code: 
  
  
  import importlib.util
  import weakref
  from functools import partialmethod
  
  from .dependency_versions_check import dep_version_check
  from .utils import is_accelerate_available, is_torch_available, logging
  
  
  if is_torch_available():
    import torch
  
  __a   				=      logging.get_logger(__name__)
  
  
  
  
  def 			__lowercase	(						)    ->Optional[int]:
  
  
  
  
  
  
  
        """simple docstring"""
  
        return importlib.util.find_spec('''deepspeed'''     ) is not None
  
  
  if is_accelerate_available() and is_deepspeed_available():
    from accelerate.utils.deepspeed import HfDeepSpeedConfig as DeepSpeedConfig
  else:
    # Inherits from a dummy `object` if accelerate is not available, so that python succeeds to import this file.
    # Deepspeed glue code will never inherit this dummy object as it checks if accelerate is available.
    from builtins import object as DeepSpeedConfig
  
  
  
  
  
  
  
  class 				__SCREAMING_SNAKE_CASE   (			A__   ):
  
  
        def __init__(							self							,						SCREAMING_SNAKE_CASE__		):
              # set global weakref object
              set_hf_deepspeed_config(self		)
              dep_version_check('''accelerate'''		)
              dep_version_check('''deepspeed'''		)
              super().__init__(SCREAMING_SNAKE_CASE__		)
  
  
  
  
  
  
  
  class 				__SCREAMING_SNAKE_CASE   (			A__   ):
  
  
        def __init__(							self							,						SCREAMING_SNAKE_CASE__		):
              super().__init__(SCREAMING_SNAKE_CASE__		)
              lowercase			:   Union[str, Any]					   =		None
              lowercase			:   List[str]					   =		[]
  
  
        def        __lowerCamelCase			(							self		):
              if self._dtype is None:
                    raise ValueError('''trainer_config_process() wasn\'t called yet to tell dtype'''		)
              return self._dtype
  
  
        def        __lowerCamelCase			(							self							,						SCREAMING_SNAKE_CASE__		):
              lowercase			:   int					   =		self.get_value(SCREAMING_SNAKE_CASE__		)
              if val is None:
                    return False
              else:
                    return val == "auto"
  
  
        def        __lowerCamelCase			(							self							,						SCREAMING_SNAKE_CASE__							,						SCREAMING_SNAKE_CASE__							,						SCREAMING_SNAKE_CASE__=None							,						SCREAMING_SNAKE_CASE__=True		):
              lowercase   ,			lowercase			:   str					   =		self.find_config_node(SCREAMING_SNAKE_CASE__		)
              if config is None:
                    return
  
              if config.get(SCREAMING_SNAKE_CASE__		) == "auto":
                    lowercase			:   Union[str, Any]					   =		hf_val
                    return
  
              if not must_match:
                    return
  
              lowercase			:   int					   =		config.get(SCREAMING_SNAKE_CASE__		)
              if ds_val is not None and ds_val != hf_val:
                    self.mismatches.append(f"""- ds {ds_key_long}={ds_val} vs hf {hf_key}={hf_val}"""		)
  
        A				:							Tuple    							=     partialmethod(A__  ,						must_match=A__   )
  
  
        def        __lowerCamelCase			(							self							,						SCREAMING_SNAKE_CASE__		):
              lowercase			:   Tuple					   =		args.world_size * args.per_device_train_batch_size * args.gradient_accumulation_steps
              self.fill_match(
                  '''train_micro_batch_size_per_gpu'''							,						args.per_device_train_batch_size							,						'''per_device_train_batch_size'''		)
              self.fill_match('''gradient_accumulation_steps'''							,						args.gradient_accumulation_steps							,						'''gradient_accumulation_steps'''		)
              self.fill_match('''train_batch_size'''							,						SCREAMING_SNAKE_CASE__							,						'''train_batch_size (calculated)'''		)
              self.fill_match('''gradient_clipping'''							,						args.max_grad_norm							,						'''max_grad_norm'''		)
  
              self.fill_match('''optimizer.params.lr'''							,						args.learning_rate							,						'''learning_rate'''		)
              self.fill_match('''optimizer.params.betas'''							,						[args.adam_betaa, args.adam_betaa]							,						'''adam_beta1+adam_beta2'''		)
              self.fill_match('''optimizer.params.eps'''							,						args.adam_epsilon							,						'''adam_epsilon'''		)
              self.fill_match('''optimizer.params.weight_decay'''							,						args.weight_decay							,						'''weight_decay'''		)
  
              self.fill_only('''scheduler.params.warmup_min_lr'''							,						0		)  # not a trainer arg
              self.fill_match('''scheduler.params.warmup_max_lr'''							,						args.learning_rate							,						'''learning_rate'''		)
              # total_num_steps - will get set in trainer_config_finalize
  
              # fp16
              if args.fpaa or args.fpaa_full_eval:
                    lowercase			:   Dict					   =		'''apex''' if args.fpaa_backend == '''apex''' else '''amp'''
              else:
                    lowercase			:   Dict					   =		None
  
              if args.save_on_each_node:
                    # deepspeed uses shared storage by default. Let's override this setting if save_on_each_node == True
                    lowercase			:   int					   =		self.config.get('''checkpoint'''							,						{}		)
                    lowercase			:   Union[str, Any]					   =		args.save_on_each_node
  
              # amp: similar to the pytorch native amp - it has a bunch of optional params but we won't set
              # any here unless the user did the work
              self.fill_match(
                  '''fp16.enabled'''							,						((args.fpaa or args.fpaa_full_eval) and fpaa_backend == '''amp''')							,						'''fp16|fp16_full_eval+fp16_backend(amp)'''							,						)
  
              # apex: delegates amp work to apex (which needs to be available), but it cannot be used with any
              # ZeRO features
              self.fill_match('''amp.enabled'''							,						fpaa_backend == '''apex'''							,						'''fp16+fp16_backend(apex)'''		)
              self.fill_match('''amp.opt_level'''							,						args.fpaa_opt_level							,						'''fp16_opt_level'''		)
  
              self.fill_match('''bf16.enabled'''							,						(args.bfaa or args.bfaa_full_eval)							,						'''bf16|bf16_full_eval'''		)
  
              # deepspeed's default mode is fp16 unless there is a config that says differently
              if self.is_true('''bf16.enabled'''		):
                    lowercase			:   str					   =		torch.bfloataa
              elif self.is_false('''fp16.enabled'''		):
                    lowercase			:   str					   =		torch.floataa
              else:
                    lowercase			:   List[Any]					   =		torch.floataa
  
  
  
  
  
  
        def        __lowerCamelCase			(							self							,						SCREAMING_SNAKE_CASE__							,						SCREAMING_SNAKE_CASE__							,						SCREAMING_SNAKE_CASE__		):
              lowercase			:   Optional[int]					   =		[
                  '''zero_optimization.reduce_bucket_size''',
                  '''zero_optimization.stage3_prefetch_bucket_size''',
                  '''zero_optimization.stage3_param_persistence_threshold''',
              ]
              lowercase			:   Optional[int]					   =		[x for x in hidden_size_based_keys if self.is_auto(SCREAMING_SNAKE_CASE__		)]
  
              if len(SCREAMING_SNAKE_CASE__		) > 0:
                    if hasattr(model.config							,						'''hidden_size'''		):
                          lowercase			:   int					   =		model.config.hidden_size
                    elif hasattr(model.config							,						'''hidden_sizes'''		):
                          # if there are many hidden sizes pick the largest one
                          lowercase			:   Tuple					   =		max(model.config.hidden_sizes		)
                    else:
                          raise ValueError(
                              '''The model\'s config file has neither `hidden_size` nor `hidden_sizes` entry, '''
                              '''therefore it\'s not possible to automatically fill out the following `auto` entries '''
                              f"""in the DeepSpeed config file: {hidden_size_auto_keys}. You can fix that by replacing """
                              '''`auto` values for these keys with an integer value of your choice.'''		)
  
                    self.fill_only('''zero_optimization.reduce_bucket_size'''							,						hidden_size * hidden_size		)
                    if self.is_zeroa():
                          # automatically assign the optimal config values based on model config
                          self.fill_only('''zero_optimization.stage3_prefetch_bucket_size'''							,						0.9 * hidden_size * hidden_size		)
                          self.fill_only('''zero_optimization.stage3_param_persistence_threshold'''							,						10 * hidden_size		)
  
          # scheduler
              self.fill_match('''scheduler.params.total_num_steps'''							,						SCREAMING_SNAKE_CASE__							,						'''num_training_steps (calculated)'''		)
              self.fill_match('''scheduler.params.warmup_num_steps'''							,						args.get_warmup_steps(SCREAMING_SNAKE_CASE__		)							,						'''warmup_steps'''		)
  
              if len(self.mismatches		) > 0:
                    lowercase			:   Any					   =		'''\n'''.join(self.mismatches		)
                    raise ValueError(
                        '''Please correct the following DeepSpeed config values that mismatch TrainingArguments'''
                        f""" values:\n{mismatches}\nThe easiest method is to set these DeepSpeed config values to 'auto'."""		)
  
  
  
  
  
  __a   				=      None
  
  
  
  
  def 			__lowercase	(						_UpperCamelCase     )    ->Union[str, Any]:
  
  
  
  
  
  
  
        """simple docstring"""
  
        global _hf_deepspeed_config_weak_ref
        # will go away automatically when HfDeepSpeedConfig is destroyed (when TrainingArguments is destroyed)
        lowercase			:   Optional[Any]					   =		weakref.ref(_UpperCamelCase     )
  
  
  
  
  def 			__lowercase	(						)    ->str:
  
  
  
  
  
  
  
        """simple docstring"""
  
        global _hf_deepspeed_config_weak_ref
        lowercase			:   int					   =		None
  
  
  
  
  def 			__lowercase	(						)    ->Tuple:
  
  
  
  
  
  
  
        """simple docstring"""
  
        if _hf_deepspeed_config_weak_ref is not None and _hf_deepspeed_config_weak_ref() is not None:
              return _hf_deepspeed_config_weak_ref().is_zeroa()
        else:
              return False
  
  
  
  
  def 			__lowercase	(						)    ->Dict:
  
  
  
  
  
  
  
        """simple docstring"""
  
        if _hf_deepspeed_config_weak_ref is not None and _hf_deepspeed_config_weak_ref() is not None:
              return _hf_deepspeed_config_weak_ref().config
        else:
              return None
  
  
  
  
  def 			__lowercase	(						_UpperCamelCase,    _UpperCamelCase,    _UpperCamelCase,    _UpperCamelCase,    _UpperCamelCase     )    ->Tuple:
  
  
  
  
  
  
  
        """simple docstring"""
  
        from accelerate.utils import DummyOptim, DummyScheduler
  
        lowercase			:   int					   =		hf_deepspeed_config.config
  
        # Optimizer + Scheduler
        # Currently supported combos:
        # 1. DS scheduler + DS optimizer: Yes
        # 2. HF scheduler + HF optimizer: Yes
        # 3. DS scheduler + HF optimizer: Yes
        # 4. HF scheduler + DS optimizer: No
        #
        # Unless Offload is enabled in which case it's:
        # 1. DS scheduler + DS optimizer: Yes
        # 2. HF scheduler + HF optimizer: Mostly*
        # 3. DS scheduler + HF optimizer: Mostly*
        # 4. HF scheduler + DS optimizer: No
        #
        # Mostly*: All non-native DeepSpeed optimizers that have both CPU and GPU implementation should work (except LAMB)
  
        lowercase			:   List[str]					   =		None
        if "optimizer" in config:
              if args.adafactor:
                    raise ValueError(
                        '''--adafactor was passed, but also found `optimizer` configured in the DeepSpeed config. '''
                        '''Only one optimizer can be configured.'''     )
              lowercase			:   Tuple					   =		DummyOptim(params=_UpperCamelCase     )
        else:
              if hf_deepspeed_config.is_offload():
                    logger.info(
                        '''Detected ZeRO Offload and non-DeepSpeed optimizers: This combination should work as long as the'''
                        ''' custom optimizer has both CPU and GPU implementation (except LAMB)'''     )
  
              # ds supports Adam, OneBitAdam, and Lamb optimizers and can import other optimizers from torch.
              # But trainer uses AdamW by default.
              lowercase			:   List[str]					   =		trainer.create_optimizer()
              # To use other optimizers requires voiding warranty with: `zero_allow_untested_optimizer`
              lowercase			:   Any					   =		True
  
        lowercase			:   str					   =		None
        if "scheduler" in config:
              lowercase			:   Optional[Any]					   =		DummyScheduler(_UpperCamelCase     )
        else:
              if isinstance(_UpperCamelCase,    _UpperCamelCase     ):
                    raise ValueError(
                        '''Found `optimizer` configured in the DeepSpeed config, but no `scheduler`. '''
                        '''Please configure a scheduler in the DeepSpeed config.'''     )
              lowercase			:   int					   =		trainer.create_scheduler(num_training_steps=_UpperCamelCase,    optimizer=_UpperCamelCase     )
  
        return optimizer, lr_scheduler
  
  
  
  
  def 			__lowercase	(						_UpperCamelCase,    _UpperCamelCase,    _UpperCamelCase=False     )    ->Optional[int]:
  
  
  
  
  
  
  
        """simple docstring"""
  
        from deepspeed.utils import logger as ds_logger
  
        lowercase			:   Tuple					   =		trainer.model
        lowercase			:   int					   =		trainer.args
  
        lowercase			:   Optional[int]					   =		trainer.accelerator.state.deepspeed_plugin.hf_ds_config
  
        # resume config update - some bits like `model` and `num_training_steps` only become available during train
        hf_deepspeed_config.trainer_config_finalize(_UpperCamelCase,    _UpperCamelCase,    _UpperCamelCase     )
  
        # set the Deepspeed log level consistent with the Trainer
        ds_logger.setLevel(args.get_process_log_level()     )
  
        if inference:
              # only Z3 makes sense for the inference
              if not hf_deepspeed_config.is_zeroa():
                    raise ValueError('''ZeRO inference only makes sense with ZeRO Stage 3 - please adjust your config'''     )
  
              # in case the training config is re-used for inference
              hf_deepspeed_config.del_config_sub_tree('''optimizer'''     )
              hf_deepspeed_config.del_config_sub_tree('''lr_scheduler'''     )
              lowercase   ,			lowercase			:   Union[str, Any]					   =		None, None
              lowercase			:   Optional[int]					   =		None
        else:
              lowercase			:   Optional[int]					   =		None  # important for when deepspeed_init is used as re-init
              lowercase			:   List[Any]					   =		list(filter(lambda _UpperCamelCase     : p.requires_grad,    model.parameters()     )     )
              lowercase   ,			lowercase			:   List[Any]					   =		deepspeed_optim_sched(
                  _UpperCamelCase,    _UpperCamelCase,    _UpperCamelCase,    _UpperCamelCase,    _UpperCamelCase     )
  
        # keep for quick debug:
        # from pprint import pprint; pprint(config)
  
        return optimizer, lr_scheduler
  
  
  
  
  def 			__lowercase	(						_UpperCamelCase,    _UpperCamelCase     )    ->List[Any]:
  
  
  
  
  
  
  
        """simple docstring"""
  
        import glob
  
        lowercase			:   Dict					   =		sorted(glob.glob(f"""{checkpoint_path}/global_step*"""     )     )
  
        if len(_UpperCamelCase     ) > 0:
              logger.info(f"""Attempting to resume from {checkpoint_path}"""     )
              # this magically updates self.optimizer and self.lr_scheduler
              lowercase   ,			lowercase			:   Tuple					   =		deepspeed_engine.load_checkpoint(
                  _UpperCamelCase,    load_optimizer_states=_UpperCamelCase,    load_lr_scheduler_states=_UpperCamelCase     )
              if load_path is None:
                    raise ValueError(f"""[deepspeed] failed to resume from checkpoint {checkpoint_path}"""     )
        else:
              raise ValueError(f"""Can't find a valid checkpoint at {checkpoint_path}"""     )
  
  
style_context: 
  
  
  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   				=      logging.get_logger(__name__)
  
  
  
  
  def 			__lowercase	(						_UpperCamelCase     )    ->Tuple:
  
  
  
  
  
  
  
        """simple docstring"""
  
        lowercase			:   Optional[int]					   =		tf.image.resize(_UpperCamelCase,    (346, 346)     )
        lowercase			:   Any					   =		tf.image.crop_to_bounding_box(_UpperCamelCase,    (346 - 289) // 2,    (346 - 289) // 2,    289,    289     )
        return image
  
  
  
  
  def 			__lowercase	(						)    ->List[str]:
  
  
  
  
  
  
  
        """simple docstring"""
  
        lowercase			:   Tuple					   =		EfficientNetConfig.from_pretrained('''google/efficientnet-b7'''     )
        lowercase			:   Optional[int]					   =		289
        lowercase			:   Optional[Any]					   =		640
        lowercase			:   Any					   =		{'''0''': '''LABEL_0''', '''1''': '''LABEL_1'''}
        lowercase			:   Optional[Any]					   =		{'''LABEL_0''': 0, '''LABEL_1''': 1}
        lowercase			:   Union[str, Any]					   =		[]
  
        lowercase			:   Union[str, Any]					   =		BertConfig()
        lowercase			:   Any					   =		AlignConfig.from_text_vision_configs(
            text_config=_UpperCamelCase,    vision_config=_UpperCamelCase,    projection_dim=640     )
        return config
  
  
  
  
  def 			__lowercase	(						)    ->Union[str, Any]:
  
  
  
  
  
  
  
        """simple docstring"""
  
        lowercase			:   int					   =		'''http://images.cocodataset.org/val2017/000000039769.jpg'''
        lowercase			:   int					   =		Image.open(requests.get(_UpperCamelCase,    stream=_UpperCamelCase     ).raw     )
        return im
  
  
  
  
  def 			__lowercase	(						)    ->Optional[int]:
  
  
  
  
  
  
  
        """simple docstring"""
  
        lowercase			:   Any					   =		EfficientNetImageProcessor(
            do_center_crop=_UpperCamelCase,    rescale_factor=1 / 1_2_7.5,    rescale_offset=_UpperCamelCase,    do_normalize=_UpperCamelCase,    include_top=_UpperCamelCase,    resample=Image.BILINEAR,    )
        lowercase			:   Any					   =		BertTokenizer.from_pretrained('''bert-base-uncased'''     )
        lowercase			:   int					   =		64
        lowercase			:   List[Any]					   =		AlignProcessor(image_processor=_UpperCamelCase,    tokenizer=_UpperCamelCase     )
        return processor
  
  
  
  
  def 			__lowercase	(						_UpperCamelCase     )    ->int:
  
  
  
  
  
  
  
        """simple docstring"""
  
        lowercase			:   str					   =		[v.split('''_'''     )[0].split('''block'''     )[1] for v in original_param_names if v.startswith('''block'''     )]
        lowercase			:   str					   =		list(set(_UpperCamelCase     )     )
        lowercase			:   List[str]					   =		sorted(_UpperCamelCase     )
        lowercase			:   str					   =		len(_UpperCamelCase     )
        lowercase			:   Dict					   =		{b: str(_UpperCamelCase     ) for b, i in zip(_UpperCamelCase,    range(_UpperCamelCase     )     )}
  
        lowercase			:   int					   =		[]
        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:
              lowercase			:   Dict					   =		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""")     )
  
        lowercase			:   Union[str, Any]					   =		{}
        for item in rename_keys:
              if item[0] in original_param_names:
                    lowercase			:   Dict					   =		'''vision_model.''' + item[1]
  
      # BERT text encoder
        lowercase			:   str					   =		[]
        lowercase			:   Optional[Any]					   =		'''tf_bert_model/bert'''
        lowercase			:   str					   =		'''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:
                    lowercase			:   Tuple					   =		item[1]
        return key_mapping
  
  
  
  
  def 			__lowercase	(						_UpperCamelCase,    _UpperCamelCase,    _UpperCamelCase     )    ->Any:
  
  
  
  
  
  
  
        """simple docstring"""
  
        list(hf_params.keys()     )
  
        for key, value in tf_params.items():
              if key not in key_mapping:
                    continue
  
              lowercase			:   List[str]					   =		key_mapping[key]
              if "_conv" in key and "kernel" in key:
                    lowercase			:   List[Any]					   =		torch.from_numpy(_UpperCamelCase     ).permute(3,    2,    0,    1     )
              elif "embeddings" in key:
                    lowercase			:   int					   =		torch.from_numpy(_UpperCamelCase     )
              elif "depthwise_kernel" in key:
                    lowercase			:   Union[str, Any]					   =		torch.from_numpy(_UpperCamelCase     ).permute(2,    3,    0,    1     )
              elif "kernel" in key:
                    lowercase			:   int					   =		torch.from_numpy(np.transpose(_UpperCamelCase     )     )
              elif "temperature" in key:
                    lowercase			:   Optional[int]					   =		value
              elif "bn/gamma" or "bn/beta" in key:
                    lowercase			:   Dict					   =		torch.from_numpy(np.transpose(_UpperCamelCase     )     ).squeeze()
              else:
                    lowercase			:   Union[str, Any]					   =		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     )    ->Dict:
  
  
  
  
  
  
  
        """simple docstring"""
  
        lowercase			:   str					   =		64
        lowercase			:   str					   =		Tokenizer(_UpperCamelCase     )
        lowercase			:   Optional[int]					   =		align.Align('''efficientnet-b7''',    '''bert-base''',    640,    _UpperCamelCase,    tok.get_vocab_size()     )
        original_model.compile()
        original_model.load_weights(_UpperCamelCase     )
  
        lowercase			:   Optional[Any]					   =		original_model.trainable_variables
        lowercase			:   Tuple					   =		original_model.non_trainable_variables
        lowercase			:   Optional[int]					   =		{param.name: param.numpy() for param in tf_params}
        for param in tf_non_train_params:
              lowercase			:   List[str]					   =		param.numpy()
        lowercase			:   str					   =		list(tf_params.keys()     )
  
        # Load HuggingFace model
        lowercase			:   Tuple					   =		get_align_config()
        lowercase			:   List[Any]					   =		AlignModel(_UpperCamelCase     ).eval()
        lowercase			:   Union[str, Any]					   =		hf_model.state_dict()
  
        # Create src-to-dst parameter name mapping dictionary
        print('''Converting parameters...'''     )
        lowercase			:   List[str]					   =		rename_keys(_UpperCamelCase     )
        replace_params(_UpperCamelCase,    _UpperCamelCase,    _UpperCamelCase     )
  
        # Initialize processor
        lowercase			:   Union[str, Any]					   =		get_processor()
        lowercase			:   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():
              lowercase			:   Dict					   =		hf_model(**_UpperCamelCase     )
  
        lowercase			:   int					   =		outputs.image_embeds.detach().numpy()
        lowercase			:   Any					   =		outputs.text_embeds.detach().numpy()
  
        # Original model inference
        lowercase			:   Any					   =		False
        lowercase			:   Tuple					   =		EfficientNetImageProcessor(
            do_center_crop=_UpperCamelCase,    do_rescale=_UpperCamelCase,    do_normalize=_UpperCamelCase,    include_top=_UpperCamelCase,    resample=Image.BILINEAR,    )
        lowercase			:   str					   =		tf_image_processor(images=prepare_img(),    return_tensors='''tf''',    data_format='''channels_last'''     )['''pixel_values''']
        lowercase			:   int					   =		tok(tf.constant(['''A picture of a cat''']     )     )
  
        lowercase			:   Optional[int]					   =		original_model.image_encoder(_UpperCamelCase,    training=_UpperCamelCase     )
        lowercase			:   Optional[int]					   =		original_model.text_encoder(_UpperCamelCase,    training=_UpperCamelCase     )
  
        lowercase			:   Union[str, Any]					   =		tf.nn.la_normalize(_UpperCamelCase,    axis=-1     )
        lowercase			:   List[str]					   =		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__":
    __a   				=      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   				=      parser.parse_args()
    convert_align_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.save_model, args.push_to_hub)
  
  