




"""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()
_lowerCamelCase					:     Dict				       =    logging.get_logger(__name__)

def 						lowercase_ (						_UpperCAmelCase		):




	"""simple docstring"""






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

def 						lowercase_ (						):




	"""simple docstring"""






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

	A_					:			List[Any]						=						BertConfig()
	A_					:			List[str]						=						AlignConfig.from_text_vision_configs(
	    text_config=_UpperCAmelCase					, vision_config=_UpperCAmelCase					, projection_dim=640		)
	return config

def 						lowercase_ (						):




	"""simple docstring"""






	A_					:			Union[str, Any]						=						'''http://images.cocodataset.org/val2017/000000039769.jpg'''
	A_					:			Any						=						Image.open(requests.get(_UpperCAmelCase					, stream=_UpperCAmelCase		).raw		)
	return im

def 						lowercase_ (						):




	"""simple docstring"""






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

def 						lowercase_ (						_UpperCAmelCase		):




	"""simple docstring"""






	A_					:			Optional[int]						=						[v.split('''_'''		)[0].split('''block'''		)[1] for v in original_param_names if v.startswith('''block'''		)]
	A_					:			Dict						=						list(set(_UpperCAmelCase		)		)
	A_					:			Any						=						sorted(_UpperCAmelCase		)
	A_					:			Union[str, Any]						=						len(_UpperCAmelCase		)
	A_					:			List[str]						=						{b: str(_UpperCAmelCase		) for b, i in zip(_UpperCAmelCase					, range(_UpperCAmelCase		)		)}

	A_					:			Tuple						=						[]
	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:
		A_					:			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""")		)

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

    # BERT text encoder
	A_					:			Tuple						=						[]
	A_					:			List[str]						=						'''tf_bert_model/bert'''
	A_					:			List[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:
			A_					:			Dict						=						item[1]
	return key_mapping

def 						lowercase_ (						_UpperCAmelCase					, _UpperCAmelCase					, _UpperCAmelCase		):




	"""simple docstring"""






	list(hf_params.keys()		)

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

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

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

@torch.no_grad()
def 						lowercase_ (						_UpperCAmelCase					, _UpperCAmelCase					, _UpperCAmelCase					, _UpperCAmelCase		):




	"""simple docstring"""






	A_					:			int						=						64
	A_					:			Optional[int]						=						Tokenizer(_UpperCAmelCase		)
	A_					:			Any						=						align.Align('''efficientnet-b7'''					, '''bert-base'''					, 640					, _UpperCAmelCase					, tok.get_vocab_size()		)
	original_model.compile()
	original_model.load_weights(_UpperCAmelCase		)

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

	# Load HuggingFace model
	A_					:			Any						=						get_align_config()
	A_					:			Dict						=						AlignModel(_UpperCAmelCase		).eval()
	A_					:			List[str]						=						hf_model.state_dict()

	# Create src-to-dst parameter name mapping dictionary
	print('''Converting parameters...'''		)
	A_					:			Dict						=						rename_keys(_UpperCAmelCase		)
	replace_params(_UpperCAmelCase					, _UpperCAmelCase					, _UpperCAmelCase		)

	# Initialize processor
	A_					:			Optional[Any]						=						get_processor()
	A_					:			Dict						=						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():
		A_					:			Union[str, Any]						=						hf_model(**_UpperCAmelCase		)

	A_					:			str						=						outputs.image_embeds.detach().numpy()
	A_					:			Any						=						outputs.text_embeds.detach().numpy()

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

	A_					:			Any						=						original_model.image_encoder(_UpperCAmelCase					, training=_UpperCAmelCase		)
	A_					:			Optional[Any]						=						original_model.text_encoder(_UpperCAmelCase					, training=_UpperCAmelCase		)

	A_					:			int						=						tf.nn.la_normalize(_UpperCAmelCase					, axis=-1		)
	A_					:			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__":
		_lowerCamelCase					:     Optional[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')

		_lowerCamelCase					:     Optional[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 enum
import inspect
import warnings
from functools import wraps
from typing import Callable, Optional

from .logging import get_logger


_lowerCamelCase					:     Optional[int]				       =    set()
_lowerCamelCase					:     Union[str, Any]				       =    get_logger(__name__)

def 						lowercase_ (						_UpperCAmelCase = None		):




	"""simple docstring"""






	def decorator(_UpperCAmelCase		):
		global _emitted_deprecation_warnings

		if inspect.isclass(_UpperCAmelCase		):
			A_					:			Union[str, Any]						=						deprecated_class_or_function.__init__
			A_					:			int						=						deprecated_class_or_function.__name__
		else:
			A_					:			Union[str, Any]						=						deprecated_class_or_function
			A_					:			List[Any]						=						deprecated_function.__name__
			# Support deprecating __init__ class method: class name instead
			A_					:			List[Any]						=						name if name != '''__init__''' else deprecated_function.__qualname__.split('''.'''		)[-2]

		A_					:			str						=						(
		    f"""{name} is deprecated and will be removed in the next major version of datasets.""" + f""" {help_message}"""
		    if help_message
		    else ''''''
		)

		@wraps(_UpperCAmelCase		)
		def wrapper(*_UpperCAmelCase					, **_UpperCAmelCase		):
			A_					:			Any						=						hash(_UpperCAmelCase		)
			if func_hash not in _emitted_deprecation_warnings:
				warnings.warn(_UpperCAmelCase					, category=_UpperCAmelCase					, stacklevel=2		)
				_emitted_deprecation_warnings.add(_UpperCAmelCase		)
			return deprecated_function(*_UpperCAmelCase					, **_UpperCAmelCase		)

		A_					:			Any						=						'''deprecated'''

		if inspect.isclass(_UpperCAmelCase		):
			A_					:			List[Any]						=						wrapper
			return deprecated_class_or_function
		else:
			return wrapper

	return decorator

class     lowercase (      enum.EnumMeta):







				def __getattribute__(							cls				:   Any ,       _lowerCamelCase				:   List[Any]    ):



					"""simple docstring"""

					A_					:			str						=						super().__getattribute__(_lowerCamelCase    )
					if isinstance(_lowerCamelCase ,       enum.Enum    ) and obj._on_access:
						obj._on_access()
					return obj







				def __getitem__(							cls				:   Any ,       _lowerCamelCase				:   Union[str, Any]    ):



					"""simple docstring"""

					A_					:			Optional[Any]						=						super().__getitem__(_lowerCamelCase    )
					if member._on_access:
						member._on_access()
					return member







				def __call__(							cls				:   str ,       _lowerCamelCase				:   str ,       _lowerCamelCase				:   List[Any]=None ,       *, _lowerCamelCase				:   int=None ,       _lowerCamelCase				:   str=None ,       _lowerCamelCase				:   Dict=None ,       _lowerCamelCase				:   Tuple=1    ):



					"""simple docstring"""

					A_					:			Dict						=						super().__call__(_lowerCamelCase ,       _lowerCamelCase ,       module=_lowerCamelCase ,       qualname=_lowerCamelCase ,       type=_lowerCamelCase ,       start=_lowerCamelCase    )
					if isinstance(_lowerCamelCase ,       enum.Enum    ) and obj._on_access:
						obj._on_access()
					return obj

class     lowercase (      enum.Enum       ,		metaclass=__UpperCAmelCase):







				def __new__(							cls				:   Optional[Any] ,       _lowerCamelCase				:   int    ):



					"""simple docstring"""

					A_					:			Tuple						=						object.__new__(cls    )
					A_					:			Union[str, Any]						=						value
					A_					:			Union[str, Any]						=						member.deprecate
					return member







				@property
				def    a_					(							self				:   Tuple    ):



					"""simple docstring"""

					return ""







				def    a_					(							self				:   Any    ):



					"""simple docstring"""

					A_					:			Optional[int]						=						F""" {self.help_message}""" if self.help_message else ''''''
					warnings.warn(
					    F"""'{self.__objclass__.__name__}' is deprecated and will be removed in the next major version of datasets."""
					    + help_message ,       _lowerCamelCase ,       stacklevel=3 ,       )





