





"""simple docstring"""




import argparse

import torch

from diffusers import UNetaDConditionModel





def 						UpperCAmelCase__		(					_UpperCAmelCase			, _UpperCAmelCase			, _UpperCAmelCase			, _UpperCAmelCase=None			, _UpperCAmelCase=None			, _UpperCAmelCase=None ):






  """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():
      A_  :	Optional[Any]    						=  old_checkpoint[path]
      A_  :	Any    						=  old_tensor.shape[0] // 3

      A_  :	Union[str, Any]    						=  (-1, channels) if len(old_tensor.shape ) == 3 else (-1)

      A_  :	Optional[Any]    						=  old_tensor.shape[0] // config['num_head_channels'] // 3

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

      A_  :	List[str]    						=  query.reshape(_UpperCAmelCase )
      A_  :	Any    						=  key.reshape(_UpperCAmelCase )
      A_  :	int    						=  value.reshape(_UpperCAmelCase )

  for path in paths:
    A_  :	Any    						=  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:
        A_  :	int    						=  new_path.replace(replacement['old']			, replacement['new'] )

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





def 						UpperCAmelCase__		(					_UpperCAmelCase			, _UpperCAmelCase=0 ):






  """simple docstring"""





  A_  :	Any    						=  []
  for old_item in old_list:
    A_  :	List[Any]    						=  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 						UpperCAmelCase__		(					_UpperCAmelCase			, _UpperCAmelCase=1 ):






  """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 						UpperCAmelCase__		(					_UpperCAmelCase			, _UpperCAmelCase=0 ):






  """simple docstring"""





  A_  :	Union[str, Any]    						=  []
  for old_item in old_list:
    mapping.append({'old': old_item, 'new': old_item} )

  return mapping





def 						UpperCAmelCase__		(					_UpperCAmelCase			, _UpperCAmelCase=0 ):






  """simple docstring"""





  A_  :	Union[str, Any]    						=  []
  for old_item in old_list:
    A_  :	Optional[Any]    						=  old_item.replace('in_layers.0'			, 'norm1' )
    A_  :	str    						=  new_item.replace('in_layers.2'			, 'conv1' )

    A_  :	List[Any]    						=  new_item.replace('out_layers.0'			, 'norm2' )
    A_  :	Tuple    						=  new_item.replace('out_layers.3'			, 'conv2' )

    A_  :	List[str]    						=  new_item.replace('emb_layers.1'			, 'time_emb_proj' )
    A_  :	int    						=  new_item.replace('skip_connection'			, 'conv_shortcut' )

    A_  :	Optional[Any]    						=  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 						UpperCAmelCase__		(					_UpperCAmelCase			, _UpperCAmelCase			, _UpperCAmelCase=None			, _UpperCAmelCase=False ):






  """simple docstring"""





  A_  :	Union[str, Any]    						=  {}
  A_  :	int    						=  list(checkpoint.keys() )

  A_  :	List[Any]    						=  '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 ) > 100 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' ):
        A_  :	str    						=  'model_ema.' + ''.join(key.split('.' )[1:] )
        A_  :	List[Any]    						=  checkpoint.pop(_UpperCAmelCase )
  else:
    if sum(k.startswith('model_ema' ) for k in keys ) > 100:
      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:
      A_  :	Union[str, Any]    						=  checkpoint.pop(_UpperCAmelCase )

  A_  :	Union[str, Any]    						=  {}

  A_  :	Tuple    						=  unet_state_dict['time_embed.0.weight']
  A_  :	List[Any]    						=  unet_state_dict['time_embed.0.bias']
  A_  :	Optional[int]    						=  unet_state_dict['time_embed.2.weight']
  A_  :	Optional[int]    						=  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":
    A_  :	Union[str, Any]    						=  unet_state_dict['label_emb.0.0.weight']
    A_  :	str    						=  unet_state_dict['label_emb.0.0.bias']
    A_  :	Optional[int]    						=  unet_state_dict['label_emb.0.2.weight']
    A_  :	List[str]    						=  unet_state_dict['label_emb.0.2.bias']
  else:
    raise NotImplementedError(f"""Not implemented `class_embed_type`: {config["class_embed_type"]}""" )

  A_  :	Any    						=  unet_state_dict['input_blocks.0.0.weight']
  A_  :	Optional[int]    						=  unet_state_dict['input_blocks.0.0.bias']

  A_  :	int    						=  [v for v in unet_state_dict if v.startswith('input_blocks.0.1' )]
  A_  :	Tuple    						=  renew_attention_paths(_UpperCAmelCase )
  A_  :	Dict    						=  {'old': 'input_blocks.0.1', 'new': 'transformer_in'}
  assign_to_checkpoint(_UpperCAmelCase			, _UpperCAmelCase			, _UpperCAmelCase			, additional_replacements=[meta_path]			, config=_UpperCAmelCase )

  A_  :	Tuple    						=  unet_state_dict['out.0.weight']
  A_  :	Optional[int]    						=  unet_state_dict['out.0.bias']
  A_  :	Optional[int]    						=  unet_state_dict['out.2.weight']
  A_  :	List[Any]    						=  unet_state_dict['out.2.bias']

  # Retrieves the keys for the input blocks only
  A_  :	List[Any]    						=  len({'.'.join(layer.split('.' )[:2] ) for layer in unet_state_dict if 'input_blocks' in layer} )
  A_  :	Union[str, Any]    						=  {
      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
  A_  :	Optional[Any]    						=  len({'.'.join(layer.split('.' )[:2] ) for layer in unet_state_dict if 'middle_block' in layer} )
  A_  :	Any    						=  {
      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
  A_  :	Union[str, Any]    						=  len({'.'.join(layer.split('.' )[:2] ) for layer in unet_state_dict if 'output_blocks' in layer} )
  A_  :	Tuple    						=  {
      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 ):
    A_  :	Any    						=  (i - 1) // (config['layers_per_block'] + 1)
    A_  :	Tuple    						=  (i - 1) % (config['layers_per_block'] + 1)

    A_  :	Any    						=  [
        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
    ]
    A_  :	Optional[int]    						=  [key for key in input_blocks[i] if f"""input_blocks.{i}.1""" in key]
    A_  :	List[Any]    						=  [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:
      A_  :	List[Any]    						=  unet_state_dict.pop(
          f"""input_blocks.{i}.op.weight""" )
      A_  :	Dict    						=  unet_state_dict.pop(
          f"""input_blocks.{i}.op.bias""" )

    A_  :	str    						=  renew_resnet_paths(_UpperCAmelCase )
    A_  :	List[str]    						=  {'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 )

    A_  :	Tuple    						=  [key for key in resnets if 'temopral_conv' in key]
    A_  :	Any    						=  renew_temp_conv_paths(_UpperCAmelCase )
    A_  :	Optional[int]    						=  {
        '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 ):
      A_  :	List[str]    						=  renew_attention_paths(_UpperCAmelCase )
      A_  :	str    						=  {'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 ):
      A_  :	List[str]    						=  renew_attention_paths(_UpperCAmelCase )
      A_  :	Tuple    						=  {
          '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 )

  A_  :	Union[str, Any]    						=  middle_blocks[0]
  A_  :	Union[str, Any]    						=  [key for key in resnet_a if 'temopral_conv' in key]
  A_  :	List[str]    						=  middle_blocks[1]
  A_  :	Optional[Any]    						=  middle_blocks[2]
  A_  :	Union[str, Any]    						=  middle_blocks[3]
  A_  :	List[Any]    						=  [key for key in resnet_a if 'temopral_conv' in key]

  A_  :	Dict    						=  renew_resnet_paths(_UpperCAmelCase )
  A_  :	Any    						=  {'old': 'middle_block.0', 'new': 'mid_block.resnets.0'}
  assign_to_checkpoint(
      _UpperCAmelCase			, _UpperCAmelCase			, _UpperCAmelCase			, config=_UpperCAmelCase			, additional_replacements=[meta_path] )

  A_  :	Dict    						=  renew_temp_conv_paths(_UpperCAmelCase )
  A_  :	List[str]    						=  {'old': 'middle_block.0.temopral_conv', 'new': 'mid_block.temp_convs.0'}
  assign_to_checkpoint(
      _UpperCAmelCase			, _UpperCAmelCase			, _UpperCAmelCase			, config=_UpperCAmelCase			, additional_replacements=[meta_path] )

  A_  :	str    						=  renew_resnet_paths(_UpperCAmelCase )
  A_  :	List[str]    						=  {'old': 'middle_block.3', 'new': 'mid_block.resnets.1'}
  assign_to_checkpoint(
      _UpperCAmelCase			, _UpperCAmelCase			, _UpperCAmelCase			, config=_UpperCAmelCase			, additional_replacements=[meta_path] )

  A_  :	Dict    						=  renew_temp_conv_paths(_UpperCAmelCase )
  A_  :	int    						=  {'old': 'middle_block.3.temopral_conv', 'new': 'mid_block.temp_convs.1'}
  assign_to_checkpoint(
      _UpperCAmelCase			, _UpperCAmelCase			, _UpperCAmelCase			, config=_UpperCAmelCase			, additional_replacements=[meta_path] )

  A_  :	int    						=  renew_attention_paths(_UpperCAmelCase )
  A_  :	Tuple    						=  {'old': 'middle_block.1', 'new': 'mid_block.attentions.0'}
  assign_to_checkpoint(
      _UpperCAmelCase			, _UpperCAmelCase			, _UpperCAmelCase			, additional_replacements=[meta_path]			, config=_UpperCAmelCase )

  A_  :	str    						=  renew_attention_paths(_UpperCAmelCase )
  A_  :	Tuple    						=  {'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 ):
    A_  :	int    						=  i // (config['layers_per_block'] + 1)
    A_  :	Optional[Any]    						=  i % (config['layers_per_block'] + 1)
    A_  :	Optional[int]    						=  [shave_segments(_UpperCAmelCase			, 2 ) for name in output_blocks[i]]
    A_  :	Tuple    						=  {}

    for layer in output_block_layers:
      A_						,	A_  :	Union[str, Any]    						=  layer.split('.' )[0], shave_segments(_UpperCAmelCase			, 1 )
      if layer_id in output_block_list:
        output_block_list[layer_id].append(_UpperCAmelCase )
      else:
        A_  :	Any    						=  [layer_name]

    if len(_UpperCAmelCase ) > 1:
      A_  :	Optional[int]    						=  [key for key in output_blocks[i] if f"""output_blocks.{i}.0""" in key]
      A_  :	int    						=  [key for key in output_blocks[i] if f"""output_blocks.{i}.1""" in key]
      A_  :	Optional[int]    						=  [key for key in output_blocks[i] if f"""output_blocks.{i}.2""" in key]

      A_  :	str    						=  renew_resnet_paths(_UpperCAmelCase )
      A_  :	str    						=  renew_resnet_paths(_UpperCAmelCase )

      A_  :	str    						=  {'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 )

      A_  :	str    						=  [key for key in resnets if 'temopral_conv' in key]
      A_  :	List[Any]    						=  renew_temp_conv_paths(_UpperCAmelCase )
      A_  :	List[Any]    						=  {
          '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 )

      A_  :	Any    						=  {k: sorted(_UpperCAmelCase ) for k, v in output_block_list.items()}
      if ["conv.bias", "conv.weight"] in output_block_list.values():
        A_  :	Optional[int]    						=  list(output_block_list.values() ).index(['conv.bias', 'conv.weight'] )
        A_  :	List[Any]    						=  unet_state_dict[
            f"""output_blocks.{i}.{index}.conv.weight"""
        ]
        A_  :	int    						=  unet_state_dict[
            f"""output_blocks.{i}.{index}.conv.bias"""
        ]

        # Clear attentions as they have been attributed above.
        if len(_UpperCAmelCase ) == 2:
          A_  :	Optional[int]    						=  []

      if len(_UpperCAmelCase ):
        A_  :	Any    						=  renew_attention_paths(_UpperCAmelCase )
        A_  :	Optional[int]    						=  {
            '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 ):
        A_  :	Tuple    						=  renew_attention_paths(_UpperCAmelCase )
        A_  :	List[Any]    						=  {
            '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:
      A_  :	str    						=  renew_resnet_paths(_UpperCAmelCase			, n_shave_prefix_segments=1 )
      for path in resnet_0_paths:
        A_  :	str    						=  '.'.join(['output_blocks', str(_UpperCAmelCase ), path['old']] )
        A_  :	Any    						=  '.'.join(['up_blocks', str(_UpperCAmelCase ), 'resnets', str(_UpperCAmelCase ), path['new']] )
        A_  :	Optional[Any]    						=  unet_state_dict[old_path]

      A_  :	Optional[Any]    						=  [l for l in output_block_layers if 'temopral_conv' in l]
      for path in temopral_conv_paths:
        A_  :	str    						=  path.split('temopral_conv.' )[-1]
        A_  :	Tuple    						=  '.'.join(['output_blocks', str(_UpperCAmelCase ), str(_UpperCAmelCase ), 'temopral_conv', pruned_path] )
        A_  :	Optional[Any]    						=  '.'.join(['up_blocks', str(_UpperCAmelCase ), 'temp_convs', str(_UpperCAmelCase ), pruned_path] )
        A_  :	str    						=  unet_state_dict[old_path]

  return new_checkpoint


if __name__ == "__main__":
   lowerCamelCase_					:						Dict      = 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.')
   lowerCamelCase_					:						Optional[int]      = parser.parse_args()

   lowerCamelCase_					:						str      = torch.load(args.checkpoint_path, map_location='cpu')
   lowerCamelCase_					:						Any      = UNetaDConditionModel()

   lowerCamelCase_					:						List[Any]      = convert_ldm_unet_checkpoint(unet_checkpoint, unet.config)

   lowerCamelCase_					:						Union[str, Any]      = set(unet.state_dict().keys()) - set(converted_ckpt.keys())
   lowerCamelCase_					:						Tuple      = 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 datetime
import json
import time
import warnings
from logging import getLogger
from pathlib import Path
from typing import Dict, List

import torch
from tqdm import tqdm

from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from utils import calculate_bleu, calculate_rouge, chunks, parse_numeric_n_bool_cl_kwargs, use_task_specific_params


lowerCamelCase_					:						List[str]      = getLogger(__name__)


lowerCamelCase_					:						Optional[Any]      = 'cuda' if torch.cuda.is_available() else 'cpu'





def 						UpperCAmelCase__		(					_UpperCAmelCase			, _UpperCAmelCase			, _UpperCAmelCase			, _UpperCAmelCase = 8			, _UpperCAmelCase = DEFAULT_DEVICE			, _UpperCAmelCase=False			, _UpperCAmelCase="summarization"			, _UpperCAmelCase=None			, **_UpperCAmelCase			, ):






  """simple docstring"""





  A_  :	Tuple    						=  Path(_UpperCAmelCase ).open('w'			, encoding='utf-8' )
  A_  :	List[str]    						=  str(_UpperCAmelCase )
  A_  :	List[str]    						=  AutoModelForSeqaSeqLM.from_pretrained(_UpperCAmelCase ).to(_UpperCAmelCase )
  if fpaa:
    A_  :	List[str]    						=  model.half()

  A_  :	int    						=  AutoTokenizer.from_pretrained(_UpperCAmelCase )
  logger.info(f"""Inferred tokenizer type: {tokenizer.__class__}""" )  # if this is wrong, check config.model_type.

  A_  :	Optional[int]    						=  time.time()
  # update config with task specific params
  use_task_specific_params(_UpperCAmelCase			, _UpperCAmelCase )
  if prefix is None:
    A_  :	Optional[int]    						=  prefix or getattr(model.config			, 'prefix'			, '' ) or ''
  for examples_chunk in tqdm(list(chunks(_UpperCAmelCase			, _UpperCAmelCase ) ) ):
    A_  :	Any    						=  [prefix + text for text in examples_chunk]
    A_  :	List[Any]    						=  tokenizer(_UpperCAmelCase			, return_tensors='pt'			, truncation=_UpperCAmelCase			, padding='longest' ).to(_UpperCAmelCase )
    A_  :	Dict    						=  model.generate(
        input_ids=batch.input_ids			, attention_mask=batch.attention_mask			, **_UpperCAmelCase			, )
    A_  :	Dict    						=  tokenizer.batch_decode(_UpperCAmelCase			, skip_special_tokens=_UpperCAmelCase			, clean_up_tokenization_spaces=_UpperCAmelCase )
    for hypothesis in dec:
      fout.write(hypothesis + '\n' )
      fout.flush()
  fout.close()
  A_  :	Dict    						=  int(time.time() - start_time )  # seconds
  A_  :	Any    						=  len(_UpperCAmelCase )
  return {"n_obs": n_obs, "runtime": runtime, "seconds_per_sample": round(runtime / n_obs			, 4 )}





def 						UpperCAmelCase__		(					):






  """simple docstring"""





  return datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S' )





def 						UpperCAmelCase__		(					_UpperCAmelCase=True ):






  """simple docstring"""





  A_  :	int    						=  argparse.ArgumentParser()
  parser.add_argument('model_name'			, type=_UpperCAmelCase			, help='like facebook/bart-large-cnn,t5-base, etc.' )
  parser.add_argument('input_path'			, type=_UpperCAmelCase			, help='like cnn_dm/test.source' )
  parser.add_argument('save_path'			, type=_UpperCAmelCase			, help='where to save summaries' )
  parser.add_argument('--reference_path'			, type=_UpperCAmelCase			, required=_UpperCAmelCase			, help='like cnn_dm/test.target' )
  parser.add_argument('--score_path'			, type=_UpperCAmelCase			, required=_UpperCAmelCase			, default='metrics.json'			, help='where to save metrics' )
  parser.add_argument('--device'			, type=_UpperCAmelCase			, required=_UpperCAmelCase			, default=_UpperCAmelCase			, help='cuda, cuda:1, cpu etc.' )
  parser.add_argument(
      '--prefix'			, type=_UpperCAmelCase			, required=_UpperCAmelCase			, default=_UpperCAmelCase			, help='will be added to the begininng of src examples' )
  parser.add_argument('--task'			, type=_UpperCAmelCase			, default='summarization'			, help='used for task_specific_params + metrics' )
  parser.add_argument('--bs'			, type=_UpperCAmelCase			, default=8			, required=_UpperCAmelCase			, help='batch size' )
  parser.add_argument(
      '--n_obs'			, type=_UpperCAmelCase			, default=-1			, required=_UpperCAmelCase			, help='How many observations. Defaults to all.' )
  parser.add_argument('--fp16'			, action='store_true' )
  parser.add_argument('--dump-args'			, action='store_true'			, help='print the custom hparams with the results' )
  parser.add_argument(
      '--info'			, nargs='?'			, type=_UpperCAmelCase			, const=datetime_now()			, help=(
          'use in conjunction w/ --dump-args to print with the results whatever other info you\'d like, e.g.'
          ' lang=en-ru. If no value is passed, the current datetime string will be used.'
      )			, )
  # Unspecified args like --num_beams=2 --decoder_start_token_id=4 are passed to model.generate
  A_						,	A_  :	int    						=  parser.parse_known_args()
  A_  :	List[Any]    						=  parse_numeric_n_bool_cl_kwargs(_UpperCAmelCase )
  if parsed_args and verbose:
    print(f"""parsed the following generate kwargs: {parsed_args}""" )
  with open(args.input_path ) as f:
    A_  :	List[str]    						=  [' ' + x.rstrip() if 't5' in args.model_name else x.rstrip() for x in f.readlines()]
  if args.n_obs > 0:
    A_  :	int    						=  examples[: args.n_obs]
  Path(args.save_path ).parent.mkdir(exist_ok=_UpperCAmelCase )
  if args.reference_path is None and Path(args.score_path ).exists():
    warnings.warn(f"""score_path {args.score_path} will be overwritten unless you type ctrl-c.""" )
  A_  :	Optional[Any]    						=  generate_summaries_or_translations(
      _UpperCAmelCase			, args.save_path			, args.model_name			, batch_size=args.bs			, device=args.device			, fpaa=args.fpaa			, task=args.task			, prefix=args.prefix			, **_UpperCAmelCase			, )

  if args.reference_path is None:
    return {}

  # Compute scores
  A_  :	Union[str, Any]    						=  calculate_bleu if 'translation' in args.task else calculate_rouge
  A_  :	List[Any]    						=  [x.rstrip() for x in open(args.save_path ).readlines()]
  A_  :	Dict    						=  [x.rstrip() for x in open(args.reference_path ).readlines()][: len(_UpperCAmelCase )]
  A_  :	dict    						=  score_fn(_UpperCAmelCase			, _UpperCAmelCase )
  scores.update(_UpperCAmelCase )

  if args.dump_args:
    scores.update(_UpperCAmelCase )
  if args.info:
    A_  :	Any    						=  args.info

  if verbose:
    print(_UpperCAmelCase )

  if args.score_path is not None:
    json.dump(_UpperCAmelCase			, open(args.score_path			, 'w' ) )

  return scores


if __name__ == "__main__":
   # Usage for MT:
   # python run_eval.py MODEL_NAME $DATA_DIR/test.source $save_dir/test_translations.txt --reference_path $DATA_DIR/test.target --score_path $save_dir/test_bleu.json  --task translation $@
   run_generate(verbose=True)