





'''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()
lowercase__		:    Tuple =       logging.get_logger(__name__)







def       a__       (       lowercase      :  List[Any]   )	->	Union[str, Any]:





					"""simple docstring"""

					_UpperCamelCase       					= tf.image.resize(lowercase,      (346, 346)   )
					_UpperCamelCase       					= tf.image.crop_to_bounding_box(lowercase,      (346 - 289) // 2,      (346 - 289) // 2,      289,      289   )
					return image







def       a__       (       )	->	Union[str, Any]:





					"""simple docstring"""

					_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=lowercase,      vision_config=lowercase,      projection_dim=640   )
					return config







def       a__       (       )	->	List[Any]:





					"""simple docstring"""

					_UpperCamelCase       					= '''http://images.cocodataset.org/val2017/000000039769.jpg'''
					_UpperCamelCase       					= Image.open(requests.get(lowercase,      stream=lowercase   ).raw   )
					return im







def       a__       (       )	->	int:





					"""simple docstring"""

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







def       a__       (       lowercase      :  Union[str, Any]   )	->	List[Any]:





					"""simple docstring"""

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

					_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       a__       (       lowercase      :  Any,      lowercase      :  Dict,      lowercase      :  Tuple   )	->	Optional[Any]:





					"""simple docstring"""

					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(lowercase   ).permute(3,      2,      0,      1   )
										elif "embeddings" in key:
															_UpperCamelCase       					= torch.from_numpy(lowercase   )
										elif "depthwise_kernel" in key:
															_UpperCamelCase       					= torch.from_numpy(lowercase   ).permute(2,      3,      0,      1   )
										elif "kernel" in key:
															_UpperCamelCase       					= torch.from_numpy(np.transpose(lowercase   )   )
										elif "temperature" in key:
															_UpperCamelCase       					= value
										elif "bn/gamma" or "bn/beta" in key:
															_UpperCamelCase       					= torch.from_numpy(np.transpose(lowercase   )   ).squeeze()
										else:
															_UpperCamelCase       					= torch.from_numpy(lowercase   )

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







@torch.no_grad()
def       a__       (       lowercase      :  Optional[Any],      lowercase      :  str,      lowercase      :  List[str],      lowercase      :  List[str]   )	->	List[str]:





					"""simple docstring"""

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

					_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(lowercase   ).eval()
					_UpperCamelCase       					= hf_model.state_dict()

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

					# 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(**lowercase   )

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

					# Original model inference
					_UpperCamelCase       					= False
					_UpperCamelCase       					= EfficientNetImageProcessor(
					    do_center_crop=lowercase,      do_rescale=lowercase,      do_normalize=lowercase,      include_top=lowercase,      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(lowercase,      training=lowercase   )
					_UpperCamelCase       					= original_model.text_encoder(lowercase,      training=lowercase   )

					_UpperCamelCase       					= tf.nn.la_normalize(lowercase,      axis=-1   )
					_UpperCamelCase       					= tf.nn.la_normalize(lowercase,      axis=-1   )

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

					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__":
			lowercase__		:    List[Any] =       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')

			lowercase__		:    Optional[int] =       parser.parse_args()
			convert_align_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.save_model, args.push_to_hub)










'''simple docstring'''

lowercase__		:    Tuple =       '\n# Transformers installation\n! pip install transformers datasets evaluate\n# To install from source instead of the last release, comment the command above and uncomment the following one.\n# ! pip install git+https://github.com/huggingface/transformers.git\n'

lowercase__		:    Union[str, Any] =       [{'type': 'code', 'content': INSTALL_CONTENT}]
lowercase__		:    str =       {
    '{processor_class}': 'FakeProcessorClass',
    '{model_class}': 'FakeModelClass',
    '{object_class}': 'FakeObjectClass',
}



