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()
snake_case__						      =			logging.get_logger(__name__)
def    __magic_name__(	__UpperCAmelCase		)    ->     str:



      '''simple docstring'''

      _lowerCamelCase           =					tf.image.resize(__UpperCAmelCase			,    (346, 346)		)
      _lowerCamelCase           =					tf.image.crop_to_bounding_box(__UpperCAmelCase			,    (346 - 289) // 2			,    (346 - 289) // 2			,    289			,    289		)
      return image
def    __magic_name__(	)    ->     int:



      '''simple docstring'''

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

      _lowerCamelCase           =					BertConfig()
      _lowerCamelCase           =					AlignConfig.from_text_vision_configs(
          text_config=__UpperCAmelCase			,    vision_config=__UpperCAmelCase			,    projection_dim=640		)
      return config
def    __magic_name__(	)    ->     Dict:



      '''simple docstring'''

      _lowerCamelCase           =					"http://images.cocodataset.org/val2017/000000039769.jpg"
      _lowerCamelCase           =					Image.open(requests.get(__UpperCAmelCase			,    stream=__UpperCAmelCase		).raw		)
      return im
def    __magic_name__(	)    ->     Tuple:



      '''simple docstring'''

      _lowerCamelCase           =					EfficientNetImageProcessor(
          do_center_crop=__UpperCAmelCase			,    rescale_factor=1 / 1_2_7.5			,    rescale_offset=__UpperCAmelCase			,    do_normalize=__UpperCAmelCase			,    include_top=__UpperCAmelCase			,    resample=Image.BILINEAR			,    )
      _lowerCamelCase           =					BertTokenizer.from_pretrained('''bert-base-uncased'''		)
      _lowerCamelCase           =					64
      _lowerCamelCase           =					AlignProcessor(image_processor=__UpperCAmelCase			,    tokenizer=__UpperCAmelCase		)
      return processor
def    __magic_name__(	__UpperCAmelCase		)    ->     Dict:



      '''simple docstring'''

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

      _lowerCamelCase           =					[]
      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:
            _lowerCamelCase           =					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')		)

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

    # BERT text encoder
      _lowerCamelCase           =					[]
      _lowerCamelCase           =					"tf_bert_model/bert"
      _lowerCamelCase           =					"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:
                  _lowerCamelCase           =					item[1]
      return key_mapping
def    __magic_name__(	__UpperCAmelCase			,    __UpperCAmelCase			,    __UpperCAmelCase		)    ->     Any:



      '''simple docstring'''

      list(hf_params.keys()		)

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

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

            # Replace HF parameters with original TF model parameters
            hf_params[hf_key].copy_(__UpperCAmelCase		)
@torch.no_grad()
def    __magic_name__(	__UpperCAmelCase			,    __UpperCAmelCase			,    __UpperCAmelCase			,    __UpperCAmelCase		)    ->     List[str]:



      '''simple docstring'''

      _lowerCamelCase           =					64
      _lowerCamelCase           =					Tokenizer(__UpperCAmelCase		)
      _lowerCamelCase           =					align.Align('''efficientnet-b7'''			,    '''bert-base'''			,    640			,    __UpperCAmelCase			,    tok.get_vocab_size()		)
      original_model.compile()
      original_model.load_weights(__UpperCAmelCase		)

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

      # Load HuggingFace model
      _lowerCamelCase           =					get_align_config()
      _lowerCamelCase           =					AlignModel(__UpperCAmelCase		).eval()
      _lowerCamelCase           =					hf_model.state_dict()

      # Create src-to-dst parameter name mapping dictionary
      print('''Converting parameters...'''		)
      _lowerCamelCase           =					rename_keys(__UpperCAmelCase		)
      replace_params(__UpperCAmelCase			,    __UpperCAmelCase			,    __UpperCAmelCase		)

      # Initialize processor
      _lowerCamelCase           =					get_processor()
      _lowerCamelCase           =					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():
            _lowerCamelCase           =					hf_model(**__UpperCAmelCase		)

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

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

      _lowerCamelCase           =					original_model.image_encoder(__UpperCAmelCase			,    training=__UpperCAmelCase		)
      _lowerCamelCase           =					original_model.text_encoder(__UpperCAmelCase			,    training=__UpperCAmelCase		)

      _lowerCamelCase           =					tf.nn.la_normalize(__UpperCAmelCase			,    axis=-1		)
      _lowerCamelCase           =					tf.nn.la_normalize(__UpperCAmelCase			,    axis=-1		)

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

      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__":
     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')

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




import numpy as np


class 						lowerCamelCase__			:


						def __init__(			self    :	Tuple   ,							_a    :	List[str]=5   ,							_a    :	Any=5      ):
													a__:							int									=depth
													a__:							Dict									=0
													a__:							str									=None
													a__:							List[Any]									=None
													a__:							List[Any]									=min_leaf_size
													a__:							Dict									=None


						def 	_lowerCamelCase      (			self    :	Union[str, Any]   ,							_a    :	int   ,							_a    :	Optional[int]      ):
													if labels.ndim != 1:
																				print("Error: Input labels must be one dimensional"      )

													return np.mean((labels - prediction) ** 2      )


						def 	_lowerCamelCase      (			self    :	Any   ,							_a    :	List[Any]   ,							_a    :	Dict      ):
													if x.ndim != 1:
																				print("Error: Input data set must be one dimensional"      )
																				return
													if len(_a      ) != len(_a      ):
																				print("Error: X and y have different lengths"      )
																				return
													if y.ndim != 1:
																				print("Error: Data set labels must be one dimensional"      )
																				return

													if len(_a      ) < 2 * self.min_leaf_size:
																				a__:							List[str]									=np.mean(_a      )
																				return

													if self.depth == 1:
																				a__:							Dict									=np.mean(_a      )
																				return

													a__:							Any									=0
													a__:							List[Any]									=self.mean_squared_error(_a   ,							np.mean(_a      )      ) * 2
													for i in range(len(_a      )      ):
																				if len(x[:i]      ) < self.min_leaf_size:
																											continue
																				elif len(x[i:]      ) < self.min_leaf_size:
																											continue
																				else:
																											a__:							List[str]									=self.mean_squared_error(x[:i]   ,							np.mean(y[:i]      )      )
																											a__:							str									=self.mean_squared_error(x[i:]   ,							np.mean(y[i:]      )      )
																											a__:							str									=error_left + error_right
																											if error < min_error:
																																		a__:							Optional[int]									=i
																																		a__:							Tuple									=error

													if best_split != 0:
																				a__:							List[Any]									=x[:best_split]
																				a__:							Any									=y[:best_split]
																				a__:							List[str]									=x[best_split:]
																				a__:							List[str]									=y[best_split:]

																				a__:							List[Any]									=x[best_split]
																				a__:							Any									=DecisionTree(
																				    depth=self.depth - 1   ,							min_leaf_size=self.min_leaf_size      )
																				a__:							Any									=DecisionTree(
																				    depth=self.depth - 1   ,							min_leaf_size=self.min_leaf_size      )
																				self.left.train(_a   ,							_a      )
																				self.right.train(_a   ,							_a      )
													else:
																				a__:							Tuple									=np.mean(_a      )

													return







						def 	_lowerCamelCase      (			self    :	List[str]   ,							_a    :	Tuple      ):
													if self.prediction is not None:
																				return self.prediction
													elif self.left or self.right is not None:
																				if x >= self.decision_boundary:
																											return self.right.predict(_a      )
																				else:
																											return self.left.predict(_a      )
													else:
																				print("Error: Decision tree not yet trained"      )
																				return None


class 						lowerCamelCase__			:


						@staticmethod
						def 	_lowerCamelCase      (			_a    :	Optional[int]   ,							_a    :	Optional[int]      ):
													a__:							Optional[int]									=float(0      )
													for label in labels:
																				squared_error_sum += (label - prediction) ** 2

													return float(squared_error_sum / labels.size      )





def     __lowerCamelCase							(   ):
							a__:							Optional[int]									=np.arange(-1.0  ,			1.0  ,			0.005    )
							a__:							List[Any]									=np.sin(__magic_name__    )

							a__:							int									=DecisionTree(depth=10  ,			min_leaf_size=10    )
							tree.train(__magic_name__  ,			__magic_name__    )

							a__:							Union[str, Any]									=(np.random.rand(10    ) * 2) - 1
							a__:							Any									=np.array([tree.predict(__magic_name__    ) for x in test_cases]    )
							a__:							Dict									=np.mean((predictions - test_cases) ** 2    )

							print("Test values: " + str(__magic_name__    )    )
							print("Predictions: " + str(__magic_name__    )    )
							print("Average error: " + str(__magic_name__    )    )


if __name__ == "__main__":
		main()
		import doctest

		doctest.testmod(name='''mean_squarred_error''', verbose=True)





