"""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()
UpperCamelCase_							:							Optional[Any]				 =		logging.get_logger(__name__)


def 	a__   (		UpperCAmelCase__							:      Optional[Any]      )->						Any:
						UpperCAmelCase__       						=		tf.image.resize(UpperCAmelCase__		,      (3_4_6, 3_4_6)      )
						UpperCAmelCase__       						=		tf.image.crop_to_bounding_box(UpperCAmelCase__		,      (3_4_6 - 2_8_9) // 2		,      (3_4_6 - 2_8_9) // 2		,      2_8_9		,      2_8_9      )
						return image


def 	a__   (		)->						Dict:
						UpperCAmelCase__       						=		EfficientNetConfig.from_pretrained("""google/efficientnet-b7"""      )
						UpperCAmelCase__       						=		2_8_9
						UpperCAmelCase__       						=		6_4_0
						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=UpperCAmelCase__		,      vision_config=UpperCAmelCase__		,      projection_dim=6_4_0      )
						return config


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


def 	a__   (		)->						Union[str, Any]:
						UpperCAmelCase__       						=		EfficientNetImageProcessor(
						    do_center_crop=UpperCAmelCase__		,      rescale_factor=1 / 1_27.5		,      rescale_offset=UpperCAmelCase__		,      do_normalize=UpperCAmelCase__		,      include_top=UpperCAmelCase__		,      resample=Image.BILINEAR		,      )
						UpperCAmelCase__       						=		BertTokenizer.from_pretrained("""bert-base-uncased"""      )
						UpperCAmelCase__       						=		6_4
						UpperCAmelCase__       						=		AlignProcessor(image_processor=UpperCAmelCase__		,      tokenizer=UpperCAmelCase__      )
						return processor


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

						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(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__       						=		item[1]
						return key_mapping


def 	a__   (		UpperCAmelCase__							:      Union[str, Any]		,      UpperCAmelCase__							:      int		,      UpperCAmelCase__							:      List[str]      )->						int:
						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(UpperCAmelCase__      ).permute(3		,      2		,      0		,      1      )
												elif "embeddings" in key:
																		UpperCAmelCase__       						=		torch.from_numpy(UpperCAmelCase__      )
												elif "depthwise_kernel" in key:
																		UpperCAmelCase__       						=		torch.from_numpy(UpperCAmelCase__      ).permute(2		,      3		,      0		,      1      )
												elif "kernel" in key:
																		UpperCAmelCase__       						=		torch.from_numpy(np.transpose(UpperCAmelCase__      )      )
												elif "temperature" in key:
																		UpperCAmelCase__       						=		value
												elif "bn/gamma" or "bn/beta" in key:
																		UpperCAmelCase__       						=		torch.from_numpy(np.transpose(UpperCAmelCase__      )      ).squeeze()
												else:
																		UpperCAmelCase__       						=		torch.from_numpy(UpperCAmelCase__      )

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


@torch.no_grad()
def 	a__   (		UpperCAmelCase__							:      List[Any]		,      UpperCAmelCase__							:      List[Any]		,      UpperCAmelCase__							:      List[Any]		,      UpperCAmelCase__							:      Any      )->						List[Any]:
						UpperCAmelCase__       						=		6_4
						UpperCAmelCase__       						=		Tokenizer(UpperCAmelCase__      )
						UpperCAmelCase__       						=		align.Align("""efficientnet-b7"""		,      """bert-base"""		,      6_4_0		,      UpperCAmelCase__		,      tok.get_vocab_size()      )
						original_model.compile()
						original_model.load_weights(UpperCAmelCase__      )

						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(UpperCAmelCase__      ).eval()
						UpperCAmelCase__       						=		hf_model.state_dict()

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

						# Initialize processor
						UpperCAmelCase__       						=		get_processor()
						UpperCAmelCase__       						=		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__       						=		hf_model(**UpperCAmelCase__      )

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

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

						UpperCAmelCase__       						=		tf.nn.la_normalize(UpperCAmelCase__		,      axis=-1      )
						UpperCAmelCase__       						=		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__":
						UpperCamelCase_							:							Dict				 =		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""")

						UpperCamelCase_							:							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"""







from ...utils import (
    OptionalDependencyNotAvailable,
    is_torch_available,
    is_transformers_available,
    is_transformers_version,
)


try:
						if not (is_transformers_available() and is_torch_available() and is_transformers_version(""">=""", """4.27.0""")):
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						from ...utils.dummy_torch_and_transformers_objects import (
						    AudioLDMPipeline,
						)
else:
						from .pipeline_audioldm import AudioLDMPipeline

