





"""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__        =      logging.get_logger(__name__)


def    a__       (       _SCREAMING_SNAKE_CASE     ):




		"""simple docstring"""


		UpperCamelCase =						tf.image.resize(_SCREAMING_SNAKE_CASE	,						(346, 346)     )
		UpperCamelCase =						tf.image.crop_to_bounding_box(_SCREAMING_SNAKE_CASE	,						(346 - 289) // 2	,						(346 - 289) // 2	,						289	,						289     )
		return image


def    a__       (       ):




		"""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=_SCREAMING_SNAKE_CASE	,						vision_config=_SCREAMING_SNAKE_CASE	,						projection_dim=640     )
		return config


def    a__       (       ):




		"""simple docstring"""


		UpperCamelCase =						"http://images.cocodataset.org/val2017/000000039769.jpg"
		UpperCamelCase =						Image.open(requests.get(_SCREAMING_SNAKE_CASE	,						stream=_SCREAMING_SNAKE_CASE     ).raw     )
		return im


def    a__       (       ):




		"""simple docstring"""


		UpperCamelCase =						EfficientNetImageProcessor(
		    do_center_crop=_SCREAMING_SNAKE_CASE	,						rescale_factor=1 / 1_27.5	,						rescale_offset=_SCREAMING_SNAKE_CASE	,						do_normalize=_SCREAMING_SNAKE_CASE	,						include_top=_SCREAMING_SNAKE_CASE	,						resample=Image.BILINEAR	,						)
		UpperCamelCase =						BertTokenizer.from_pretrained("bert-base-uncased"     )
		UpperCamelCase =						64
		UpperCamelCase =						AlignProcessor(image_processor=_SCREAMING_SNAKE_CASE	,						tokenizer=_SCREAMING_SNAKE_CASE     )
		return processor


def    a__       (       _SCREAMING_SNAKE_CASE     ):




		"""simple docstring"""


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

		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__       (       _SCREAMING_SNAKE_CASE	,						_SCREAMING_SNAKE_CASE	,						_SCREAMING_SNAKE_CASE     ):




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

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


@torch.no_grad()
def    a__       (       _SCREAMING_SNAKE_CASE	,						_SCREAMING_SNAKE_CASE	,						_SCREAMING_SNAKE_CASE	,						_SCREAMING_SNAKE_CASE     ):




		"""simple docstring"""


		UpperCamelCase =						64
		UpperCamelCase =						Tokenizer(_SCREAMING_SNAKE_CASE     )
		UpperCamelCase =						align.Align("efficientnet-b7"	,						"bert-base"	,						640	,						_SCREAMING_SNAKE_CASE	,						tok.get_vocab_size()     )
		original_model.compile()
		original_model.load_weights(_SCREAMING_SNAKE_CASE     )

		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(_SCREAMING_SNAKE_CASE     ).eval()
		UpperCamelCase =						hf_model.state_dict()

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

		# 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(**_SCREAMING_SNAKE_CASE     )

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

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

		UpperCamelCase =						tf.nn.la_normalize(_SCREAMING_SNAKE_CASE	,						axis=-1     )
		UpperCamelCase =						tf.nn.la_normalize(_SCREAMING_SNAKE_CASE	,						axis=-1     )

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

		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__        =      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__        =      parser.parse_args()
						convert_align_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.save_model, args.push_to_hub)













"""simple docstring"""





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowerCAmelCase__        =      {
    '''configuration_deberta_v2''': ['''DEBERTA_V2_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''DebertaV2Config''', '''DebertaV2OnnxConfig'''],
    '''tokenization_deberta_v2''': ['''DebertaV2Tokenizer'''],
}

try:
						if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCAmelCase__        =      ['''DebertaV2TokenizerFast''']

try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCAmelCase__        =      [
						    '''TF_DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST''',
						    '''TFDebertaV2ForMaskedLM''',
						    '''TFDebertaV2ForQuestionAnswering''',
						    '''TFDebertaV2ForSequenceClassification''',
						    '''TFDebertaV2ForTokenClassification''',
						    '''TFDebertaV2Model''',
						    '''TFDebertaV2PreTrainedModel''',
						]

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCAmelCase__        =      [
						    '''DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST''',
						    '''DebertaV2ForMaskedLM''',
						    '''DebertaV2ForMultipleChoice''',
						    '''DebertaV2ForQuestionAnswering''',
						    '''DebertaV2ForSequenceClassification''',
						    '''DebertaV2ForTokenClassification''',
						    '''DebertaV2Model''',
						    '''DebertaV2PreTrainedModel''',
						]


if TYPE_CHECKING:
						from .configuration_deberta_va import (
						    DEBERTA_V2_PRETRAINED_CONFIG_ARCHIVE_MAP,
						    DebertaVaConfig,
						    DebertaVaOnnxConfig,
						)
						from .tokenization_deberta_va import DebertaVaTokenizer

						try:
												if not is_tokenizers_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_deberta_va_fast import DebertaVaTokenizerFast

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_deberta_va import (
												    TF_DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST,
												    TFDebertaVaForMaskedLM,
												    TFDebertaVaForQuestionAnswering,
												    TFDebertaVaForSequenceClassification,
												    TFDebertaVaForTokenClassification,
												    TFDebertaVaModel,
												    TFDebertaVaPreTrainedModel,
												)

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_deberta_va import (
												    DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST,
												    DebertaVaForMaskedLM,
												    DebertaVaForMultipleChoice,
												    DebertaVaForQuestionAnswering,
												    DebertaVaForSequenceClassification,
												    DebertaVaForTokenClassification,
												    DebertaVaModel,
												    DebertaVaPreTrainedModel,
												)

else:
						import sys

						lowerCAmelCase__        =      _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)






