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()
_SCREAMING_SNAKE_CASE   						=    logging.get_logger(__name__)
def 				__lowercase	(						lowerCamelCase__					)				->str:

  '''simple docstring'''




  a__     		=     tf.image.resize(lowerCamelCase__ ,				(3_46, 3_46)					)
  a__     		=     tf.image.crop_to_bounding_box(lowerCamelCase__ ,				(3_46 - 2_89) // 2 ,				(3_46 - 2_89) // 2 ,				2_89 ,				2_89					)
  return image
def 				__lowercase	(						)				->Union[str, Any]:

  '''simple docstring'''




  a__     		=     EfficientNetConfig.from_pretrained("google/efficientnet-b7"					)
  a__     		=     2_89
  a__     		=     6_40
  a__     		=     {"0": "LABEL_0", "1": "LABEL_1"}
  a__     		=     {"LABEL_0": 0, "LABEL_1": 1}
  a__     		=     []

  a__     		=     BertConfig()
  a__     		=     AlignConfig.from_text_vision_configs(
      text_config=lowerCamelCase__ ,				vision_config=lowerCamelCase__ ,				projection_dim=6_40					)
  return config
def 				__lowercase	(						)				->Optional[Any]:

  '''simple docstring'''




  a__     		=     "http://images.cocodataset.org/val2017/000000039769.jpg"
  a__     		=     Image.open(requests.get(lowerCamelCase__ ,				stream=lowerCamelCase__					).raw					)
  return im
def 				__lowercase	(						)				->List[Any]:

  '''simple docstring'''




  a__     		=     EfficientNetImageProcessor(
      do_center_crop=lowerCamelCase__ ,				rescale_factor=1 / 127.5 ,				rescale_offset=lowerCamelCase__ ,				do_normalize=lowerCamelCase__ ,				include_top=lowerCamelCase__ ,				resample=Image.BILINEAR ,				)
  a__     		=     BertTokenizer.from_pretrained("bert-base-uncased"					)
  a__     		=     64
  a__     		=     AlignProcessor(image_processor=lowerCamelCase__ ,				tokenizer=lowerCamelCase__					)
  return processor
def 				__lowercase	(						lowerCamelCase__					)				->Union[str, Any]:

  '''simple docstring'''




  a__     		=     [v.split("_"					)[0].split("block"					)[1] for v in original_param_names if v.startswith("block"					)]
  a__     		=     list(set(lowerCamelCase__					)					)
  a__     		=     sorted(lowerCamelCase__					)
  a__     		=     len(lowerCamelCase__					)
  a__     		=     {b: str(lowerCamelCase__					) for b, i in zip(lowerCamelCase__ ,				range(lowerCamelCase__					)					)}

  a__     		=     []
  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:
    a__     		=     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")					)

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

    # BERT text encoder
  a__     		=     []
  a__     		=     "tf_bert_model/bert"
  a__     		=     "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:
      a__     		=     item[1]
  return key_mapping
def 				__lowercase	(						lowerCamelCase__ ,				lowerCamelCase__ ,				lowerCamelCase__					)				->Optional[Any]:

  '''simple docstring'''




  list(hf_params.keys()					)

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

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

    # Replace HF parameters with original TF model parameters
    hf_params[hf_key].copy_(lowerCamelCase__					)
@torch.no_grad()
def 				__lowercase	(						lowerCamelCase__ ,				lowerCamelCase__ ,				lowerCamelCase__ ,				lowerCamelCase__					)				->Optional[int]:

  '''simple docstring'''




  a__     		=     64
  a__     		=     Tokenizer(lowerCamelCase__					)
  a__     		=     align.Align("efficientnet-b7" ,				"bert-base" ,				6_40 ,				lowerCamelCase__ ,				tok.get_vocab_size()					)
  original_model.compile()
  original_model.load_weights(lowerCamelCase__					)

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

  # Load HuggingFace model
  a__     		=     get_align_config()
  a__     		=     AlignModel(lowerCamelCase__					).eval()
  a__     		=     hf_model.state_dict()

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

  # Initialize processor
  a__     		=     get_processor()
  a__     		=     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():
    a__     		=     hf_model(**lowerCamelCase__					)

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

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

  a__     		=     original_model.image_encoder(lowerCamelCase__ ,				training=lowerCamelCase__					)
  a__     		=     original_model.text_encoder(lowerCamelCase__ ,				training=lowerCamelCase__					)

  a__     		=     tf.nn.la_normalize(lowerCamelCase__ ,				axis=-1					)
  a__     		=     tf.nn.la_normalize(lowerCamelCase__ ,				axis=-1					)

  # Check whether original and HF model outputs match  -> np.allclose
  if not np.allclose(lowerCamelCase__ ,				lowerCamelCase__ ,				atol=1E-3					):
    raise ValueError("The predicted image features are not the same."					)
  if not np.allclose(lowerCamelCase__ ,				lowerCamelCase__ ,				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(lowerCamelCase__					):
      os.mkdir(lowerCamelCase__					)
    # Save converted model and image processor
    hf_model.save_pretrained(lowerCamelCase__					)
    processor.save_pretrained(lowerCamelCase__					)

  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__":
     _SCREAMING_SNAKE_CASE   						=    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""")

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


import random







class 					UpperCAmelCase_				:




    def __init__(					self  :   Optional[int]      ,      lowerCAmelCase  :   list[list[float]]      ,      lowerCAmelCase  :   list[int]      ,      lowerCAmelCase  :   float = 0.01      ,      lowerCAmelCase  :   int = 1000      ,      lowerCAmelCase  :   float = -1      ,      )	-> None:
      a__     		=     sample
      if len(self.sample   ) == 0:
        raise ValueError("Sample data can not be empty"   )
      a__     		=     target
      if len(self.target   ) == 0:
        raise ValueError("Target data can not be empty"   )
      if len(self.sample   ) != len(self.target   ):
        raise ValueError("Sample data and Target data do not have matching lengths"   )
      a__     		=     learning_rate
      a__     		=     epoch_number
      a__     		=     bias
      a__     		=     len(lowerCAmelCase   )
      a__     		=     len(sample[0]   )  # number of columns in dataset
      a__     		=     []




    def 						__lowerCamelCase (					self  :   Optional[Any]   )	-> None:
      for sample in self.sample:
        sample.insert(0      ,      self.bias   )

      for _ in range(self.col_sample   ):
        self.weight.append(random.random()   )

      self.weight.insert(0      ,      self.bias   )

      a__     		=     0

      while True:
        a__     		=     False
        for i in range(self.number_sample   ):
          a__     		=     0
          for j in range(self.col_sample + 1   ):
            a__     		=     u + self.weight[j] * self.sample[i][j]
          a__     		=     self.sign(lowerCAmelCase   )
          if y != self.target[i]:
            for j in range(self.col_sample + 1   ):
              a__     		=     (
                  self.weight[j]
                  + self.learning_rate
                  * (self.target[i] - y)
                  * self.sample[i][j]
              )
            a__     		=     True
            # print('Epoch: \n',epoch_count)
        a__     		=     epoch_count + 1
        # if you want control the epoch or just by error
        if not has_misclassified:
          print(("\nEpoch:\n", epoch_count)   )
          print("------------------------\n"   )
          # if epoch_count > self.epoch_number or not error:
          break




    def 						__lowerCamelCase (					self  :   List[str]      ,      lowerCAmelCase  :   list[float]   )	-> None:
      if len(self.sample   ) == 0:
        raise ValueError("Sample data can not be empty"   )
      sample.insert(0      ,      self.bias   )
      a__     		=     0
      for i in range(self.col_sample + 1   ):
        a__     		=     u + self.weight[i] * sample[i]

      a__     		=     self.sign(lowerCAmelCase   )

      if y == -1:
        print(("Sample: ", sample)   )
        print("classification: P1"   )
      else:
        print(("Sample: ", sample)   )
        print("classification: P2"   )




    def 						__lowerCamelCase (					self  :   int      ,      lowerCAmelCase  :   float   )	-> int:
      return 1 if u >= 0 else -1

_SCREAMING_SNAKE_CASE   						=    [
    [-0.6508, 0.1097, 4.0009],
    [-1.4492, 0.8896, 4.4005],
    [2.0850, 0.6876, 12.0710],
    [0.2626, 1.1476, 7.7985],
    [0.6418, 1.0234, 7.0427],
    [0.2569, 0.6730, 8.3265],
    [1.1155, 0.6043, 7.4446],
    [0.0914, 0.3399, 7.0677],
    [0.0121, 0.5256, 4.6316],
    [-0.0429, 0.4660, 5.4323],
    [0.4340, 0.6870, 8.2287],
    [0.2735, 1.0287, 7.1934],
    [0.4839, 0.4851, 7.4850],
    [0.4089, -0.1267, 5.5019],
    [1.4391, 0.1614, 8.5843],
    [-0.9115, -0.1973, 2.1962],
    [0.3654, 1.0475, 7.4858],
    [0.2144, 0.7515, 7.1699],
    [0.2013, 1.0014, 6.5489],
    [0.6483, 0.2183, 5.8991],
    [-0.1147, 0.2242, 7.2435],
    [-0.7970, 0.8795, 3.8762],
    [-1.0625, 0.6366, 2.4707],
    [0.5307, 0.1285, 5.6883],
    [-1.2200, 0.7777, 1.7252],
    [0.3957, 0.1076, 5.6623],
    [-0.1013, 0.5989, 7.1812],
    [2.4482, 0.9455, 11.2095],
    [2.0149, 0.6192, 10.9263],
    [0.2012, 0.2611, 5.4631],
]

_SCREAMING_SNAKE_CASE   						=    [
    -1,
    -1,
    -1,
    1,
    1,
    -1,
    1,
    -1,
    1,
    1,
    -1,
    1,
    -1,
    -1,
    -1,
    -1,
    1,
    1,
    1,
    1,
    -1,
    1,
    1,
    1,
    1,
    -1,
    -1,
    1,
    -1,
    1,
]


if __name__ == "__main__":
     import doctest

     doctest.testmod()

     _SCREAMING_SNAKE_CASE   						=    Perceptron(
         sample=samples, target=target, learning_rate=0.01, epoch_number=1000, bias=-1
     )
     network.training()
     print("""Finished training perceptron""")
     print("""Enter values to predict or q to exit""")
     while True:
          _SCREAMING_SNAKE_CASE   						=    []
          for i in range(len(samples[0])):
               _SCREAMING_SNAKE_CASE   						=    input("""value: """).strip()
               if user_input == "q":
                    break
               _SCREAMING_SNAKE_CASE   						=    float(user_input)
               sample.insert(i, observation)
          network.sort(sample)

