

import argparse
import json
from pathlib import Path

import requests
import torch
from datasets import load_dataset
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import (
    BeitConfig,
    BeitForImageClassification,
    BeitForMaskedImageModeling,
    BeitForSemanticSegmentation,
    BeitImageProcessor,
)
from transformers.image_utils import PILImageResampling
from transformers.utils import logging


logging.set_verbosity_info()
_UpperCamelCase			      =		logging.get_logger(__name__)







def 		_lowercase			(					lowercase__       ,     lowercase__=False       ,     lowercase__=False						):






      """simple docstring"""




      __lowerCAmelCase :						Optional[int] = '''backbone.''' if is_semantic else ''''''

      __lowerCAmelCase :						Optional[Any] = []
      for i in range(config.num_hidden_layers						):
            # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
            rename_keys.append((f"""{prefix}blocks.{i}.norm1.weight""", f"""beit.encoder.layer.{i}.layernorm_before.weight""")						)
            rename_keys.append((f"""{prefix}blocks.{i}.norm1.bias""", f"""beit.encoder.layer.{i}.layernorm_before.bias""")						)
            rename_keys.append(
                (f"""{prefix}blocks.{i}.attn.proj.weight""", f"""beit.encoder.layer.{i}.attention.output.dense.weight""")						)
            rename_keys.append(
                (f"""{prefix}blocks.{i}.attn.proj.bias""", f"""beit.encoder.layer.{i}.attention.output.dense.bias""")						)
            rename_keys.append((f"""{prefix}blocks.{i}.norm2.weight""", f"""beit.encoder.layer.{i}.layernorm_after.weight""")						)
            rename_keys.append((f"""{prefix}blocks.{i}.norm2.bias""", f"""beit.encoder.layer.{i}.layernorm_after.bias""")						)
            rename_keys.append((f"""{prefix}blocks.{i}.mlp.fc1.weight""", f"""beit.encoder.layer.{i}.intermediate.dense.weight""")						)
            rename_keys.append((f"""{prefix}blocks.{i}.mlp.fc1.bias""", f"""beit.encoder.layer.{i}.intermediate.dense.bias""")						)
            rename_keys.append((f"""{prefix}blocks.{i}.mlp.fc2.weight""", f"""beit.encoder.layer.{i}.output.dense.weight""")						)
            rename_keys.append((f"""{prefix}blocks.{i}.mlp.fc2.bias""", f"""beit.encoder.layer.{i}.output.dense.bias""")						)

      # projection layer + position embeddings
      rename_keys.extend(
          [
              (f"""{prefix}cls_token""", '''beit.embeddings.cls_token'''),
              (f"""{prefix}patch_embed.proj.weight""", '''beit.embeddings.patch_embeddings.projection.weight'''),
              (f"""{prefix}patch_embed.proj.bias""", '''beit.embeddings.patch_embeddings.projection.bias'''),
          ]						)

      if has_lm_head:
            # mask token + shared relative position bias + layernorm
            rename_keys.extend(
                [
                    ('''mask_token''', '''beit.embeddings.mask_token'''),
                    (
                        '''rel_pos_bias.relative_position_bias_table''',
                        '''beit.encoder.relative_position_bias.relative_position_bias_table''',
                    ),
                    (
                        '''rel_pos_bias.relative_position_index''',
                        '''beit.encoder.relative_position_bias.relative_position_index''',
                    ),
                    ('''norm.weight''', '''layernorm.weight'''),
                    ('''norm.bias''', '''layernorm.bias'''),
                ]						)
      elif is_semantic:
            # semantic segmentation classification heads
            rename_keys.extend(
                [
                    ('''decode_head.conv_seg.weight''', '''decode_head.classifier.weight'''),
                    ('''decode_head.conv_seg.bias''', '''decode_head.classifier.bias'''),
                    ('''auxiliary_head.conv_seg.weight''', '''auxiliary_head.classifier.weight'''),
                    ('''auxiliary_head.conv_seg.bias''', '''auxiliary_head.classifier.bias'''),
                ]						)
      else:
            # layernorm + classification head
            rename_keys.extend(
                [
                    ('''fc_norm.weight''', '''beit.pooler.layernorm.weight'''),
                    ('''fc_norm.bias''', '''beit.pooler.layernorm.bias'''),
                    ('''head.weight''', '''classifier.weight'''),
                    ('''head.bias''', '''classifier.bias'''),
                ]						)

      return rename_keys







def 		_lowercase			(					lowercase__       ,     lowercase__       ,     lowercase__=False       ,     lowercase__=False						):






      """simple docstring"""




      for i in range(config.num_hidden_layers						):
            __lowerCAmelCase :						str = '''backbone.''' if is_semantic else ''''''
            # queries, keys and values
            __lowerCAmelCase :						str = state_dict.pop(f"""{prefix}blocks.{i}.attn.qkv.weight"""						)
            __lowerCAmelCase :						Dict = state_dict.pop(f"""{prefix}blocks.{i}.attn.q_bias"""						)
            __lowerCAmelCase :						List[str] = state_dict.pop(f"""{prefix}blocks.{i}.attn.v_bias"""						)

            __lowerCAmelCase :						str = in_proj_weight[
                : config.hidden_size, :
            ]
            __lowerCAmelCase :						List[Any] = q_bias
            __lowerCAmelCase :						Dict = in_proj_weight[
                config.hidden_size : config.hidden_size * 2, :
            ]
            __lowerCAmelCase :						List[Any] = in_proj_weight[
                -config.hidden_size :, :
            ]
            __lowerCAmelCase :						str = v_bias

            # gamma_1 and gamma_2
            # we call them lambda because otherwise they are renamed when using .from_pretrained
            __lowerCAmelCase :						Optional[int] = state_dict.pop(f"""{prefix}blocks.{i}.gamma_1"""						)
            __lowerCAmelCase :						Optional[Any] = state_dict.pop(f"""{prefix}blocks.{i}.gamma_2"""						)

            __lowerCAmelCase :						Optional[int] = gamma_a
            __lowerCAmelCase :						str = gamma_a

            # relative_position bias table + index
            if not has_lm_head:
                  # each layer has its own relative position bias
                  __lowerCAmelCase :						Optional[int] = state_dict.pop(f"""{prefix}blocks.{i}.attn.relative_position_bias_table"""						)
                  __lowerCAmelCase :						Optional[int] = state_dict.pop(f"""{prefix}blocks.{i}.attn.relative_position_index"""						)

                  __lowerCAmelCase :						List[str] = table
                  __lowerCAmelCase :						Optional[Any] = index







def 		_lowercase			(					lowercase__       ,     lowercase__       ,     lowercase__						):






      """simple docstring"""




      __lowerCAmelCase :						List[str] = dct.pop(__UpperCamelCase						)
      __lowerCAmelCase :						int = val







def 		_lowercase			(					):






      """simple docstring"""




      __lowerCAmelCase :						List[Any] = '''http://images.cocodataset.org/val2017/000000039769.jpg'''
      __lowerCAmelCase :						Optional[Any] = Image.open(requests.get(__UpperCamelCase       ,     stream=__UpperCamelCase						).raw						)
      return im







@torch.no_grad()
def 		_lowercase			(					lowercase__       ,     lowercase__						):






      """simple docstring"""




      __lowerCAmelCase :						Dict = BeitConfig()
      __lowerCAmelCase :						Any = False
      __lowerCAmelCase :						Optional[int] = False
      __lowerCAmelCase :						Any = '''huggingface/label-files'''
      # set config parameters based on URL
      if checkpoint_url[-9:-4] == "pt22k":
            # masked image modeling
            __lowerCAmelCase :						Dict = True
            __lowerCAmelCase :						Optional[Any] = True
            __lowerCAmelCase :						Tuple = True
      elif checkpoint_url[-9:-4] == "ft22k":
            # intermediate fine-tuning on ImageNet-22k
            __lowerCAmelCase :						int = True
            __lowerCAmelCase :						List[str] = 2_1_8_4_1
            __lowerCAmelCase :						Union[str, Any] = '''imagenet-22k-id2label.json'''
            __lowerCAmelCase :						Union[str, Any] = json.load(open(hf_hub_download(__UpperCamelCase       ,     __UpperCamelCase       ,     repo_type='''dataset'''						)       ,     '''r'''						)						)
            __lowerCAmelCase :						Optional[int] = {int(__UpperCamelCase						): v for k, v in idalabel.items()}
            # this dataset contains 21843 labels but the model only has 21841
            # we delete the classes as mentioned in https://github.com/google-research/big_transfer/issues/18
            del idalabel[9_2_0_5]
            del idalabel[1_5_0_2_7]
            __lowerCAmelCase :						Any = idalabel
            __lowerCAmelCase :						str = {v: k for k, v in idalabel.items()}
      elif checkpoint_url[-8:-4] == "to1k":
            # fine-tuning on ImageNet-1k
            __lowerCAmelCase :						Optional[int] = True
            __lowerCAmelCase :						str = 1_0_0_0
            __lowerCAmelCase :						Dict = '''imagenet-1k-id2label.json'''
            __lowerCAmelCase :						int = json.load(open(hf_hub_download(__UpperCamelCase       ,     __UpperCamelCase       ,     repo_type='''dataset'''						)       ,     '''r'''						)						)
            __lowerCAmelCase :						int = {int(__UpperCamelCase						): v for k, v in idalabel.items()}
            __lowerCAmelCase :						List[Any] = idalabel
            __lowerCAmelCase :						Tuple = {v: k for k, v in idalabel.items()}
            if "384" in checkpoint_url:
                  __lowerCAmelCase :						Union[str, Any] = 3_8_4
            if "512" in checkpoint_url:
                  __lowerCAmelCase :						Optional[Any] = 5_1_2
      elif "ade20k" in checkpoint_url:
            # fine-tuning
            __lowerCAmelCase :						int = True
            __lowerCAmelCase :						Optional[Any] = 1_5_0
            __lowerCAmelCase :						Tuple = '''ade20k-id2label.json'''
            __lowerCAmelCase :						int = json.load(open(hf_hub_download(__UpperCamelCase       ,     __UpperCamelCase       ,     repo_type='''dataset'''						)       ,     '''r'''						)						)
            __lowerCAmelCase :						Optional[Any] = {int(__UpperCamelCase						): v for k, v in idalabel.items()}
            __lowerCAmelCase :						str = idalabel
            __lowerCAmelCase :						Tuple = {v: k for k, v in idalabel.items()}
            __lowerCAmelCase :						Optional[int] = 6_4_0
            __lowerCAmelCase :						Union[str, Any] = True
      else:
            raise ValueError('''Checkpoint not supported, URL should either end with \'pt22k\', \'ft22k\', \'to1k\' or \'ade20k\''''						)

      # size of the architecture
      if "base" in checkpoint_url:
            pass
      elif "large" in checkpoint_url:
            __lowerCAmelCase :						Union[str, Any] = 1_0_2_4
            __lowerCAmelCase :						Optional[Any] = 4_0_9_6
            __lowerCAmelCase :						List[str] = 2_4
            __lowerCAmelCase :						int = 1_6
            if "ade20k" in checkpoint_url:
                  __lowerCAmelCase :						Optional[int] = 6_4_0
                  __lowerCAmelCase :						List[Any] = [7, 1_1, 1_5, 2_3]
      else:
            raise ValueError('''Should either find \'base\' or \'large\' in checkpoint URL'''						)

      # load state_dict of original model, remove and rename some keys
      __lowerCAmelCase :						List[Any] = torch.hub.load_state_dict_from_url(__UpperCamelCase       ,     map_location='''cpu'''       ,     check_hash=__UpperCamelCase						)
      __lowerCAmelCase :						Any = state_dict['''model'''] if '''ade20k''' not in checkpoint_url else state_dict['''state_dict''']

      __lowerCAmelCase :						Any = create_rename_keys(__UpperCamelCase       ,     has_lm_head=__UpperCamelCase       ,     is_semantic=__UpperCamelCase						)
      for src, dest in rename_keys:
            rename_key(__UpperCamelCase       ,     __UpperCamelCase       ,     __UpperCamelCase						)
      read_in_q_k_v(__UpperCamelCase       ,     __UpperCamelCase       ,     has_lm_head=__UpperCamelCase       ,     is_semantic=__UpperCamelCase						)
      if is_semantic:
            # add prefix to decoder keys
            for key, val in state_dict.copy().items():
                  __lowerCAmelCase :						Dict = state_dict.pop(__UpperCamelCase						)
                  if key.startswith('''backbone.fpn'''						):
                        __lowerCAmelCase :						int = key.replace('''backbone.fpn'''       ,     '''fpn'''						)
                  __lowerCAmelCase :						Union[str, Any] = val

            # load HuggingFace model
      if checkpoint_url[-9:-4] == "pt22k":
            __lowerCAmelCase :						Tuple = BeitForMaskedImageModeling(__UpperCamelCase						)
      elif "ade20k" in checkpoint_url:
            __lowerCAmelCase :						int = BeitForSemanticSegmentation(__UpperCamelCase						)
      else:
            __lowerCAmelCase :						List[Any] = BeitForImageClassification(__UpperCamelCase						)
      model.eval()
      model.load_state_dict(__UpperCamelCase						)

      # Check outputs on an image
      if is_semantic:
            __lowerCAmelCase :						Optional[Any] = BeitImageProcessor(size=config.image_size       ,     do_center_crop=__UpperCamelCase						)
            __lowerCAmelCase :						Optional[Any] = load_dataset('''hf-internal-testing/fixtures_ade20k'''       ,     split='''test'''						)
            __lowerCAmelCase :						List[Any] = Image.open(ds[0]['''file''']						)
      else:
            __lowerCAmelCase :						Optional[Any] = BeitImageProcessor(
                size=config.image_size       ,     resample=PILImageResampling.BILINEAR       ,     do_center_crop=__UpperCamelCase						)
            __lowerCAmelCase :						Dict = prepare_img()

      __lowerCAmelCase :						Any = image_processor(images=__UpperCamelCase       ,     return_tensors='''pt'''						)
      __lowerCAmelCase :						int = encoding['''pixel_values''']

      __lowerCAmelCase :						List[str] = model(__UpperCamelCase						)
      __lowerCAmelCase :						str = outputs.logits

      # verify logits
      __lowerCAmelCase :						Optional[Any] = torch.Size([1, 1_0_0_0]						)
      if checkpoint_url[:-4].endswith('''beit_base_patch16_224_pt22k'''						):
            __lowerCAmelCase :						Optional[Any] = torch.Size([1, 1_9_6, 8_1_9_2]						)
      elif checkpoint_url[:-4].endswith('''beit_large_patch16_224_pt22k'''						):
            __lowerCAmelCase :						Optional[int] = torch.Size([1, 1_9_6, 8_1_9_2]						)
      elif checkpoint_url[:-4].endswith('''beit_base_patch16_224_pt22k_ft22k'''						):
            __lowerCAmelCase :						Any = torch.Size([1, 2_1_8_4_1]						)
            __lowerCAmelCase :						Union[str, Any] = torch.tensor([2.2_2_8_8, 2.4_6_7_1, 0.7_3_9_5]						)
            __lowerCAmelCase :						Optional[int] = 2_3_9_7
      elif checkpoint_url[:-4].endswith('''beit_large_patch16_224_pt22k_ft22k'''						):
            __lowerCAmelCase :						int = torch.Size([1, 2_1_8_4_1]						)
            __lowerCAmelCase :						List[str] = torch.tensor([1.6_8_8_1, -0.2_7_8_7, 0.5_9_0_1]						)
            __lowerCAmelCase :						Dict = 2_3_9_6
      elif checkpoint_url[:-4].endswith('''beit_base_patch16_224_pt22k_ft1k'''						):
            __lowerCAmelCase :						int = torch.tensor([0.1_2_4_1, 0.0_7_9_8, -0.6_5_6_9]						)
            __lowerCAmelCase :						Dict = 2_8_5
      elif checkpoint_url[:-4].endswith('''beit_base_patch16_224_pt22k_ft22kto1k'''						):
            __lowerCAmelCase :						str = torch.tensor([-1.2_3_8_5, -1.0_9_8_7, -1.0_1_0_8]						)
            __lowerCAmelCase :						int = 2_8_1
      elif checkpoint_url[:-4].endswith('''beit_base_patch16_384_pt22k_ft22kto1k'''						):
            __lowerCAmelCase :						List[str] = torch.tensor([-1.5_3_0_3, -0.9_4_8_4, -0.3_1_4_7]						)
            __lowerCAmelCase :						Dict = 7_6_1
      elif checkpoint_url[:-4].endswith('''beit_large_patch16_224_pt22k_ft1k'''						):
            __lowerCAmelCase :						List[Any] = torch.tensor([0.4_6_1_0, -0.0_9_2_8, 0.2_0_8_6]						)
            __lowerCAmelCase :						str = 7_6_1
      elif checkpoint_url[:-4].endswith('''beit_large_patch16_224_pt22k_ft22kto1k'''						):
            __lowerCAmelCase :						Union[str, Any] = torch.tensor([-0.4_8_0_4, 0.6_2_5_7, -0.1_8_3_7]						)
            __lowerCAmelCase :						Optional[int] = 7_6_1
      elif checkpoint_url[:-4].endswith('''beit_large_patch16_384_pt22k_ft22kto1k'''						):
            __lowerCAmelCase :						Any = torch.tensor([[-0.5_1_2_2, 0.5_1_1_7, -0.2_1_1_3]]						)
            __lowerCAmelCase :						Dict = 7_6_1
      elif checkpoint_url[:-4].endswith('''beit_large_patch16_512_pt22k_ft22kto1k'''						):
            __lowerCAmelCase :						Optional[int] = torch.tensor([-0.3_0_6_2, 0.7_2_6_1, 0.4_8_5_2]						)
            __lowerCAmelCase :						Tuple = 7_6_1
      elif checkpoint_url[:-4].endswith('''beit_base_patch16_640_pt22k_ft22ktoade20k'''						):
            __lowerCAmelCase :						str = (1, 1_5_0, 1_6_0, 1_6_0)
            __lowerCAmelCase :						int = torch.tensor(
                [
                    [[-4.9_2_2_5, -2.3_9_5_4, -3.0_5_2_2], [-2.8_8_2_2, -1.0_0_4_6, -1.7_5_6_1], [-2.9_5_4_9, -1.3_2_2_8, -2.1_3_4_7]],
                    [[-5.8_1_6_8, -3.4_1_2_9, -4.0_7_7_8], [-3.8_6_5_1, -2.2_2_1_4, -3.0_2_7_7], [-3.8_3_5_6, -2.4_6_4_3, -3.3_5_3_5]],
                    [[-0.0_0_7_8, 3.9_9_5_2, 4.0_7_5_4], [2.9_8_5_6, 4.6_9_4_4, 5.0_0_3_5], [3.2_4_1_3, 4.7_8_1_3, 4.9_9_6_9]],
                ]						)
      elif checkpoint_url[:-4].endswith('''beit_large_patch16_640_pt22k_ft22ktoade20k'''						):
            __lowerCAmelCase :						Optional[Any] = (1, 1_5_0, 1_6_0, 1_6_0)
            __lowerCAmelCase :						Dict = torch.tensor(
                [
                    [[-4.3_3_0_5, -2.3_0_4_9, -3.0_1_6_1], [-2.9_5_9_1, -1.5_3_0_5, -2.2_2_5_1], [-3.4_1_9_8, -1.8_0_0_4, -2.9_0_6_2]],
                    [[-5.8_9_2_2, -3.7_4_3_5, -4.3_9_7_8], [-4.2_0_6_3, -2.7_8_7_2, -3.4_7_5_5], [-4.2_7_9_1, -3.1_8_7_4, -4.1_6_8_1]],
                    [[0.9_8_9_5, 4.3_4_6_7, 4.7_6_6_3], [4.2_4_7_6, 5.6_8_3_0, 6.1_5_1_8], [4.5_5_5_0, 6.2_4_9_5, 6.5_1_5_4]],
                ]						)
      else:
            raise ValueError('''Can\'t verify logits as model is not supported'''						)

      assert logits.shape == expected_shape, "Shape of logits not as expected"
      if not has_lm_head:
            if is_semantic:
                  assert torch.allclose(
                      logits[0, :3, :3, :3]       ,     __UpperCamelCase       ,     atol=1E-3						), "First elements of logits not as expected"
            else:
                  print('''Predicted class idx:'''       ,     logits.argmax(-1						).item()						)
                  assert torch.allclose(
                      logits[0, :3]       ,     __UpperCamelCase       ,     atol=1E-3						), "First elements of logits not as expected"
                  assert logits.argmax(-1						).item() == expected_class_idx, "Predicted class index not as expected"

      Path(__UpperCamelCase						).mkdir(exist_ok=__UpperCamelCase						)
      print(f"""Saving model to {pytorch_dump_folder_path}"""						)
      model.save_pretrained(__UpperCamelCase						)
      print(f"""Saving image processor to {pytorch_dump_folder_path}"""						)
      image_processor.save_pretrained(__UpperCamelCase						)


if __name__ == "__main__":
 _UpperCamelCase			      =		argparse.ArgumentParser()

 parser.add_argument(
     "--checkpoint_url",
     default="https://conversationhub.blob.core.windows.net/beit-share-public/beit/beit_base_patch16_224_pt22k_ft22kto1k.pth",
     type=str,
     help="URL to the original PyTorch checkpoint (.pth file).",
 )
 parser.add_argument(
     "--pytorch_dump_folder_path", default=None, type=str, help="Path to the folder to output PyTorch model."
 )
 _UpperCamelCase			      =		parser.parse_args()
 convert_beit_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)





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


_UpperCamelCase			      =		logging.getLogger(__name__)

require_version("pytorch_lightning>=1.0.4")

_UpperCamelCase			      =		{
    "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
_UpperCamelCase			      =		{
    "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
}
_UpperCamelCase			      =		sorted(arg_to_scheduler.keys())
_UpperCamelCase			      =		"{" + ", ".join(arg_to_scheduler_choices) + "}"







class   __lowercase				(pl.LightningModule			):
 def __init__(							self				,							A_				,							A_=None				,							A_="base"				,							A_=None				,							A_=None				,							A_=None				,							**A_				,							) ->Tuple:



       '''simple docstring'''





       super().__init__()
       # TODO: move to self.save_hyperparameters()
       # self.save_hyperparameters()
       # can also expand arguments into trainer signature for easier reading

       self.save_hyperparameters(A_ )
       __lowerCAmelCase :						List[str] = 0
       __lowerCAmelCase :						Optional[Any] = Path(self.hparams.output_dir )
       __lowerCAmelCase :						Optional[Any] = self.hparams.cache_dir if self.hparams.cache_dir else None
       if config is None:
             __lowerCAmelCase :						Tuple = 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=A_				,							**A_				,							)
       else:
             __lowerCAmelCase :						PretrainedConfig = config

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

       if tokenizer is None:
             __lowerCAmelCase :						Tuple = AutoTokenizer.from_pretrained(
                 self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path				,							cache_dir=A_				,							)
       else:
             __lowerCAmelCase :						PreTrainedTokenizer = tokenizer
       __lowerCAmelCase :						Optional[Any] = MODEL_MODES[mode]
       if model is None:
             __lowerCAmelCase :						Any = 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=A_				,							)
       else:
             __lowerCAmelCase :						Tuple = model
 def      UpperCamelCase__  (							self				,							*A_				,							**A_ ) ->List[Any]:



       '''simple docstring'''





       __lowerCAmelCase :						Tuple = self.model_type.from_pretrained(*A_				,							**A_ )
 def      UpperCamelCase__  (							self ) ->int:



       '''simple docstring'''





       __lowerCAmelCase :						Optional[Any] = arg_to_scheduler[self.hparams.lr_scheduler]
       __lowerCAmelCase :						Optional[Any] = get_schedule_func(
           self.opt				,							num_warmup_steps=self.hparams.warmup_steps				,							num_training_steps=self.total_steps() )
       __lowerCAmelCase :						Optional[int] = {'''scheduler''': scheduler, '''interval''': '''step''', '''frequency''': 1}
       return scheduler
 def      UpperCamelCase__  (							self ) ->str:



       '''simple docstring'''





       __lowerCAmelCase :						int = self.model
       __lowerCAmelCase :						int = ['''bias''', '''LayerNorm.weight''']
       __lowerCAmelCase :						Dict = [
           {
               '''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:
             __lowerCAmelCase :						List[Any] = Adafactor(
                 A_				,							lr=self.hparams.learning_rate				,							scale_parameter=A_				,							relative_step=A_ )

       else:
             __lowerCAmelCase :						Union[str, Any] = AdamW(
                 A_				,							lr=self.hparams.learning_rate				,							eps=self.hparams.adam_epsilon )
       __lowerCAmelCase :						str = optimizer

       __lowerCAmelCase :						Optional[int] = self.get_lr_scheduler()

       return [optimizer], [scheduler]
 def      UpperCamelCase__  (							self				,							A_				,							A_ ) ->Union[str, Any]:



       '''simple docstring'''





       return self.validation_step(A_				,							A_ )
 def      UpperCamelCase__  (							self				,							A_ ) ->List[str]:



       '''simple docstring'''





       return self.validation_end(A_ )
 def      UpperCamelCase__  (							self ) ->int:



       '''simple docstring'''





       __lowerCAmelCase :						Dict = max(1				,							self.hparams.gpus )  # TODO: consider num_tpu_cores
       __lowerCAmelCase :						Dict = 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				,							A_ ) ->int:



       '''simple docstring'''





       if mode == "test":
             __lowerCAmelCase :						List[Any] = len(self.test_dataloader().dataset )
       else:
             __lowerCAmelCase :						int = self.get_dataloader('''train'''				,							self.hparams.train_batch_size				,							shuffle=A_ )
             __lowerCAmelCase :						str = len(self.train_dataloader().dataset )
 def      UpperCamelCase__  (							self				,							A_				,							A_				,							A_ = False ) ->int:



       '''simple docstring'''





       raise NotImplementedError('''You must implement this for your task''' )
 def      UpperCamelCase__  (							self ) ->Any:



       '''simple docstring'''





       return self.train_loader
 def      UpperCamelCase__  (							self ) ->Tuple:



       '''simple docstring'''





       return self.get_dataloader('''dev'''				,							self.hparams.eval_batch_size				,							shuffle=A_ )
 def      UpperCamelCase__  (							self ) ->List[str]:



       '''simple docstring'''





       return self.get_dataloader('''test'''				,							self.hparams.eval_batch_size				,							shuffle=A_ )
 def      UpperCamelCase__  (							self				,							A_ ) ->str:



       '''simple docstring'''





       return os.path.join(
           self.hparams.data_dir				,							'''cached_{}_{}_{}'''.format(
               A_				,							list(filter(A_				,							self.hparams.model_name_or_path.split('''/''' ) ) ).pop()				,							str(self.hparams.max_seq_length )				,							)				,							)
 @pl.utilities.rank_zero_only
 def      UpperCamelCase__  (							self				,							A_ ) ->None:



       '''simple docstring'''





       __lowerCAmelCase :						Union[str, Any] = self.output_dir.joinpath('''best_tfmr''' )
       __lowerCAmelCase :						List[str] = self.step_count
       self.model.save_pretrained(A_ )
       self.tokenizer.save_pretrained(A_ )





 @staticmethod
 def      UpperCamelCase__  (							A_				,							A_ ) ->str:



       '''simple docstring'''





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







class   __lowercase				(pl.Callback			):
 def      UpperCamelCase__  (							self				,							A_				,							A_ ) ->Optional[Any]:



       '''simple docstring'''





       __lowerCAmelCase :						str = trainer.lr_schedulers[0]['''scheduler''']
       __lowerCAmelCase :						Optional[int] = {f"""lr_group_{i}""": lr for i, lr in enumerate(lr_scheduler.get_lr() )}
       pl_module.logger.log_metrics(A_ )
 def      UpperCamelCase__  (							self				,							A_				,							A_ ) ->List[Any]:



       '''simple docstring'''





       rank_zero_info('''***** Validation results *****''' )
       __lowerCAmelCase :						str = trainer.callback_metrics
       # Log results
       for key in sorted(A_ ):
             if key not in ["log", "progress_bar"]:
                   rank_zero_info('''{} = {}\n'''.format(A_				,							str(metrics[key] ) ) )





 def      UpperCamelCase__  (							self				,							A_				,							A_ ) ->Tuple:



       '''simple docstring'''





       rank_zero_info('''***** Test results *****''' )
       __lowerCAmelCase :						Dict = trainer.callback_metrics
       # Log and save results to file
       __lowerCAmelCase :						Optional[Any] = os.path.join(pl_module.hparams.output_dir				,							'''test_results.txt''' )
       with open(A_				,							'''w''' ) as writer:
             for key in sorted(A_ ):
                   if key not in ["log", "progress_bar"]:
                         rank_zero_info('''{} = {}\n'''.format(A_				,							str(metrics[key] ) ) )
                         writer.write('''{} = {}\n'''.format(A_				,							str(metrics[key] ) ) )







def 		_lowercase			(					lowercase__       ,     lowercase__						):
      #  To allow all pl args uncomment the following line
      #  parser = pl.Trainer.add_argparse_args(parser)
      parser.add_argument(
          '''--output_dir'''       ,     default=lowercase__       ,     type=lowercase__       ,     required=lowercase__       ,     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=lowercase__       ,     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=lowercase__						)
      parser.add_argument('''--max_grad_norm'''       ,     dest='''gradient_clip_val'''       ,     default=1.0       ,     type=lowercase__       ,     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=lowercase__       ,     default=1       ,     help='''Number of updates steps to accumulate before performing a backward/update pass.'''       ,     )
      parser.add_argument('''--seed'''       ,     type=lowercase__       ,     default=4_2       ,     help='''random seed for initialization'''						)
      parser.add_argument(
          '''--data_dir'''       ,     default=lowercase__       ,     type=lowercase__       ,     required=lowercase__       ,     help='''The input data dir. Should contain the training files for the CoNLL-2003 NER task.'''       ,     )







def 		_lowercase			(					lowercase__       ,     lowercase__       ,     lowercase__=None       ,     lowercase__=True       ,     lowercase__=[]       ,     lowercase__=None       ,     lowercase__=None       ,     **lowercase__       ,     ):
      pl.seed_everything(args.seed						)

      # init model
      __lowerCAmelCase :						str = Path(model.hparams.output_dir						)
      odir.mkdir(exist_ok=lowercase__						)

      # add custom checkpoints
      if checkpoint_callback is None:
            __lowerCAmelCase :						int = 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(lowercase__						)
      if logging_callback is None:
            __lowerCAmelCase :						str = LoggingCallback()

      __lowerCAmelCase :						Dict = {}

      # TODO: remove with PyTorch 1.6 since pl uses native amp
      if args.fpaa:
            __lowerCAmelCase :						List[Any] = 1_6
            __lowerCAmelCase :						Optional[Any] = args.fpaa_opt_level

      if args.gpus > 1:
            __lowerCAmelCase :						str = '''ddp'''

      __lowerCAmelCase :						int = args.accumulate_grad_batches
      __lowerCAmelCase :						Optional[Any] = extra_train_kwargs.get('''accelerator'''       ,     lowercase__						)
      __lowerCAmelCase :						Optional[int] = extra_train_kwargs.get('''profiler'''       ,     lowercase__						)

      __lowerCAmelCase :						List[str] = pl.Trainer.from_argparse_args(
          lowercase__       ,     weights_summary=lowercase__       ,     callbacks=[logging_callback] + extra_callbacks       ,     logger=lowercase__       ,     checkpoint_callback=lowercase__       ,     **lowercase__       ,     )

      if args.do_train:
            trainer.fit(lowercase__						)

      return trainer


