


'''simple docstring'''

import argparse

import torch

from diffusers import UNetaDConditionModel



def 							SCREAMING_SNAKE_CASE__(		_UpperCamelCase   :					Optional[Any]     ,		_UpperCamelCase   :					List[Any]     ,		_UpperCamelCase   :					str     ,		_UpperCamelCase   :					Tuple=None     ,		_UpperCamelCase   :					Optional[Any]=None     ,		_UpperCamelCase   :					Union[str, Any]=None )		->       List[Any]:





   '''simple docstring'''







   assert isinstance(_UpperCamelCase     ,		_UpperCamelCase ), "Paths should be a list of dicts containing 'old' and 'new' keys."

   # Splits the attention layers into three variables.
   if attention_paths_to_split is not None:
      for path, path_map in attention_paths_to_split.items():
         UpperCamelCase__			    =    old_checkpoint[path]
         UpperCamelCase__			    =    old_tensor.shape[0] // 3

         UpperCamelCase__			    =    (-1, channels) if len(old_tensor.shape ) == 3 else (-1)

         UpperCamelCase__			    =    old_tensor.shape[0] // config["num_head_channels"] // 3

         UpperCamelCase__			    =    old_tensor.reshape((num_heads, 3 * channels // num_heads) + old_tensor.shape[1:] )
         UpperCamelCase__    ,		UpperCamelCase__    ,		UpperCamelCase__			    =    old_tensor.split(channels // num_heads     ,		dim=1 )

         UpperCamelCase__			    =    query.reshape(_UpperCamelCase )
         UpperCamelCase__			    =    key.reshape(_UpperCamelCase )
         UpperCamelCase__			    =    value.reshape(_UpperCamelCase )

   for path in paths:
      UpperCamelCase__			    =    path["new"]

      # These have already been assigned
      if attention_paths_to_split is not None and new_path in attention_paths_to_split:
         continue

      if additional_replacements is not None:
         for replacement in additional_replacements:
            UpperCamelCase__			    =    new_path.replace(replacement["old"]     ,		replacement["new"] )

        # proj_attn.weight has to be converted from conv 1D to linear
      UpperCamelCase__			    =    old_checkpoint[path["old"]]
      UpperCamelCase__			    =    ["proj_attn.weight"]
      UpperCamelCase__			    =    ["proj_out.weight", "proj_in.weight"]
      if any(k in new_path for k in names ):
         UpperCamelCase__			    =    weight[:, :, 0]
      elif any(k in new_path for k in names_a ) and len(weight.shape ) > 2 and ".attentions." not in new_path:
         UpperCamelCase__			    =    weight[:, :, 0]
      else:
         UpperCamelCase__			    =    weight



def 							SCREAMING_SNAKE_CASE__(		_UpperCamelCase   :					Optional[Any]     ,		_UpperCamelCase   :					Any=0 )		->       Optional[Any]:





   '''simple docstring'''







   UpperCamelCase__			    =    []
   for old_item in old_list:
      UpperCamelCase__			    =    old_item

      #         new_item = new_item.replace('norm.weight', 'group_norm.weight')
      #         new_item = new_item.replace('norm.bias', 'group_norm.bias')

      #         new_item = new_item.replace('proj_out.weight', 'proj_attn.weight')
      #         new_item = new_item.replace('proj_out.bias', 'proj_attn.bias')

      #         new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments)

      mapping.append({"old": old_item, "new": new_item} )

   return mapping



def 							SCREAMING_SNAKE_CASE__(		_UpperCamelCase   :					Any     ,		_UpperCamelCase   :					Optional[int]=1 )		->       List[Any]:





   '''simple docstring'''







   if n_shave_prefix_segments >= 0:
      return ".".join(path.split("." )[n_shave_prefix_segments:] )
   else:
      return ".".join(path.split("." )[:n_shave_prefix_segments] )



def 							SCREAMING_SNAKE_CASE__(		_UpperCamelCase   :					Tuple     ,		_UpperCamelCase   :					List[str]=0 )		->       Union[str, Any]:





   '''simple docstring'''







   UpperCamelCase__			    =    []
   for old_item in old_list:
      mapping.append({"old": old_item, "new": old_item} )

   return mapping



def 							SCREAMING_SNAKE_CASE__(		_UpperCamelCase   :					Optional[int]     ,		_UpperCamelCase   :					List[str]=0 )		->       List[str]:





   '''simple docstring'''







   UpperCamelCase__			    =    []
   for old_item in old_list:
      UpperCamelCase__			    =    old_item.replace("in_layers.0"     ,		"norm1" )
      UpperCamelCase__			    =    new_item.replace("in_layers.2"     ,		"conv1" )

      UpperCamelCase__			    =    new_item.replace("out_layers.0"     ,		"norm2" )
      UpperCamelCase__			    =    new_item.replace("out_layers.3"     ,		"conv2" )

      UpperCamelCase__			    =    new_item.replace("emb_layers.1"     ,		"time_emb_proj" )
      UpperCamelCase__			    =    new_item.replace("skip_connection"     ,		"conv_shortcut" )

      UpperCamelCase__			    =    shave_segments(_UpperCamelCase     ,		n_shave_prefix_segments=_UpperCamelCase )

      if "temopral_conv" not in old_item:
         mapping.append({"old": old_item, "new": new_item} )

   return mapping



def 							SCREAMING_SNAKE_CASE__(		_UpperCamelCase   :					List[Any]     ,		_UpperCamelCase   :					Any     ,		_UpperCamelCase   :					Optional[Any]=None     ,		_UpperCamelCase   :					Tuple=False )		->       List[Any]:





   '''simple docstring'''







   UpperCamelCase__			    =    {}
   UpperCamelCase__			    =    list(checkpoint.keys() )

   UpperCamelCase__			    =    "model.diffusion_model."

   # at least a 100 parameters have to start with `model_ema` in order for the checkpoint to be EMA
   if sum(k.startswith("model_ema" ) for k in keys ) > 1_00 and extract_ema:
      print(F'Checkpoint {path} has both EMA and non-EMA weights.' )
      print(
          "In this conversion only the EMA weights are extracted. If you want to instead extract the non-EMA"
          " weights (useful to continue fine-tuning), please make sure to remove the `--extract_ema` flag." )
      for key in keys:
         if key.startswith("model.diffusion_model" ):
            UpperCamelCase__			    =    "model_ema." + "".join(key.split("." )[1:] )
            UpperCamelCase__			    =    checkpoint.pop(_UpperCamelCase )
   else:
      if sum(k.startswith("model_ema" ) for k in keys ) > 1_00:
         print(
             "In this conversion only the non-EMA weights are extracted. If you want to instead extract the EMA"
             " weights (usually better for inference), please make sure to add the `--extract_ema` flag." )

      for key in keys:
         UpperCamelCase__			    =    checkpoint.pop(_UpperCamelCase )

   UpperCamelCase__			    =    {}

   UpperCamelCase__			    =    unet_state_dict["time_embed.0.weight"]
   UpperCamelCase__			    =    unet_state_dict["time_embed.0.bias"]
   UpperCamelCase__			    =    unet_state_dict["time_embed.2.weight"]
   UpperCamelCase__			    =    unet_state_dict["time_embed.2.bias"]

   if config["class_embed_type"] is None:
      # No parameters to port
      ...
   elif config["class_embed_type"] == "timestep" or config["class_embed_type"] == "projection":
      UpperCamelCase__			    =    unet_state_dict["label_emb.0.0.weight"]
      UpperCamelCase__			    =    unet_state_dict["label_emb.0.0.bias"]
      UpperCamelCase__			    =    unet_state_dict["label_emb.0.2.weight"]
      UpperCamelCase__			    =    unet_state_dict["label_emb.0.2.bias"]
   else:
      raise NotImplementedError(F'Not implemented `class_embed_type`: {config["class_embed_type"]}' )

   UpperCamelCase__			    =    unet_state_dict["input_blocks.0.0.weight"]
   UpperCamelCase__			    =    unet_state_dict["input_blocks.0.0.bias"]

   UpperCamelCase__			    =    [v for v in unet_state_dict if v.startswith("input_blocks.0.1" )]
   UpperCamelCase__			    =    renew_attention_paths(_UpperCamelCase )
   UpperCamelCase__			    =    {"old": "input_blocks.0.1", "new": "transformer_in"}
   assign_to_checkpoint(_UpperCamelCase     ,		_UpperCamelCase     ,		_UpperCamelCase     ,		additional_replacements=[meta_path]     ,		config=_UpperCamelCase )

   UpperCamelCase__			    =    unet_state_dict["out.0.weight"]
   UpperCamelCase__			    =    unet_state_dict["out.0.bias"]
   UpperCamelCase__			    =    unet_state_dict["out.2.weight"]
   UpperCamelCase__			    =    unet_state_dict["out.2.bias"]

   # Retrieves the keys for the input blocks only
   UpperCamelCase__			    =    len({".".join(layer.split("." )[:2] ) for layer in unet_state_dict if "input_blocks" in layer} )
   UpperCamelCase__			    =    {
       layer_id: [key for key in unet_state_dict if F'input_blocks.{layer_id}' in key]
       for layer_id in range(_UpperCamelCase )
   }

   # Retrieves the keys for the middle blocks only
   UpperCamelCase__			    =    len({".".join(layer.split("." )[:2] ) for layer in unet_state_dict if "middle_block" in layer} )
   UpperCamelCase__			    =    {
       layer_id: [key for key in unet_state_dict if F'middle_block.{layer_id}' in key]
       for layer_id in range(_UpperCamelCase )
   }

   # Retrieves the keys for the output blocks only
   UpperCamelCase__			    =    len({".".join(layer.split("." )[:2] ) for layer in unet_state_dict if "output_blocks" in layer} )
   UpperCamelCase__			    =    {
       layer_id: [key for key in unet_state_dict if F'output_blocks.{layer_id}' in key]
       for layer_id in range(_UpperCamelCase )
   }

   for i in range(1     ,		_UpperCamelCase ):
      UpperCamelCase__			    =    (i - 1) // (config["layers_per_block"] + 1)
      UpperCamelCase__			    =    (i - 1) % (config["layers_per_block"] + 1)

      UpperCamelCase__			    =    [
          key for key in input_blocks[i] if F'input_blocks.{i}.0' in key and F'input_blocks.{i}.0.op' not in key
      ]
      UpperCamelCase__			    =    [key for key in input_blocks[i] if F'input_blocks.{i}.1' in key]
      UpperCamelCase__			    =    [key for key in input_blocks[i] if F'input_blocks.{i}.2' in key]

      if F'input_blocks.{i}.op.weight' in unet_state_dict:
         UpperCamelCase__			    =    unet_state_dict.pop(
             F'input_blocks.{i}.op.weight' )
         UpperCamelCase__			    =    unet_state_dict.pop(
             F'input_blocks.{i}.op.bias' )

      UpperCamelCase__			    =    renew_resnet_paths(_UpperCamelCase )
      UpperCamelCase__			    =    {"old": F'input_blocks.{i}.0', "new": F'down_blocks.{block_id}.resnets.{layer_in_block_id}'}
      assign_to_checkpoint(
          _UpperCamelCase     ,		_UpperCamelCase     ,		_UpperCamelCase     ,		additional_replacements=[meta_path]     ,		config=_UpperCamelCase )

      UpperCamelCase__			    =    [key for key in resnets if "temopral_conv" in key]
      UpperCamelCase__			    =    renew_temp_conv_paths(_UpperCamelCase )
      UpperCamelCase__			    =    {
          "old": F'input_blocks.{i}.0.temopral_conv',
          "new": F'down_blocks.{block_id}.temp_convs.{layer_in_block_id}',
      }
      assign_to_checkpoint(
          _UpperCamelCase     ,		_UpperCamelCase     ,		_UpperCamelCase     ,		additional_replacements=[meta_path]     ,		config=_UpperCamelCase )

      if len(_UpperCamelCase ):
         UpperCamelCase__			    =    renew_attention_paths(_UpperCamelCase )
         UpperCamelCase__			    =    {"old": F'input_blocks.{i}.1', "new": F'down_blocks.{block_id}.attentions.{layer_in_block_id}'}
         assign_to_checkpoint(
             _UpperCamelCase     ,		_UpperCamelCase     ,		_UpperCamelCase     ,		additional_replacements=[meta_path]     ,		config=_UpperCamelCase )

      if len(_UpperCamelCase ):
         UpperCamelCase__			    =    renew_attention_paths(_UpperCamelCase )
         UpperCamelCase__			    =    {
             "old": F'input_blocks.{i}.2',
             "new": F'down_blocks.{block_id}.temp_attentions.{layer_in_block_id}',
         }
         assign_to_checkpoint(
             _UpperCamelCase     ,		_UpperCamelCase     ,		_UpperCamelCase     ,		additional_replacements=[meta_path]     ,		config=_UpperCamelCase )

   UpperCamelCase__			    =    middle_blocks[0]
   UpperCamelCase__			    =    [key for key in resnet_a if "temopral_conv" in key]
   UpperCamelCase__			    =    middle_blocks[1]
   UpperCamelCase__			    =    middle_blocks[2]
   UpperCamelCase__			    =    middle_blocks[3]
   UpperCamelCase__			    =    [key for key in resnet_a if "temopral_conv" in key]

   UpperCamelCase__			    =    renew_resnet_paths(_UpperCamelCase )
   UpperCamelCase__			    =    {"old": "middle_block.0", "new": "mid_block.resnets.0"}
   assign_to_checkpoint(
       _UpperCamelCase     ,		_UpperCamelCase     ,		_UpperCamelCase     ,		config=_UpperCamelCase     ,		additional_replacements=[meta_path] )

   UpperCamelCase__			    =    renew_temp_conv_paths(_UpperCamelCase )
   UpperCamelCase__			    =    {"old": "middle_block.0.temopral_conv", "new": "mid_block.temp_convs.0"}
   assign_to_checkpoint(
       _UpperCamelCase     ,		_UpperCamelCase     ,		_UpperCamelCase     ,		config=_UpperCamelCase     ,		additional_replacements=[meta_path] )

   UpperCamelCase__			    =    renew_resnet_paths(_UpperCamelCase )
   UpperCamelCase__			    =    {"old": "middle_block.3", "new": "mid_block.resnets.1"}
   assign_to_checkpoint(
       _UpperCamelCase     ,		_UpperCamelCase     ,		_UpperCamelCase     ,		config=_UpperCamelCase     ,		additional_replacements=[meta_path] )

   UpperCamelCase__			    =    renew_temp_conv_paths(_UpperCamelCase )
   UpperCamelCase__			    =    {"old": "middle_block.3.temopral_conv", "new": "mid_block.temp_convs.1"}
   assign_to_checkpoint(
       _UpperCamelCase     ,		_UpperCamelCase     ,		_UpperCamelCase     ,		config=_UpperCamelCase     ,		additional_replacements=[meta_path] )

   UpperCamelCase__			    =    renew_attention_paths(_UpperCamelCase )
   UpperCamelCase__			    =    {"old": "middle_block.1", "new": "mid_block.attentions.0"}
   assign_to_checkpoint(
       _UpperCamelCase     ,		_UpperCamelCase     ,		_UpperCamelCase     ,		additional_replacements=[meta_path]     ,		config=_UpperCamelCase )

   UpperCamelCase__			    =    renew_attention_paths(_UpperCamelCase )
   UpperCamelCase__			    =    {"old": "middle_block.2", "new": "mid_block.temp_attentions.0"}
   assign_to_checkpoint(
       _UpperCamelCase     ,		_UpperCamelCase     ,		_UpperCamelCase     ,		additional_replacements=[meta_path]     ,		config=_UpperCamelCase )

   for i in range(_UpperCamelCase ):
      UpperCamelCase__			    =    i // (config["layers_per_block"] + 1)
      UpperCamelCase__			    =    i % (config["layers_per_block"] + 1)
      UpperCamelCase__			    =    [shave_segments(_UpperCamelCase     ,		2 ) for name in output_blocks[i]]
      UpperCamelCase__			    =    {}

      for layer in output_block_layers:
         UpperCamelCase__    ,		UpperCamelCase__			    =    layer.split("." )[0], shave_segments(_UpperCamelCase     ,		1 )
         if layer_id in output_block_list:
            output_block_list[layer_id].append(_UpperCamelCase )
         else:
            UpperCamelCase__			    =    [layer_name]

      if len(_UpperCamelCase ) > 1:
         UpperCamelCase__			    =    [key for key in output_blocks[i] if F'output_blocks.{i}.0' in key]
         UpperCamelCase__			    =    [key for key in output_blocks[i] if F'output_blocks.{i}.1' in key]
         UpperCamelCase__			    =    [key for key in output_blocks[i] if F'output_blocks.{i}.2' in key]

         UpperCamelCase__			    =    renew_resnet_paths(_UpperCamelCase )
         UpperCamelCase__			    =    renew_resnet_paths(_UpperCamelCase )

         UpperCamelCase__			    =    {"old": F'output_blocks.{i}.0', "new": F'up_blocks.{block_id}.resnets.{layer_in_block_id}'}
         assign_to_checkpoint(
             _UpperCamelCase     ,		_UpperCamelCase     ,		_UpperCamelCase     ,		additional_replacements=[meta_path]     ,		config=_UpperCamelCase )

         UpperCamelCase__			    =    [key for key in resnets if "temopral_conv" in key]
         UpperCamelCase__			    =    renew_temp_conv_paths(_UpperCamelCase )
         UpperCamelCase__			    =    {
             "old": F'output_blocks.{i}.0.temopral_conv',
             "new": F'up_blocks.{block_id}.temp_convs.{layer_in_block_id}',
         }
         assign_to_checkpoint(
             _UpperCamelCase     ,		_UpperCamelCase     ,		_UpperCamelCase     ,		additional_replacements=[meta_path]     ,		config=_UpperCamelCase )

         UpperCamelCase__			    =    {k: sorted(_UpperCamelCase ) for k, v in output_block_list.items()}
         if ["conv.bias", "conv.weight"] in output_block_list.values():
            UpperCamelCase__			    =    list(output_block_list.values() ).index(["conv.bias", "conv.weight"] )
            UpperCamelCase__			    =    unet_state_dict[
                F'output_blocks.{i}.{index}.conv.weight'
            ]
            UpperCamelCase__			    =    unet_state_dict[
                F'output_blocks.{i}.{index}.conv.bias'
            ]

            # Clear attentions as they have been attributed above.
            if len(_UpperCamelCase ) == 2:
               UpperCamelCase__			    =    []

         if len(_UpperCamelCase ):
            UpperCamelCase__			    =    renew_attention_paths(_UpperCamelCase )
            UpperCamelCase__			    =    {
                "old": F'output_blocks.{i}.1',
                "new": F'up_blocks.{block_id}.attentions.{layer_in_block_id}',
            }
            assign_to_checkpoint(
                _UpperCamelCase     ,		_UpperCamelCase     ,		_UpperCamelCase     ,		additional_replacements=[meta_path]     ,		config=_UpperCamelCase )

         if len(_UpperCamelCase ):
            UpperCamelCase__			    =    renew_attention_paths(_UpperCamelCase )
            UpperCamelCase__			    =    {
                "old": F'output_blocks.{i}.2',
                "new": F'up_blocks.{block_id}.temp_attentions.{layer_in_block_id}',
            }
            assign_to_checkpoint(
                _UpperCamelCase     ,		_UpperCamelCase     ,		_UpperCamelCase     ,		additional_replacements=[meta_path]     ,		config=_UpperCamelCase )
      else:
         UpperCamelCase__			    =    renew_resnet_paths(_UpperCamelCase     ,		n_shave_prefix_segments=1 )
         for path in resnet_0_paths:
            UpperCamelCase__			    =    ".".join(["output_blocks", str(_UpperCamelCase ), path["old"]] )
            UpperCamelCase__			    =    ".".join(["up_blocks", str(_UpperCamelCase ), "resnets", str(_UpperCamelCase ), path["new"]] )
            UpperCamelCase__			    =    unet_state_dict[old_path]

         UpperCamelCase__			    =    [l for l in output_block_layers if "temopral_conv" in l]
         for path in temopral_conv_paths:
            UpperCamelCase__			    =    path.split("temopral_conv." )[-1]
            UpperCamelCase__			    =    ".".join(["output_blocks", str(_UpperCamelCase ), str(_UpperCamelCase ), "temopral_conv", pruned_path] )
            UpperCamelCase__			    =    ".".join(["up_blocks", str(_UpperCamelCase ), "temp_convs", str(_UpperCamelCase ), pruned_path] )
            UpperCamelCase__			    =    unet_state_dict[old_path]

   return new_checkpoint


if __name__ == "__main__":
       __lowercase:		int    						=  argparse.ArgumentParser()

       parser.add_argument(
           "--checkpoint_path", default=None, type=str, required=True, help="Path to the checkpoint to convert."
       )
       parser.add_argument("--dump_path", default=None, type=str, required=True, help="Path to the output model.")
       __lowercase:		List[str]    						=  parser.parse_args()

       __lowercase:		List[Any]    						=  torch.load(args.checkpoint_path, map_location="cpu")
       __lowercase:		Optional[int]    						=  UNetaDConditionModel()

       __lowercase:		Optional[int]    						=  convert_ldm_unet_checkpoint(unet_checkpoint, unet.config)

       __lowercase:		Union[str, Any]    						=  set(unet.state_dict().keys()) - set(converted_ckpt.keys())
       __lowercase:		Any    						=  set(converted_ckpt.keys()) - set(unet.state_dict().keys())

       assert len(diff_a) == len(diff_a) == 0, "Converted weights don't match"

       # load state_dict
       unet.load_state_dict(converted_ckpt)

       unet.save_pretrained(args.dump_path)

       # -- finish converting the unet --




'''simple docstring'''







import argparse
import logging
import os
import sys
import time

import tensorflow as tf
from datasets import load_dataset
from tqdm import tqdm

from transformers import AutoTokenizer, TFAutoModelForSequenceClassification
from transformers.utils import is_sagemaker_dp_enabled


if os.environ.get('SDP_ENABLED') or is_sagemaker_dp_enabled():
      lowercase__  :	Any            =      True
      lowercase__  :	Tuple            =      'p3dn.24xlarge'
      import smdistributed.dataparallel.tensorflow as sdp
else:
      lowercase__  :	Dict            =      False





def     snake_case__			(lowerCAmelCase_       ,	lowerCAmelCase_       ,	lowerCAmelCase_       ,	lowerCAmelCase_       ,	lowerCAmelCase_       ,	lowerCAmelCase_       ,	lowerCAmelCase_=None						):
    UpperCamelCase__	     =      tqdm(lowerCAmelCase_						)
    for i, batch in enumerate(lowerCAmelCase_						):
        with tf.GradientTape() as tape:
            UpperCamelCase__     ,	UpperCamelCase__	     =      batch
            UpperCamelCase__	     =      model(lowerCAmelCase_						)
            UpperCamelCase__	     =      loss(lowerCAmelCase_       ,	outputs.logits						)

        if SDP_ENABLED:
            UpperCamelCase__	     =      sdp.DistributedGradientTape(lowerCAmelCase_       ,	sparse_as_dense=lowerCAmelCase_						)

        UpperCamelCase__	     =      tape.gradient(lowerCAmelCase_       ,	model.trainable_variables						)
        opt.apply_gradients(zip(lowerCAmelCase_       ,	model.trainable_variables						)						)

        pbar.set_description(F'Loss: {loss_value:.4f}'						)

        if SDP_ENABLED and i == 0:
            sdp.broadcast_variables(model.variables       ,	root_rank=0						)
            sdp.broadcast_variables(opt.variables()       ,	root_rank=0						)

        if max_steps and i >= max_steps:
            break

    UpperCamelCase__	     =      {"loss": loss_value.numpy()}
    return train_results





def     snake_case__			(lowerCAmelCase_       ,	lowerCAmelCase_       ,	lowerCAmelCase_						):
    # Load dataset
    UpperCamelCase__     ,	UpperCamelCase__	     =      load_dataset("imdb"       ,	split=["train", "test"]						)

    # Preprocess train dataset
    UpperCamelCase__	     =      train_dataset.map(
        lambda lowerCAmelCase_						: tokenizer(e["text"]       ,	truncation=lowerCAmelCase_       ,	padding="max_length"						)       ,	batched=lowerCAmelCase_						)
    train_dataset.set_format(type="tensorflow"       ,	columns=["input_ids", "attention_mask", "label"]						)

    UpperCamelCase__	     =      {
        x: train_dataset[x].to_tensor(default_value=0       ,	shape=[None, tokenizer.model_max_length]						)
        for x in ["input_ids", "attention_mask"]
    }
    UpperCamelCase__	     =      tf.data.Dataset.from_tensor_slices((train_features, train_dataset["label"])						)

    # Preprocess test dataset
    UpperCamelCase__	     =      test_dataset.map(
        lambda lowerCAmelCase_						: tokenizer(e["text"]       ,	truncation=lowerCAmelCase_       ,	padding="max_length"						)       ,	batched=lowerCAmelCase_						)
    test_dataset.set_format(type="tensorflow"       ,	columns=["input_ids", "attention_mask", "label"]						)

    UpperCamelCase__	     =      {
        x: test_dataset[x].to_tensor(default_value=0       ,	shape=[None, tokenizer.model_max_length]						)
        for x in ["input_ids", "attention_mask"]
    }
    UpperCamelCase__	     =      tf.data.Dataset.from_tensor_slices((test_features, test_dataset["label"])						)

    if SDP_ENABLED:
        UpperCamelCase__	     =      tf_train_dataset.shard(sdp.size()       ,	sdp.rank()						)
        UpperCamelCase__	     =      tf_test_dataset.shard(sdp.size()       ,	sdp.rank()						)
    UpperCamelCase__	     =      tf_train_dataset.batch(lowerCAmelCase_       ,	drop_remainder=lowerCAmelCase_						)
    UpperCamelCase__	     =      tf_test_dataset.batch(lowerCAmelCase_       ,	drop_remainder=lowerCAmelCase_						)

    return tf_train_dataset, tf_test_dataset


if __name__ == "__main__":
      lowercase__  :	Any            =      argparse.ArgumentParser()

      # Hyperparameters sent by the client are passed as command-line arguments to the script.
      parser.add_argument('--epochs', type=int, default=3)
      parser.add_argument('--per_device_train_batch_size', type=int, default=16)
      parser.add_argument('--per_device_eval_batch_size', type=int, default=8)
      parser.add_argument('--model_name_or_path', type=str)
      parser.add_argument('--learning_rate', type=str, default=5e-5)
      parser.add_argument('--do_train', type=bool, default=True)
      parser.add_argument('--do_eval', type=bool, default=True)
      parser.add_argument('--output_dir', type=str)
      parser.add_argument('--max_steps', type=int, default=None)

      # Data, model, and output directories
      parser.add_argument('--output_data_dir', type=str, default=os.environ['SM_OUTPUT_DATA_DIR'])
      parser.add_argument('--model_dir', type=str, default=os.environ['SM_MODEL_DIR'])
      parser.add_argument('--n_gpus', type=str, default=os.environ['SM_NUM_GPUS'])

      lowercase__       ,	lowercase__  :	Union[str, Any]            =      parser.parse_known_args()

      # Set up logging
      lowercase__  :	int            =      logging.getLogger(__name__)

      logging.basicConfig(
          level=logging.getLevelName('INFO'),
          handlers=[logging.StreamHandler(sys.stdout)],
          format='%(asctime)s - %(name)s - %(levelname)s - %(message)s',
      )

      if SDP_ENABLED:
            sdp.init()

            lowercase__  :	Union[str, Any]            =      tf.config.experimental.list_physical_devices('GPU')
            for gpu in gpus:
                  tf.config.experimental.set_memory_growth(gpu, True)
            if gpus:
                  tf.config.experimental.set_visible_devices(gpus[sdp.local_rank()], 'GPU')

    # Load model and tokenizer
      lowercase__  :	int            =      TFAutoModelForSequenceClassification.from_pretrained(args.model_name_or_path)
      lowercase__  :	Any            =      AutoTokenizer.from_pretrained(args.model_name_or_path)

      # get datasets
      lowercase__       ,	lowercase__  :	Dict            =      get_datasets(
          tokenizer=tokenizer,
          train_batch_size=args.per_device_train_batch_size,
          eval_batch_size=args.per_device_eval_batch_size,
      )

      # fine optimizer and loss
      lowercase__  :	Any            =      tf.keras.optimizers.Adam(learning_rate=args.learning_rate)
      lowercase__  :	Tuple            =      tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
      lowercase__  :	Tuple            =      [tf.keras.metrics.SparseCategoricalAccuracy()]
      model.compile(optimizer=optimizer, loss=loss, metrics=metrics)

      # Training
      if args.do_train:
            # train_results = model.fit(tf_train_dataset, epochs=args.epochs, batch_size=args.train_batch_size)
            lowercase__  :	List[str]            =      time.time()
            lowercase__  :	Union[str, Any]            =      fit(
                model,
                loss,
                optimizer,
                tf_train_dataset,
                args.epochs,
                args.per_device_train_batch_size,
                max_steps=args.max_steps,
            )
            lowercase__  :	Dict            =      time.time() - start_train_time
            logger.info('*** Train ***')
            logger.info(F'''train_runtime = {end_train_time}''')

            lowercase__  :	List[str]            =      os.path.join(args.output_dir, 'train_results.txt')

            if not SDP_ENABLED or sdp.rank() == 0:
                  with open(output_eval_file, 'w') as writer:
                        logger.info('***** Train results *****')
                        logger.info(train_results)
                        for key, value in train_results.items():
                              logger.info(F'''  {key} = {value}''')
                              writer.write(F'''{key} = {value}\n''')

    # Evaluation
      if args.do_eval and (not SDP_ENABLED or sdp.rank() == 0):
            lowercase__  :	Tuple            =      model.evaluate(tf_test_dataset, batch_size=args.per_device_eval_batch_size, return_dict=True)
            logger.info('*** Evaluate ***')

            lowercase__  :	Any            =      os.path.join(args.output_dir, 'eval_results.txt')

            with open(output_eval_file, 'w') as writer:
                  logger.info('***** Eval results *****')
                  logger.info(result)
                  for key, value in result.items():
                        logger.info(F'''  {key} = {value}''')
                        writer.write(F'''{key} = {value}\n''')

    # Save result
      if SDP_ENABLED:
            if sdp.rank() == 0:
                  model.save_pretrained(args.output_dir)
                  tokenizer.save_pretrained(args.output_dir)
      else:
            model.save_pretrained(args.output_dir)
            tokenizer.save_pretrained(args.output_dir)




