

"""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()
_SCREAMING_SNAKE_CASE							:	Union[str, Any]										=			logging.get_logger(__name__)



def 							_lowerCAmelCase   (    UpperCAmelCase  :				Any					):


					'''simple docstring'''






					UpperCamelCase__		:     Any								=tf.image.resize(UpperCAmelCase   ,				(346, 346)					)
					UpperCamelCase__		:     Optional[Any]								=tf.image.crop_to_bounding_box(UpperCAmelCase   ,				(346 - 289) // 2   ,				(346 - 289) // 2   ,				289   ,				289					)
					return image



def 							_lowerCAmelCase   (    ):


					'''simple docstring'''






					UpperCamelCase__		:     List[str]								=EfficientNetConfig.from_pretrained('''google/efficientnet-b7'''					)
					UpperCamelCase__		:     List[str]								=289
					UpperCamelCase__		:     Union[str, Any]								=640
					UpperCamelCase__		:     Optional[int]								={'''0''': '''LABEL_0''', '''1''': '''LABEL_1'''}
					UpperCamelCase__		:     List[Any]								={'''LABEL_0''': 0, '''LABEL_1''': 1}
					UpperCamelCase__		:     Tuple								=[]

					UpperCamelCase__		:     Optional[Any]								=BertConfig()
					UpperCamelCase__		:     Any								=AlignConfig.from_text_vision_configs(
					    text_config=UpperCAmelCase   ,				vision_config=UpperCAmelCase   ,				projection_dim=640					)
					return config



def 							_lowerCAmelCase   (    ):


					'''simple docstring'''






					UpperCamelCase__		:     Optional[Any]								='''http://images.cocodataset.org/val2017/000000039769.jpg'''
					UpperCamelCase__		:     List[str]								=Image.open(requests.get(UpperCAmelCase   ,				stream=UpperCAmelCase					).raw					)
					return im



def 							_lowerCAmelCase   (    ):


					'''simple docstring'''






					UpperCamelCase__		:     str								=EfficientNetImageProcessor(
					    do_center_crop=UpperCAmelCase   ,				rescale_factor=1 / 127.5   ,				rescale_offset=UpperCAmelCase   ,				do_normalize=UpperCAmelCase   ,				include_top=UpperCAmelCase   ,				resample=Image.BILINEAR   ,				)
					UpperCamelCase__		:     Optional[Any]								=BertTokenizer.from_pretrained('''bert-base-uncased'''					)
					UpperCamelCase__		:     str								=64
					UpperCamelCase__		:     Optional[Any]								=AlignProcessor(image_processor=UpperCAmelCase   ,				tokenizer=UpperCAmelCase					)
					return processor



def 							_lowerCAmelCase   (    UpperCAmelCase  :				Union[str, Any]					):


					'''simple docstring'''






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

					UpperCamelCase__		:     List[str]								=[]
					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__		:     Tuple								=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__		:     Tuple								={}
					for item in rename_keys:
										if item[0] in original_param_names:
															UpperCamelCase__		:     str								='''vision_model.''' + item[1]

    # BERT text encoder
					UpperCamelCase__		:     Union[str, Any]								=[]
					UpperCamelCase__		:     Tuple								='''tf_bert_model/bert'''
					UpperCamelCase__		:     Optional[Any]								='''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__		:     str								=item[1]
					return key_mapping



def 							_lowerCAmelCase   (    UpperCAmelCase  :				List[str]   ,				UpperCAmelCase  :				Any   ,				UpperCAmelCase  :				Any					):


					'''simple docstring'''






					list(hf_params.keys()					)

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

										UpperCamelCase__		:     Optional[int]								=key_mapping[key]
										if "_conv" in key and "kernel" in key:
															UpperCamelCase__		:     List[str]								=torch.from_numpy(UpperCAmelCase					).permute(3   ,				2   ,				0   ,				1					)
										elif "embeddings" in key:
															UpperCamelCase__		:     Optional[Any]								=torch.from_numpy(UpperCAmelCase					)
										elif "depthwise_kernel" in key:
															UpperCamelCase__		:     List[str]								=torch.from_numpy(UpperCAmelCase					).permute(2   ,				3   ,				0   ,				1					)
										elif "kernel" in key:
															UpperCamelCase__		:     Any								=torch.from_numpy(np.transpose(UpperCAmelCase					)					)
										elif "temperature" in key:
															UpperCamelCase__		:     int								=value
										elif "bn/gamma" or "bn/beta" in key:
															UpperCamelCase__		:     Optional[Any]								=torch.from_numpy(np.transpose(UpperCAmelCase					)					).squeeze()
										else:
															UpperCamelCase__		:     Union[str, Any]								=torch.from_numpy(UpperCAmelCase					)

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



@torch.no_grad()
def 							_lowerCAmelCase   (    UpperCAmelCase  :				Any   ,				UpperCAmelCase  :				int   ,				UpperCAmelCase  :				Optional[Any]   ,				UpperCAmelCase  :				str					):


					'''simple docstring'''






					UpperCamelCase__		:     int								=64
					UpperCamelCase__		:     Optional[int]								=Tokenizer(UpperCAmelCase					)
					UpperCamelCase__		:     List[str]								=align.Align('''efficientnet-b7'''   ,				'''bert-base'''   ,				640   ,				UpperCAmelCase   ,				tok.get_vocab_size()					)
					original_model.compile()
					original_model.load_weights(UpperCAmelCase					)

					UpperCamelCase__		:     int								=original_model.trainable_variables
					UpperCamelCase__		:     Dict								=original_model.non_trainable_variables
					UpperCamelCase__		:     List[Any]								={param.name: param.numpy() for param in tf_params}
					for param in tf_non_train_params:
										UpperCamelCase__		:     Union[str, Any]								=param.numpy()
					UpperCamelCase__		:     Any								=list(tf_params.keys()					)

					# Load HuggingFace model
					UpperCamelCase__		:     Union[str, Any]								=get_align_config()
					UpperCamelCase__		:     Any								=AlignModel(UpperCAmelCase					).eval()
					UpperCamelCase__		:     List[str]								=hf_model.state_dict()

					# Create src-to-dst parameter name mapping dictionary
					print('''Converting parameters...'''					)
					UpperCamelCase__		:     Tuple								=rename_keys(UpperCAmelCase					)
					replace_params(UpperCAmelCase   ,				UpperCAmelCase   ,				UpperCAmelCase					)

					# Initialize processor
					UpperCamelCase__		:     str								=get_processor()
					UpperCamelCase__		:     Optional[int]								=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__		:     Optional[int]								=hf_model(**UpperCAmelCase					)

					UpperCamelCase__		:     Tuple								=outputs.image_embeds.detach().numpy()
					UpperCamelCase__		:     Dict								=outputs.text_embeds.detach().numpy()

					# Original model inference
					UpperCamelCase__		:     str								=False
					UpperCamelCase__		:     Any								=EfficientNetImageProcessor(
					    do_center_crop=UpperCAmelCase   ,				do_rescale=UpperCAmelCase   ,				do_normalize=UpperCAmelCase   ,				include_top=UpperCAmelCase   ,				resample=Image.BILINEAR   ,				)
					UpperCamelCase__		:     List[Any]								=tf_image_processor(images=prepare_img()   ,				return_tensors='''tf'''   ,				data_format='''channels_last'''					)['''pixel_values''']
					UpperCamelCase__		:     Union[str, Any]								=tok(tf.constant(['''A picture of a cat''']					)					)

					UpperCamelCase__		:     int								=original_model.image_encoder(UpperCAmelCase   ,				training=UpperCAmelCase					)
					UpperCamelCase__		:     List[str]								=original_model.text_encoder(UpperCAmelCase   ,				training=UpperCAmelCase					)

					UpperCamelCase__		:     Optional[int]								=tf.nn.la_normalize(UpperCAmelCase   ,				axis=-1					)
					UpperCamelCase__		:     Union[str, Any]								=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__":
				_SCREAMING_SNAKE_CASE							:	Optional[int]										=			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							:	Union[str, Any]										=			parser.parse_args()
				convert_align_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.save_model, args.push_to_hub)





"""simple docstring"""





def 							_lowerCAmelCase   (    UpperCAmelCase  :				int   ,				UpperCAmelCase  :				int   ,				UpperCAmelCase  :				int					):


					'''simple docstring'''






					if power < 0:
										return -1
					base %= mod
					UpperCamelCase__		:     Any								=1

					while power > 0:
										if power & 1:
															UpperCamelCase__		:     Optional[Any]								=(result * base) % mod
										UpperCamelCase__		:     Union[str, Any]								=power >> 1
										UpperCamelCase__		:     Union[str, Any]								=(base * base) % mod

					return result



def 							_lowerCAmelCase   (    ):


					'''simple docstring'''






					print(modular_exponential(3   ,				200   ,				13					)					)


if __name__ == "__main__":
				import doctest

				doctest.testmod()

				main()


