add_argument("--save_model", action="store_true", help="Save model to local")
    parser.add_argument("--push_to_hub", action="store_true", help="Push model and image processor to the hub")

    A_			  =    parser.parse_args()
    convert_align_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.save_model, args.push_to_hub)








"""simple docstring"""






from __future__ import annotations

from pathlib import Path




def 						_UpperCamelCase			(  A    ,       A			):
   return pointa[0] * pointa[1] - pointa[1] * pointa[0]




def 						_UpperCamelCase			(  A    ,       A    ,       A    ,       A    ,       A    ,       A			):
   UpperCamelCase_     =(xa, ya)
   UpperCamelCase_     =(xa - xa, ya - ya)
   UpperCamelCase_     =(xa - xa, ya - ya)
   UpperCamelCase_     =-vector_product(A    ,       A			) / vector_product(
       A    ,       A			)
   UpperCamelCase_     =+vector_product(A    ,       A			) / vector_product(
       A    ,       A			)

   return a > 0 and b > 0 and a + b < 1




def 						_UpperCamelCase			(  A = "p102_triangles.txt"			):
   UpperCamelCase_     =Path(A			).parent.joinpath(A			).read_text(encoding="utf-8"			)

   UpperCamelCase_     =[]
   for line in data.strip().split("\n"			):
      triangles.append([int(A			) for number in line.split(","			)]			)

   UpperCamelCase_     =0
   UpperCamelCase_     =42

   for triangle in triangles:
      ret += contains_origin(*A			)

   return ret


if __name__ == "__main__":
    print(f'''{solution() = }''')
