'''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()
snake_case__	:     Any      =    logging.get_logger(__name__)


def    _lowerCamelCase       ( lowerCamelCase_   :				Optional[int]  ):



     """simple docstring"""





     UpperCAmelCase_				:  Optional[Any]  = tf.image.resize(lowerCamelCase_      ,	(346, 346)  )
     UpperCAmelCase_				:  Dict  = tf.image.crop_to_bounding_box(lowerCamelCase_      ,	(346 - 289) // 2      ,	(346 - 289) // 2      ,	289      ,	289  )
     return image


def    _lowerCamelCase       ( ):



     """simple docstring"""





     UpperCAmelCase_				:  Union[str, Any]  = EfficientNetConfig.from_pretrained('google/efficientnet-b7'  )
     UpperCAmelCase_				:  List[Any]  = 289
     UpperCAmelCase_				:  Union[str, Any]  = 640
     UpperCAmelCase_				:  Tuple  = {'0': 'LABEL_0', '1': 'LABEL_1'}
     UpperCAmelCase_				:  List[Any]  = {'LABEL_0': 0, 'LABEL_1': 1}
     UpperCAmelCase_				:  List[Any]  = []

     UpperCAmelCase_				:  Optional[Any]  = BertConfig()
     UpperCAmelCase_				:  Optional[int]  = AlignConfig.from_text_vision_configs(
         text_config=lowerCamelCase_      ,	vision_config=lowerCamelCase_      ,	projection_dim=640  )
     return config


def    _lowerCamelCase       ( ):



     """simple docstring"""





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


def    _lowerCamelCase       ( ):



     """simple docstring"""





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


def    _lowerCamelCase       ( lowerCamelCase_   :				str  ):



     """simple docstring"""





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

     UpperCAmelCase_				:  List[str]  = []
     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_				:  List[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_				:  str  = {}
     for item in rename_keys:
          if item[0] in original_param_names:
               UpperCAmelCase_				:  Dict  = 'vision_model.' + item[1]

    # BERT text encoder
     UpperCAmelCase_				:  Dict  = []
     UpperCAmelCase_				:  str  = 'tf_bert_model/bert'
     UpperCAmelCase_				:  Optional[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:
               UpperCAmelCase_				:  Optional[Any]  = item[1]
     return key_mapping


def    _lowerCamelCase       ( lowerCamelCase_   :				Union[str, Any]      ,	lowerCamelCase_   :				Union[str, Any]      ,	lowerCamelCase_   :				str  ):



     """simple docstring"""





     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_				:  Dict  = torch.from_numpy(lowerCamelCase_  ).permute(3      ,	2      ,	0      ,	1  )
          elif "embeddings" in key:
               UpperCAmelCase_				:  Optional[Any]  = torch.from_numpy(lowerCamelCase_  )
          elif "depthwise_kernel" in key:
               UpperCAmelCase_				:  str  = torch.from_numpy(lowerCamelCase_  ).permute(2      ,	3      ,	0      ,	1  )
          elif "kernel" in key:
               UpperCAmelCase_				:  Dict  = torch.from_numpy(np.transpose(lowerCamelCase_  )  )
          elif "temperature" in key:
               UpperCAmelCase_				:  Dict  = value
          elif "bn/gamma" or "bn/beta" in key:
               UpperCAmelCase_				:  int  = torch.from_numpy(np.transpose(lowerCamelCase_  )  ).squeeze()
          else:
               UpperCAmelCase_				:  str  = torch.from_numpy(lowerCamelCase_  )

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


@torch.no_grad()
def    _lowerCamelCase       ( lowerCamelCase_   :				List[Any]      ,	lowerCamelCase_   :				Any      ,	lowerCamelCase_   :				Any      ,	lowerCamelCase_   :				List[Any]  ):



     """simple docstring"""





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

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

     # Load HuggingFace model
     UpperCAmelCase_				:  Union[str, Any]  = get_align_config()
     UpperCAmelCase_				:  List[str]  = AlignModel(lowerCamelCase_  ).eval()
     UpperCAmelCase_				:  List[Any]  = hf_model.state_dict()

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

     # Initialize processor
     UpperCAmelCase_				:  Union[str, Any]  = get_processor()
     UpperCAmelCase_				:  Optional[Any]  = 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_				:  Optional[Any]  = hf_model(**lowerCamelCase_  )

     UpperCAmelCase_				:  Optional[Any]  = outputs.image_embeds.detach().numpy()
     UpperCAmelCase_				:  str  = outputs.text_embeds.detach().numpy()

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

     UpperCAmelCase_				:  Optional[int]  = original_model.image_encoder(lowerCamelCase_      ,	training=lowerCamelCase_  )
     UpperCAmelCase_				:  Optional[int]  = original_model.text_encoder(lowerCamelCase_      ,	training=lowerCamelCase_  )

     UpperCAmelCase_				:  Union[str, Any]  = tf.nn.la_normalize(lowerCamelCase_      ,	axis=-1  )
     UpperCAmelCase_				:  Optional[int]  = tf.nn.la_normalize(lowerCamelCase_      ,	axis=-1  )

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

     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__":
 snake_case__	:     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''')

 snake_case__	:     Tuple      =    parser.parse_args()
 convert_align_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.save_model, args.push_to_hub)











"""simple docstring"""


import os
from dataclasses import replace

import jax
import wandb
from bigbird_flax import Args, DataCollator, FlaxBigBirdForNaturalQuestions, Trainer, build_tx, train_step, val_step
from datasets import load_dataset
from flax import jax_utils

from transformers import BigBirdTokenizerFast


if __name__ == "__main__":
				print("""#################### AVAILABLE DEVICES ####################""")
				print(jax.devices())
				print("""###########################################################""")

				# setup for wandb sweep
				snake_case_								=			Args()
				snake_case_								=			wandb.init(project="""bigbird-natural-questions""", config=args.__dict__)
				snake_case_								=			dict(logger.config)
				del wandb_args["batch_size"]
				snake_case_								=			replace(args, **wandb_args)
				snake_case_								=			args.base_dir + """-""" + wandb.run.id
				snake_case_								=			replace(args, base_dir=base_dir)
				print(args)

				snake_case_								=			load_dataset("""json""", data_files=args.tr_data_path)["""train"""]
				snake_case_								=			load_dataset("""json""", data_files=args.val_data_path)["""train"""]

				# drop extra batch for now
				snake_case_								=			range(len(tr_dataset) - len(tr_dataset) % args.batch_size)
				snake_case_								=			tr_dataset.shuffle().select(indices)
				snake_case_								=			range(len(val_dataset) - len(val_dataset) % args.batch_size)
				snake_case_								=			val_dataset.shuffle().select(indices)

				if os.environ.get("""TRAIN_ON_SMALL""", """false""") == "true":
								snake_case_								=			tr_dataset.shuffle().select(range(8_0000))
								snake_case_								=			val_dataset.shuffle().select(range(8000))

				print(tr_dataset)
				print(val_dataset)

				snake_case_								=			FlaxBigBirdForNaturalQuestions.from_pretrained(
				    args.model_id, block_size=args.block_size, num_random_blocks=args.num_random_blocks
				)
				snake_case_								=			BigBirdTokenizerFast.from_pretrained(args.model_id)
				snake_case_								=			DataCollator(pad_id=tokenizer.pad_token_id, max_length=4096)

				snake_case_								=			{
				    """lr""": args.lr,
				    """init_lr""": args.init_lr,
				    """warmup_steps""": args.warmup_steps,
				    """num_train_steps""": args.max_epochs * (len(tr_dataset) // args.batch_size),
				    """weight_decay""": args.weight_decay,
				}
				snake_case_	,  snake_case_								=			build_tx(**tx_args)

				snake_case_								=			Trainer(
				    args=args,
				    data_collator=data_collator,
				    model_save_fn=model.save_pretrained,
				    train_step_fn=train_step,
				    val_step_fn=val_step,
				    logger=logger,
				    scheduler_fn=lr,
				)

				snake_case_								=			None
				snake_case_								=			trainer.create_state(model, tx, num_train_steps=tx_args["""num_train_steps"""], ckpt_dir=ckpt_dir)
				try:
								trainer.train(state, tr_dataset, val_dataset)
				except KeyboardInterrupt:
								print("""Oooops; TRAINING STOPPED UNFORTUNATELY""")

				print("""SAVING WEIGHTS IN `final-weights`""")
				snake_case_								=			jax_utils.unreplicate(state.params)
				model.save_pretrained(os.path.join(args.base_dir, """final-weights"""), params=params)





