






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		:	str      					=			logging.get_logger(__name__)






def     lowerCamelCase__				(   __lowerCamelCase      :					Optional[int]		):
  __UpperCAmelCase							:     List[str]									=  tf.image.resize(__lowerCamelCase  ,		(346, 346)		)
  __UpperCAmelCase							:     Tuple									=  tf.image.crop_to_bounding_box(__lowerCamelCase  ,		(346 - 289) // 2  ,		(346 - 289) // 2  ,		289  ,		289		)
  return image






def     lowerCamelCase__				(   ):
  __UpperCAmelCase							:     Any									=  EfficientNetConfig.from_pretrained("""google/efficientnet-b7"""		)
  __UpperCAmelCase							:     int									=  289
  __UpperCAmelCase							:     Union[str, Any]									=  640
  __UpperCAmelCase							:     Dict									=  {"""0""": """LABEL_0""", """1""": """LABEL_1"""}
  __UpperCAmelCase							:     Dict									=  {"""LABEL_0""": 0, """LABEL_1""": 1}
  __UpperCAmelCase							:     Tuple									=  []

  __UpperCAmelCase							:     str									=  BertConfig()
  __UpperCAmelCase							:     Tuple									=  AlignConfig.from_text_vision_configs(
      text_config=__lowerCamelCase  ,		vision_config=__lowerCamelCase  ,		projection_dim=640		)
  return config






def     lowerCamelCase__				(   ):
  __UpperCAmelCase							:     List[Any]									=  """http://images.cocodataset.org/val2017/000000039769.jpg"""
  __UpperCAmelCase							:     List[str]									=  Image.open(requests.get(__lowerCamelCase  ,		stream=__lowerCamelCase		).raw		)
  return im






def     lowerCamelCase__				(   ):
  __UpperCAmelCase							:     Optional[int]									=  EfficientNetImageProcessor(
      do_center_crop=__lowerCamelCase  ,		rescale_factor=1 / 1_2_7.5  ,		rescale_offset=__lowerCamelCase  ,		do_normalize=__lowerCamelCase  ,		include_top=__lowerCamelCase  ,		resample=Image.BILINEAR  ,		)
  __UpperCAmelCase							:     List[str]									=  BertTokenizer.from_pretrained("""bert-base-uncased"""		)
  __UpperCAmelCase							:     Any									=  64
  __UpperCAmelCase							:     str									=  AlignProcessor(image_processor=__lowerCamelCase  ,		tokenizer=__lowerCamelCase		)
  return processor






def     lowerCamelCase__				(   __lowerCamelCase      :					Union[str, Any]		):
  # EfficientNet image encoder
  __UpperCAmelCase							:     Optional[Any]									=  [v.split("""_"""		)[0].split("""block"""		)[1] for v in original_param_names if v.startswith("""block"""		)]
  __UpperCAmelCase							:     List[Any]									=  list(set(__lowerCamelCase		)		)
  __UpperCAmelCase							:     Optional[int]									=  sorted(__lowerCamelCase		)
  __UpperCAmelCase							:     List[Any]									=  len(__lowerCamelCase		)
  __UpperCAmelCase							:     Dict									=  {b: str(__lowerCamelCase		) for b, i in zip(__lowerCamelCase  ,		range(__lowerCamelCase		)		)}

  __UpperCAmelCase							:     Optional[Any]									=  []
  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							:     int									=  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							:     List[Any]									=  {}
  for item in rename_keys:
    if item[0] in original_param_names:
      __UpperCAmelCase							:     Any									=  """vision_model.""" + item[1]

    # BERT text encoder
  __UpperCAmelCase							:     Tuple									=  []
  __UpperCAmelCase							:     List[Any]									=  """tf_bert_model/bert"""
  __UpperCAmelCase							:     List[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:
      __UpperCAmelCase							:     str									=  item[1]
  return key_mapping






def     lowerCamelCase__				(   __lowerCamelCase      :					Optional[Any]  ,		__lowerCamelCase      :					Optional[int]  ,		__lowerCamelCase      :					Any		):
  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							:     List[Any]									=  torch.from_numpy(__lowerCamelCase		).permute(3  ,		2  ,		0  ,		1		)
    elif "embeddings" in key:
      __UpperCAmelCase							:     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							:     str									=  torch.from_numpy(np.transpose(__lowerCamelCase		)		)
    elif "temperature" in key:
      __UpperCAmelCase							:     List[Any]									=  value
    elif "bn/gamma" or "bn/beta" in key:
      __UpperCAmelCase							:     str									=  torch.from_numpy(np.transpose(__lowerCamelCase		)		).squeeze()
    else:
      __UpperCAmelCase							:     Any									=  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[str]  ,		__lowerCamelCase      :					Optional[Any]  ,		__lowerCamelCase      :					List[Any]  ,		__lowerCamelCase      :					int		):
  __UpperCAmelCase							:     str									=  64
  __UpperCAmelCase							:     Optional[Any]									=  Tokenizer(__lowerCamelCase		)
  __UpperCAmelCase							:     str									=  align.Align("""efficientnet-b7"""  ,		"""bert-base"""  ,		640  ,		__lowerCamelCase  ,		tok.get_vocab_size()		)
  original_model.compile()
  original_model.load_weights(__lowerCamelCase		)

  __UpperCAmelCase							:     Dict									=  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							:     Optional[Any]									=  param.numpy()
  __UpperCAmelCase							:     Tuple									=  list(tf_params.keys()		)

  # Load HuggingFace model
  __UpperCAmelCase							:     int									=  get_align_config()
  __UpperCAmelCase							:     str									=  AlignModel(__lowerCamelCase		).eval()
  __UpperCAmelCase							:     Union[str, Any]									=  hf_model.state_dict()

  # Create src-to-dst parameter name mapping dictionary
  print("""Converting parameters..."""		)
  __UpperCAmelCase							:     List[Any]									=  rename_keys(__lowerCamelCase		)
  replace_params(__lowerCamelCase  ,		__lowerCamelCase  ,		__lowerCamelCase		)

  # Initialize processor
  __UpperCAmelCase							:     Optional[Any]									=  get_processor()
  __UpperCAmelCase							:     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							:     List[str]									=  hf_model(**__lowerCamelCase		)

  __UpperCAmelCase							:     Optional[Any]									=  outputs.image_embeds.detach().numpy()
  __UpperCAmelCase							:     Dict									=  outputs.text_embeds.detach().numpy()

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

  __UpperCAmelCase							:     Union[str, Any]									=  original_model.image_encoder(__lowerCamelCase  ,		training=__lowerCamelCase		)
  __UpperCAmelCase							:     Tuple									=  original_model.text_encoder(__lowerCamelCase  ,		training=__lowerCamelCase		)

  __UpperCAmelCase							:     Tuple									=  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__":
       a		:	List[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")

       a		:	List[Any]      					=			parser.parse_args()
       convert_align_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.save_model, args.push_to_hub)










import argparse
import logging
import os
from pathlib import Path
from typing import Any, Dict

import pytorch_lightning as pl
from pytorch_lightning.utilities import rank_zero_info

from transformers import (
    AdamW,
    AutoConfig,
    AutoModel,
    AutoModelForPreTraining,
    AutoModelForQuestionAnswering,
    AutoModelForSeqaSeqLM,
    AutoModelForSequenceClassification,
    AutoModelForTokenClassification,
    AutoModelWithLMHead,
    AutoTokenizer,
    PretrainedConfig,
    PreTrainedTokenizer,
)
from transformers.optimization import (
    Adafactor,
    get_cosine_schedule_with_warmup,
    get_cosine_with_hard_restarts_schedule_with_warmup,
    get_linear_schedule_with_warmup,
    get_polynomial_decay_schedule_with_warmup,
)
from transformers.utils.versions import require_version


a		:	str      					=			logging.getLogger(__name__)

require_version("pytorch_lightning>=1.0.4")

a		:	Any      					=			{
    "base": AutoModel,
    "sequence-classification": AutoModelForSequenceClassification,
    "question-answering": AutoModelForQuestionAnswering,
    "pretraining": AutoModelForPreTraining,
    "token-classification": AutoModelForTokenClassification,
    "language-modeling": AutoModelWithLMHead,
    "summarization": AutoModelForSeqaSeqLM,
    "translation": AutoModelForSeqaSeqLM,
}


# update this and the import above to support new schedulers from transformers.optimization
a		:	List[str]      					=			{
    "linear": get_linear_schedule_with_warmup,
    "cosine": get_cosine_schedule_with_warmup,
    "cosine_w_restarts": get_cosine_with_hard_restarts_schedule_with_warmup,
    "polynomial": get_polynomial_decay_schedule_with_warmup,
    # '': get_constant_schedule,             # not supported for now
    # '': get_constant_schedule_with_warmup, # not supported for now
}
a		:	int      					=			sorted(arg_to_scheduler.keys())
a		:	Dict      					=			"{" + ", ".join(arg_to_scheduler_choices) + "}"
class    a   (			pl.LightningModule       ):



      """simple docstring"""


      def __init__(				self :      Union[str, Any]		,				__lowercase :      argparse.Namespace		,				__lowercase :      int=None		,				__lowercase :      Any="base"		,				__lowercase :      str=None		,				__lowercase :      Dict=None		,				__lowercase :      Optional[Any]=None		,				**__lowercase :      Optional[Any]		,				)							->				List[str]:
        super().__init__()
        # TODO: move to self.save_hyperparameters()
        # self.save_hyperparameters()
        # can also expand arguments into trainer signature for easier reading

        self.save_hyperparameters(__lowercase )
        __UpperCAmelCase							:     Any									=  0
        __UpperCAmelCase							:     List[Any]									=  Path(self.hparams.output_dir )
        __UpperCAmelCase							:     int									=  self.hparams.cache_dir if self.hparams.cache_dir else None
        if config is None:
          __UpperCAmelCase							:     str									=  AutoConfig.from_pretrained(
              self.hparams.config_name if self.hparams.config_name else self.hparams.model_name_or_path		,				**({"""num_labels""": num_labels} if num_labels is not None else {})		,				cache_dir=__lowercase		,				**__lowercase		,				)
        else:
          __UpperCAmelCase							:     PretrainedConfig									=  config

        __UpperCAmelCase							:     Optional[int]									=  ("""encoder_layerdrop""", """decoder_layerdrop""", """dropout""", """attention_dropout""")
        for p in extra_model_params:
          if getattr(self.hparams		,				__lowercase		,				__lowercase ):
            assert hasattr(self.config		,				__lowercase ), f"""model config doesn't have a `{p}` attribute"""
            setattr(self.config		,				__lowercase		,				getattr(self.hparams		,				__lowercase ) )

        if tokenizer is None:
          __UpperCAmelCase							:     Tuple									=  AutoTokenizer.from_pretrained(
              self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path		,				cache_dir=__lowercase		,				)
        else:
          __UpperCAmelCase							:     PreTrainedTokenizer									=  tokenizer
        __UpperCAmelCase							:     Dict									=  MODEL_MODES[mode]
        if model is None:
          __UpperCAmelCase							:     Dict									=  self.model_type.from_pretrained(
              self.hparams.model_name_or_path		,				from_tf=bool(""".ckpt""" in self.hparams.model_name_or_path )		,				config=self.config		,				cache_dir=__lowercase		,				)
        else:
          __UpperCAmelCase							:     Any									=  model
      def      UpperCAmelCase				(				self :      Optional[Any]		,				*__lowercase :      Optional[Any]		,				**__lowercase :      Optional[Any] )							->				List[Any]:
        __UpperCAmelCase							:     List[Any]									=  self.model_type.from_pretrained(*__lowercase		,				**__lowercase )
      def      UpperCAmelCase				(				self :      Union[str, Any] )							->				Dict:
        __UpperCAmelCase							:     Union[str, Any]									=  arg_to_scheduler[self.hparams.lr_scheduler]
        __UpperCAmelCase							:     Optional[int]									=  get_schedule_func(
            self.opt		,				num_warmup_steps=self.hparams.warmup_steps		,				num_training_steps=self.total_steps() )
        __UpperCAmelCase							:     int									=  {"""scheduler""": scheduler, """interval""": """step""", """frequency""": 1}
        return scheduler
      def      UpperCAmelCase				(				self :      Optional[int] )							->				int:
        __UpperCAmelCase							:     List[str]									=  self.model
        __UpperCAmelCase							:     List[str]									=  ["""bias""", """LayerNorm.weight"""]
        __UpperCAmelCase							:     List[str]									=  [
            {
                """params""": [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay )],
                """weight_decay""": self.hparams.weight_decay,
            },
            {
                """params""": [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay )],
                """weight_decay""": 0.0,
            },
        ]
        if self.hparams.adafactor:
          __UpperCAmelCase							:     List[Any]									=  Adafactor(
              __lowercase		,				lr=self.hparams.learning_rate		,				scale_parameter=__lowercase		,				relative_step=__lowercase )

        else:
          __UpperCAmelCase							:     Optional[int]									=  AdamW(
              __lowercase		,				lr=self.hparams.learning_rate		,				eps=self.hparams.adam_epsilon )
        __UpperCAmelCase							:     Dict									=  optimizer

        __UpperCAmelCase							:     Dict									=  self.get_lr_scheduler()

        return [optimizer], [scheduler]
      def      UpperCAmelCase				(				self :      Any		,				__lowercase :      List[Any]		,				__lowercase :      Tuple )							->				List[Any]:
        return self.validation_step(__lowercase		,				__lowercase )
      def      UpperCAmelCase				(				self :      Union[str, Any]		,				__lowercase :      Optional[int] )							->				Optional[int]:
        return self.validation_end(__lowercase )
      def      UpperCAmelCase				(				self :      Union[str, Any] )							->				int:
        __UpperCAmelCase							:     Optional[Any]									=  max(1		,				self.hparams.gpus )  # TODO: consider num_tpu_cores
        __UpperCAmelCase							:     int									=  self.hparams.train_batch_size * self.hparams.accumulate_grad_batches * num_devices
        return (self.dataset_size / effective_batch_size) * self.hparams.max_epochs
      def      UpperCAmelCase				(				self :      Any		,				__lowercase :      Tuple )							->				Dict:
        if stage == "test":
          __UpperCAmelCase							:     str									=  len(self.test_dataloader().dataset )
        else:
          __UpperCAmelCase							:     Any									=  self.get_dataloader("""train"""		,				self.hparams.train_batch_size		,				shuffle=__lowercase )
          __UpperCAmelCase							:     List[Any]									=  len(self.train_dataloader().dataset )
      def      UpperCAmelCase				(				self :      List[str]		,				__lowercase :      str		,				__lowercase :      int		,				__lowercase :      bool = False )							->				Optional[int]:
        raise NotImplementedError("""You must implement this for your task""" )
      def      UpperCAmelCase				(				self :      Optional[Any] )							->				Dict:
        return self.train_loader
      def      UpperCAmelCase				(				self :      Optional[Any] )							->				Tuple:
        return self.get_dataloader("""dev"""		,				self.hparams.eval_batch_size		,				shuffle=__lowercase )
      def      UpperCAmelCase				(				self :      Dict )							->				List[Any]:
        return self.get_dataloader("""test"""		,				self.hparams.eval_batch_size		,				shuffle=__lowercase )
      def      UpperCAmelCase				(				self :      Optional[int]		,				__lowercase :      Optional[int] )							->				List[Any]:
        return os.path.join(
            self.hparams.data_dir		,				"""cached_{}_{}_{}""".format(
                __lowercase		,				list(filter(__lowercase		,				self.hparams.model_name_or_path.split("""/""" ) ) ).pop()		,				str(self.hparams.max_seq_length )		,				)		,				)
      @pl.utilities.rank_zero_only
      def      UpperCAmelCase				(				self :      Optional[Any]		,				__lowercase :      Dict[str, Any] )							->				None:
        __UpperCAmelCase							:     List[str]									=  self.output_dir.joinpath("""best_tfmr""" )
        __UpperCAmelCase							:     str									=  self.step_count
        self.model.save_pretrained(__lowercase )
        self.tokenizer.save_pretrained(__lowercase )




      @staticmethod
      def      UpperCAmelCase				(				__lowercase :      List[str]		,				__lowercase :      Optional[int] )							->				Optional[int]:
        parser.add_argument(
            """--model_name_or_path"""		,				default=__lowercase		,				type=__lowercase		,				required=__lowercase		,				help="""Path to pretrained model or model identifier from huggingface.co/models"""		,				)
        parser.add_argument(
            """--config_name"""		,				default=""""""		,				type=__lowercase		,				help="""Pretrained config name or path if not the same as model_name""" )
        parser.add_argument(
            """--tokenizer_name"""		,				default=__lowercase		,				type=__lowercase		,				help="""Pretrained tokenizer name or path if not the same as model_name"""		,				)
        parser.add_argument(
            """--cache_dir"""		,				default=""""""		,				type=__lowercase		,				help="""Where do you want to store the pre-trained models downloaded from huggingface.co"""		,				)
        parser.add_argument(
            """--encoder_layerdrop"""		,				type=__lowercase		,				help="""Encoder layer dropout probability (Optional). Goes into model.config"""		,				)
        parser.add_argument(
            """--decoder_layerdrop"""		,				type=__lowercase		,				help="""Decoder layer dropout probability (Optional). Goes into model.config"""		,				)
        parser.add_argument(
            """--dropout"""		,				type=__lowercase		,				help="""Dropout probability (Optional). Goes into model.config"""		,				)
        parser.add_argument(
            """--attention_dropout"""		,				type=__lowercase		,				help="""Attention dropout probability (Optional). Goes into model.config"""		,				)
        parser.add_argument("""--learning_rate"""		,				default=5e-5		,				type=__lowercase		,				help="""The initial learning rate for Adam.""" )
        parser.add_argument(
            """--lr_scheduler"""		,				default="""linear"""		,				choices=__lowercase		,				metavar=__lowercase		,				type=__lowercase		,				help="""Learning rate scheduler"""		,				)
        parser.add_argument("""--weight_decay"""		,				default=0.0		,				type=__lowercase		,				help="""Weight decay if we apply some.""" )
        parser.add_argument("""--adam_epsilon"""		,				default=1e-8		,				type=__lowercase		,				help="""Epsilon for Adam optimizer.""" )
        parser.add_argument("""--warmup_steps"""		,				default=0		,				type=__lowercase		,				help="""Linear warmup over warmup_steps.""" )
        parser.add_argument("""--num_workers"""		,				default=4		,				type=__lowercase		,				help="""kwarg passed to DataLoader""" )
        parser.add_argument("""--num_train_epochs"""		,				dest="""max_epochs"""		,				default=3		,				type=__lowercase )
        parser.add_argument("""--train_batch_size"""		,				default=32		,				type=__lowercase )
        parser.add_argument("""--eval_batch_size"""		,				default=32		,				type=__lowercase )
        parser.add_argument("""--adafactor"""		,				action="""store_true""" )


class    a   (			pl.Callback       ):



      """simple docstring"""


      def      UpperCAmelCase				(				self :      int		,				__lowercase :      Tuple		,				__lowercase :      str )							->				Optional[Any]:
        if (
            trainer.is_global_zero and trainer.global_rank == 0
        ):  # we initialize the retriever only on master worker with RAY. In new pytorch-lightning accelorators are removed.
          pl_module.model.rag.retriever.init_retrieval()  # better to use hook functions.


class    a   (			pl.Callback       ):



      """simple docstring"""


      def      UpperCAmelCase				(				self :      List[Any]		,				__lowercase :      int		,				__lowercase :      Union[str, Any] )							->				Optional[int]:
        __UpperCAmelCase							:     Union[str, Any]									=  trainer.lr_schedulers[0]["""scheduler"""]
        __UpperCAmelCase							:     Optional[Any]									=  {f"""lr_group_{i}""": lr for i, lr in enumerate(lr_scheduler.get_lr() )}
        pl_module.logger.log_metrics(__lowercase )
      def      UpperCAmelCase				(				self :      Any		,				__lowercase :      pl.Trainer		,				__lowercase :      pl.LightningModule )							->				Dict:
        rank_zero_info("""***** Validation results *****""" )
        __UpperCAmelCase							:     int									=  trainer.callback_metrics
        # Log results
        for key in sorted(__lowercase ):
          if key not in ["log", "progress_bar"]:
            rank_zero_info("""{} = {}\n""".format(__lowercase		,				str(metrics[key] ) ) )




      def      UpperCAmelCase				(				self :      Any		,				__lowercase :      pl.Trainer		,				__lowercase :      pl.LightningModule )							->				Any:
        rank_zero_info("""***** Test results *****""" )
        __UpperCAmelCase							:     List[str]									=  trainer.callback_metrics
        # Log and save results to file
        __UpperCAmelCase							:     Dict									=  os.path.join(pl_module.hparams.output_dir		,				"""test_results.txt""" )
        with open(__lowercase		,				"""w""" ) as writer:
          for key in sorted(__lowercase ):
            if key not in ["log", "progress_bar"]:
              rank_zero_info("""{} = {}\n""".format(__lowercase		,				str(metrics[key] ) ) )
              writer.write("""{} = {}\n""".format(__lowercase		,				str(metrics[key] ) ) )






def     lowerCamelCase__				(   __lowerCamelCase      :					Optional[Any]  ,		__lowerCamelCase      :					List[Any]		):
  #  To allow all pl args uncomment the following line
  #  parser = pl.Trainer.add_argparse_args(parser)
  parser.add_argument(
      """--output_dir"""  ,		default=__lowerCamelCase  ,		type=__lowerCamelCase  ,		required=__lowerCamelCase  ,		help="""The output directory where the model predictions and checkpoints will be written."""  ,		)
  parser.add_argument(
      """--fp16"""  ,		action="""store_true"""  ,		help="""Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit"""  ,		)

  parser.add_argument(
      """--fp16_opt_level"""  ,		type=__lowerCamelCase  ,		default="""O2"""  ,		help=(
          """For fp16: Apex AMP optimization level selected in ['O0', 'O1', 'O2', and 'O3']."""
          """See details at https://nvidia.github.io/apex/amp.html"""
      )  ,		)
  parser.add_argument("""--n_tpu_cores"""  ,		dest="""tpu_cores"""  ,		type=__lowerCamelCase		)
  parser.add_argument("""--max_grad_norm"""  ,		dest="""gradient_clip_val"""  ,		default=1.0  ,		type=__lowerCamelCase  ,		help="""Max gradient norm"""		)
  parser.add_argument("""--do_train"""  ,		action="""store_true"""  ,		help="""Whether to run training."""		)
  parser.add_argument("""--do_predict"""  ,		action="""store_true"""  ,		help="""Whether to run predictions on the test set."""		)
  parser.add_argument(
      """--gradient_accumulation_steps"""  ,		dest="""accumulate_grad_batches"""  ,		type=__lowerCamelCase  ,		default=1  ,		help="""Number of updates steps to accumulate before performing a backward/update pass."""  ,		)
  parser.add_argument("""--seed"""  ,		type=__lowerCamelCase  ,		default=42  ,		help="""random seed for initialization"""		)
  parser.add_argument(
      """--data_dir"""  ,		default=__lowerCamelCase  ,		type=__lowerCamelCase  ,		required=__lowerCamelCase  ,		help="""The input data dir. Should contain the training files for the CoNLL-2003 NER task."""  ,		)







def     lowerCamelCase__				(   __lowerCamelCase      :					BaseTransformer  ,		__lowerCamelCase      :					argparse.Namespace  ,		__lowerCamelCase      :					Any=None  ,		__lowerCamelCase      :					Optional[Any]=True  ,		__lowerCamelCase      :					Tuple=None  ,		__lowerCamelCase      :					Optional[int]=[]  ,		__lowerCamelCase      :					int=None  ,		__lowerCamelCase      :					List[str]=None  ,		**__lowerCamelCase      :					List[Any]  ,		):
  pl.seed_everything(args.seed		)

  # init model
  __UpperCAmelCase							:     Optional[Any]									=  Path(model.hparams.output_dir		)
  odir.mkdir(exist_ok=__lowerCamelCase		)

  # add custom checkpoints
  if checkpoint_callback is None:
    __UpperCAmelCase							:     Tuple									=  pl.callbacks.ModelCheckpoint(
        filepath=args.output_dir  ,		prefix="""checkpoint"""  ,		monitor="""val_loss"""  ,		mode="""min"""  ,		save_top_k=1		)
  if early_stopping_callback:
    extra_callbacks.append(__lowerCamelCase		)
  if logging_callback is None:
    __UpperCAmelCase							:     int									=  LoggingCallback()

  __UpperCAmelCase							:     int									=  {}

  # TODO: remove with PyTorch 1.6 since pl uses native amp
  if args.fpaa:
    __UpperCAmelCase							:     Dict									=  16
    # train_params["amp_level"] = args.fp16_opt_level

  if args.gpus > 1:
    __UpperCAmelCase							:     List[str]									=  """auto"""  # "ddp"
    __UpperCAmelCase							:     Union[str, Any]									=  """ddp"""

  __UpperCAmelCase							:     Optional[int]									=  args.accumulate_grad_batches
  __UpperCAmelCase							:     Dict									=  None  # extra_train_kwargs.get("profiler", None) #get unwanted logs
  __UpperCAmelCase							:     Optional[int]									=  """auto"""

  __UpperCAmelCase							:     Optional[Any]									=  pl.Trainer.from_argparse_args(
      __lowerCamelCase  ,		weights_summary=__lowerCamelCase  ,		callbacks=[logging_callback] + extra_callbacks + [checkpoint_callback] + [InitCallback()]  ,		logger=__lowerCamelCase  ,		**__lowerCamelCase  ,		)

  if args.do_train:
    trainer.fit(__lowerCamelCase		)

  return trainer


