



'''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  :     Dict       =       logging.get_logger(__name__)



def  __lowerCamelCase    (   _lowercase						) ->     Tuple:
							UpperCAmelCase	:						str					=						tf.image.resize(_lowercase      ,							(3_4_6, 3_4_6)						)
							UpperCAmelCase	:						List[Any]					=						tf.image.crop_to_bounding_box(_lowercase      ,							(3_4_6 - 2_8_9) // 2      ,							(3_4_6 - 2_8_9) // 2      ,							2_8_9      ,							2_8_9						)
							return image



def  __lowerCamelCase    (   ) ->     str:
							UpperCAmelCase	:						Optional[Any]					=						EfficientNetConfig.from_pretrained("""google/efficientnet-b7"""						)
							UpperCAmelCase	:						int					=						2_8_9
							UpperCAmelCase	:						List[Any]					=						6_4_0
							UpperCAmelCase	:						Optional[Any]					=						{"""0""": """LABEL_0""", """1""": """LABEL_1"""}
							UpperCAmelCase	:						str					=						{"""LABEL_0""": 0, """LABEL_1""": 1}
							UpperCAmelCase	:						Dict					=						[]

							UpperCAmelCase	:						int					=						BertConfig()
							UpperCAmelCase	:						Optional[Any]					=						AlignConfig.from_text_vision_configs(
							    text_config=_lowercase      ,							vision_config=_lowercase      ,							projection_dim=6_4_0						)
							return config



def  __lowerCamelCase    (   ) ->     int:
							UpperCAmelCase	:						List[str]					=						"""http://images.cocodataset.org/val2017/000000039769.jpg"""
							UpperCAmelCase	:						Union[str, Any]					=						Image.open(requests.get(_lowercase      ,							stream=_lowercase						).raw						)
							return im



def  __lowerCamelCase    (   ) ->     int:
							UpperCAmelCase	:						Dict					=						EfficientNetImageProcessor(
							    do_center_crop=_lowercase      ,							rescale_factor=1 / 127.5      ,							rescale_offset=_lowercase      ,							do_normalize=_lowercase      ,							include_top=_lowercase      ,							resample=Image.BILINEAR      ,							)
							UpperCAmelCase	:						int					=						BertTokenizer.from_pretrained("""bert-base-uncased"""						)
							UpperCAmelCase	:						Optional[int]					=						6_4
							UpperCAmelCase	:						Dict					=						AlignProcessor(image_processor=_lowercase      ,							tokenizer=_lowercase						)
							return processor



def  __lowerCamelCase    (   _lowercase						) ->     Union[str, Any]:
							# EfficientNet image encoder
							UpperCAmelCase	:						int					=						[v.split("""_"""						)[0].split("""block"""						)[1] for v in original_param_names if v.startswith("""block"""						)]
							UpperCAmelCase	:						Dict					=						list(set(_lowercase						)						)
							UpperCAmelCase	:						str					=						sorted(_lowercase						)
							UpperCAmelCase	:						Any					=						len(_lowercase						)
							UpperCAmelCase	:						List[Any]					=						{b: str(_lowercase						) for b, i in zip(_lowercase      ,							range(_lowercase						)						)}

							UpperCAmelCase	:						List[Any]					=						[]
							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	:						Union[str, Any]					=						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	:						Union[str, Any]					=						{}
							for item in rename_keys:
														if item[0] in original_param_names:
																					UpperCAmelCase	:						Union[str, Any]					=						"""vision_model.""" + item[1]

    # BERT text encoder
							UpperCAmelCase	:						int					=						[]
							UpperCAmelCase	:						Union[str, Any]					=						"""tf_bert_model/bert"""
							UpperCAmelCase	:						Optional[int]					=						"""text_model"""
							for i in range(1_2						):
														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	:						Union[str, Any]					=						item[1]
							return key_mapping



def  __lowerCamelCase    (   _lowercase      ,							_lowercase      ,							_lowercase						) ->     List[str]:
							list(hf_params.keys()						)

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

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

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



@torch.no_grad()
def  __lowerCamelCase    (   _lowercase      ,							_lowercase      ,							_lowercase      ,							_lowercase						) ->     Any:
							UpperCAmelCase	:						Dict					=						6_4
							UpperCAmelCase	:						Tuple					=						Tokenizer(_lowercase						)
							UpperCAmelCase	:						Tuple					=						align.Align("""efficientnet-b7"""      ,							"""bert-base"""      ,							6_4_0      ,							_lowercase      ,							tok.get_vocab_size()						)
							original_model.compile()
							original_model.load_weights(_lowercase						)

							UpperCAmelCase	:						int					=						original_model.trainable_variables
							UpperCAmelCase	:						str					=						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	:						List[str]					=						param.numpy()
							UpperCAmelCase	:						List[Any]					=						list(tf_params.keys()						)

							# Load HuggingFace model
							UpperCAmelCase	:						Tuple					=						get_align_config()
							UpperCAmelCase	:						str					=						AlignModel(_lowercase						).eval()
							UpperCAmelCase	:						Optional[Any]					=						hf_model.state_dict()

							# Create src-to-dst parameter name mapping dictionary
							print("""Converting parameters..."""						)
							UpperCAmelCase	:						Optional[Any]					=						rename_keys(_lowercase						)
							replace_params(_lowercase      ,							_lowercase      ,							_lowercase						)

							# Initialize processor
							UpperCAmelCase	:						List[str]					=						get_processor()
							UpperCAmelCase	:						Optional[Any]					=						processor(
							    images=prepare_img()      ,							text="""A picture of a cat"""      ,							padding="""max_length"""      ,							max_length=6_4      ,							return_tensors="""pt"""						)

							# HF model inference
							hf_model.eval()
							with torch.no_grad():
														UpperCAmelCase	:						Optional[int]					=						hf_model(**_lowercase						)

							UpperCAmelCase	:						List[Any]					=						outputs.image_embeds.detach().numpy()
							UpperCAmelCase	:						Union[str, Any]					=						outputs.text_embeds.detach().numpy()

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

							UpperCAmelCase	:						Any					=						original_model.image_encoder(_lowercase      ,							training=_lowercase						)
							UpperCAmelCase	:						Dict					=						original_model.text_encoder(_lowercase      ,							training=_lowercase						)

							UpperCAmelCase	:						Any					=						tf.nn.la_normalize(_lowercase      ,							axis=-1						)
							UpperCAmelCase	:						Any					=						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__":
							a  :     str       =       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  :     Any       =       parser.parse_args()
							convert_align_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.save_model, args.push_to_hub)






'''simple docstring'''




import sys



def  __lowerCamelCase    (   _lowercase      ,							_lowercase						) ->     int:
							if not numbers:
														return 0
							if target == 0 and target in numbers:
														return 0
							if not isinstance(_lowercase      ,							(list, tuple)						) or not all(
							    isinstance(_lowercase      ,							_lowercase						) for number in numbers						):
														raise ValueError("""numbers must be an iterable of integers"""						)

							UpperCAmelCase	:						Optional[Any]					=						0
							UpperCAmelCase	:						List[str]					=						sys.maxsize

							while right < len(_lowercase						):
														curr_sum += numbers[right]
														while curr_sum >= target and left <= right:
																					UpperCAmelCase	:						Optional[Any]					=						min(_lowercase      ,							right - left + 1						)
																					curr_sum -= numbers[left]
																					left += 1
														right += 1

							return 0 if min_len == sys.maxsize else min_len

