

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


UpperCamelCase__     :					str								=			getLogger(__name__)


UpperCamelCase__     :					Tuple								=			"cuda" if torch.cuda.is_available() else "cpu"


def     _UpperCAmelCase				(     _SCREAMING_SNAKE_CASE    :						List[str]       ,      _SCREAMING_SNAKE_CASE    :						str       ,      _SCREAMING_SNAKE_CASE    :						str       ,      _SCREAMING_SNAKE_CASE    :						int = 8       ,      _SCREAMING_SNAKE_CASE    :						str = DEFAULT_DEVICE       ,      _SCREAMING_SNAKE_CASE    :						Optional[Any]=False       ,      _SCREAMING_SNAKE_CASE    :						Dict="summarization"       ,      _SCREAMING_SNAKE_CASE    :						List[Any]=None       ,      **_SCREAMING_SNAKE_CASE    :						List[str]       ,      ):





    """simple docstring"""


    SCREAMING_SNAKE_CASE_													=  Path(_SCREAMING_SNAKE_CASE				).open('w'       ,      encoding='utf-8'				)
    SCREAMING_SNAKE_CASE_													=  str(_SCREAMING_SNAKE_CASE				)
    SCREAMING_SNAKE_CASE_													=  AutoModelForSeqaSeqLM.from_pretrained(_SCREAMING_SNAKE_CASE				).to(_SCREAMING_SNAKE_CASE				)
    if fpaa:
        SCREAMING_SNAKE_CASE_													=  model.half()

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

    SCREAMING_SNAKE_CASE_													=  time.time()
    # update config with task specific params
    use_task_specific_params(_SCREAMING_SNAKE_CASE       ,      _SCREAMING_SNAKE_CASE				)
    if prefix is None:
        SCREAMING_SNAKE_CASE_													=  prefix or getattr(model.config       ,      'prefix'       ,      ''				) or ''
    for examples_chunk in tqdm(list(chunks(_SCREAMING_SNAKE_CASE       ,      _SCREAMING_SNAKE_CASE				)				)				):
        SCREAMING_SNAKE_CASE_													=  [prefix + text for text in examples_chunk]
        SCREAMING_SNAKE_CASE_													=  tokenizer(_SCREAMING_SNAKE_CASE       ,      return_tensors='pt'       ,      truncation=_SCREAMING_SNAKE_CASE       ,      padding='longest'				).to(_SCREAMING_SNAKE_CASE				)
        SCREAMING_SNAKE_CASE_													=  model.generate(
            input_ids=batch.input_ids       ,      attention_mask=batch.attention_mask       ,      **_SCREAMING_SNAKE_CASE       ,      )
        SCREAMING_SNAKE_CASE_													=  tokenizer.batch_decode(_SCREAMING_SNAKE_CASE       ,      skip_special_tokens=_SCREAMING_SNAKE_CASE       ,      clean_up_tokenization_spaces=_SCREAMING_SNAKE_CASE				)
        for hypothesis in dec:
            fout.write(hypothesis + '\n'				)
            fout.flush()
    fout.close()
    SCREAMING_SNAKE_CASE_													=  int(time.time() - start_time				)  # seconds
    SCREAMING_SNAKE_CASE_													=  len(_SCREAMING_SNAKE_CASE				)
    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				(     _SCREAMING_SNAKE_CASE    :						Optional[int]=True				):





    """simple docstring"""


    SCREAMING_SNAKE_CASE_													=  argparse.ArgumentParser()
    parser.add_argument('model_name'       ,      type=_SCREAMING_SNAKE_CASE       ,      help='like facebook/bart-large-cnn,t5-base, etc.'				)
    parser.add_argument('input_path'       ,      type=_SCREAMING_SNAKE_CASE       ,      help='like cnn_dm/test.source'				)
    parser.add_argument('save_path'       ,      type=_SCREAMING_SNAKE_CASE       ,      help='where to save summaries'				)
    parser.add_argument('--reference_path'       ,      type=_SCREAMING_SNAKE_CASE       ,      required=_SCREAMING_SNAKE_CASE       ,      help='like cnn_dm/test.target'				)
    parser.add_argument('--score_path'       ,      type=_SCREAMING_SNAKE_CASE       ,      required=_SCREAMING_SNAKE_CASE       ,      default='metrics.json'       ,      help='where to save metrics'				)
    parser.add_argument('--device'       ,      type=_SCREAMING_SNAKE_CASE       ,      required=_SCREAMING_SNAKE_CASE       ,      default=_SCREAMING_SNAKE_CASE       ,      help='cuda, cuda:1, cpu etc.'				)
    parser.add_argument(
        '--prefix'       ,      type=_SCREAMING_SNAKE_CASE       ,      required=_SCREAMING_SNAKE_CASE       ,      default=_SCREAMING_SNAKE_CASE       ,      help='will be added to the begininng of src examples'				)
    parser.add_argument('--task'       ,      type=_SCREAMING_SNAKE_CASE       ,      default='summarization'       ,      help='used for task_specific_params + metrics'				)
    parser.add_argument('--bs'       ,      type=_SCREAMING_SNAKE_CASE       ,      default=8       ,      required=_SCREAMING_SNAKE_CASE       ,      help='batch size'				)
    parser.add_argument(
        '--n_obs'       ,      type=_SCREAMING_SNAKE_CASE       ,      default=-1       ,      required=_SCREAMING_SNAKE_CASE       ,      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=_SCREAMING_SNAKE_CASE       ,      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
    SCREAMING_SNAKE_CASE_							,	SCREAMING_SNAKE_CASE_													=  parser.parse_known_args()
    SCREAMING_SNAKE_CASE_													=  parse_numeric_n_bool_cl_kwargs(_SCREAMING_SNAKE_CASE				)
    if parsed_args and verbose:
        print(f"""parsed the following generate kwargs: {parsed_args}"""				)
    with open(args.input_path				) as f:
        SCREAMING_SNAKE_CASE_													=  [' ' + x.rstrip() if 't5' in args.model_name else x.rstrip() for x in f.readlines()]
    if args.n_obs > 0:
        SCREAMING_SNAKE_CASE_													=  examples[: args.n_obs]
    Path(args.save_path				).parent.mkdir(exist_ok=_SCREAMING_SNAKE_CASE				)
    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."""				)
    SCREAMING_SNAKE_CASE_													=  generate_summaries_or_translations(
        _SCREAMING_SNAKE_CASE       ,      args.save_path       ,      args.model_name       ,      batch_size=args.bs       ,      device=args.device       ,      fpaa=args.fpaa       ,      task=args.task       ,      prefix=args.prefix       ,      **_SCREAMING_SNAKE_CASE       ,      )

    if args.reference_path is None:
        return {}

    # Compute scores
    SCREAMING_SNAKE_CASE_													=  calculate_bleu if 'translation' in args.task else calculate_rouge
    SCREAMING_SNAKE_CASE_													=  [x.rstrip() for x in open(args.save_path				).readlines()]
    SCREAMING_SNAKE_CASE_													=  [x.rstrip() for x in open(args.reference_path				).readlines()][: len(_SCREAMING_SNAKE_CASE				)]
    SCREAMING_SNAKE_CASE_													=  score_fn(_SCREAMING_SNAKE_CASE       ,      _SCREAMING_SNAKE_CASE				)
    scores.update(_SCREAMING_SNAKE_CASE				)

    if args.dump_args:
        scores.update(_SCREAMING_SNAKE_CASE				)
    if args.info:
        SCREAMING_SNAKE_CASE_													=  args.info

    if verbose:
        print(_SCREAMING_SNAKE_CASE				)

    if args.score_path is not None:
        json.dump(_SCREAMING_SNAKE_CASE       ,      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)





from dataclasses import dataclass
from typing import Any, Dict, Optional

import torch
import torch.nn.functional as F
from torch import nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..models.embeddings import ImagePositionalEmbeddings
from ..utils import BaseOutput, deprecate
from .attention import BasicTransformerBlock
from .embeddings import PatchEmbed
from .modeling_utils import ModelMixin



@dataclass
class 					__snake_case     (			lowerCAmelCase__				):
  __lowerCAmelCase   :		torch.FloatTensor






class 					__snake_case     (			lowerCAmelCase__   ,       lowerCAmelCase__				):

  @register_to_config
  def __init__(    self  ,						_A = 16  ,						_A = 88  ,						_A = None  ,						_A = None  ,						_A = 1  ,						_A = 0.0  ,						_A = 32  ,						_A = None  ,						_A = False  ,						_A = None  ,						_A = None  ,						_A = None  ,						_A = "geglu"  ,						_A = None  ,						_A = False  ,						_A = False  ,						_A = False  ,						_A = "layer_norm"  ,						_A = True  ,						):
      super().__init__()
      SCREAMING_SNAKE_CASE_													=  use_linear_projection
      SCREAMING_SNAKE_CASE_													=  num_attention_heads
      SCREAMING_SNAKE_CASE_													=  attention_head_dim
      SCREAMING_SNAKE_CASE_													=  num_attention_heads * attention_head_dim

      # 1. Transformer2DModel can process both standard continuous images of shape `(batch_size, num_channels, width, height)` as well as quantized image embeddings of shape `(batch_size, num_image_vectors)`
      # Define whether input is continuous or discrete depending on configuration
      SCREAMING_SNAKE_CASE_													=  (in_channels is not None) and (patch_size is None)
      SCREAMING_SNAKE_CASE_													=  num_vector_embeds is not None
      SCREAMING_SNAKE_CASE_													=  in_channels is not None and patch_size is not None

      if norm_type == "layer_norm" and num_embeds_ada_norm is not None:
          SCREAMING_SNAKE_CASE_													=  (
              f"""The configuration file of this model: {self.__class__} is outdated. `norm_type` is either not set or"""
              ' incorrectly set to `\'layer_norm\'`.Make sure to set `norm_type` to `\'ada_norm\'` in the config.'
              ' Please make sure to update the config accordingly as leaving `norm_type` might led to incorrect'
              ' results in future versions. If you have downloaded this checkpoint from the Hugging Face Hub, it'
              ' would be very nice if you could open a Pull request for the `transformer/config.json` file'
          )
          deprecate('norm_type!=num_embeds_ada_norm'  ,						'1.0.0'  ,						_A  ,						standard_warn=_A)
          SCREAMING_SNAKE_CASE_													=  'ada_norm'

      if self.is_input_continuous and self.is_input_vectorized:
          raise ValueError(
              f"""Cannot define both `in_channels`: {in_channels} and `num_vector_embeds`: {num_vector_embeds}. Make"""
              ' sure that either `in_channels` or `num_vector_embeds` is None.')
      elif self.is_input_vectorized and self.is_input_patches:
          raise ValueError(
              f"""Cannot define both `num_vector_embeds`: {num_vector_embeds} and `patch_size`: {patch_size}. Make"""
              ' sure that either `num_vector_embeds` or `num_patches` is None.')
      elif not self.is_input_continuous and not self.is_input_vectorized and not self.is_input_patches:
          raise ValueError(
              f"""Has to define `in_channels`: {in_channels}, `num_vector_embeds`: {num_vector_embeds}, or patch_size:"""
              f""" {patch_size}. Make sure that `in_channels`, `num_vector_embeds` or `num_patches` is not None.""")

      # 2. Define input layers
      if self.is_input_continuous:
          SCREAMING_SNAKE_CASE_													=  in_channels

          SCREAMING_SNAKE_CASE_													=  torch.nn.GroupNorm(num_groups=_A  ,						num_channels=_A  ,						eps=1E-6  ,						affine=_A)
          if use_linear_projection:
              SCREAMING_SNAKE_CASE_													=  nn.Linear(_A  ,						_A)
          else:
              SCREAMING_SNAKE_CASE_													=  nn.Convad(_A  ,						_A  ,						kernel_size=1  ,						stride=1  ,						padding=0)
      elif self.is_input_vectorized:
          assert sample_size is not None, "Transformer2DModel over discrete input must provide sample_size"
          assert num_vector_embeds is not None, "Transformer2DModel over discrete input must provide num_embed"

          SCREAMING_SNAKE_CASE_													=  sample_size
          SCREAMING_SNAKE_CASE_													=  sample_size
          SCREAMING_SNAKE_CASE_													=  num_vector_embeds
          SCREAMING_SNAKE_CASE_													=  self.height * self.width

          SCREAMING_SNAKE_CASE_													=  ImagePositionalEmbeddings(
              num_embed=_A  ,						embed_dim=_A  ,						height=self.height  ,						width=self.width)
      elif self.is_input_patches:
          assert sample_size is not None, "Transformer2DModel over patched input must provide sample_size"

          SCREAMING_SNAKE_CASE_													=  sample_size
          SCREAMING_SNAKE_CASE_													=  sample_size

          SCREAMING_SNAKE_CASE_													=  patch_size
          SCREAMING_SNAKE_CASE_													=  PatchEmbed(
              height=_A  ,						width=_A  ,						patch_size=_A  ,						in_channels=_A  ,						embed_dim=_A  ,						)

      # 3. Define transformers blocks
      SCREAMING_SNAKE_CASE_													=  nn.ModuleList(
          [
              BasicTransformerBlock(
                  _A  ,						_A  ,						_A  ,						dropout=_A  ,						cross_attention_dim=_A  ,						activation_fn=_A  ,						num_embeds_ada_norm=_A  ,						attention_bias=_A  ,						only_cross_attention=_A  ,						upcast_attention=_A  ,						norm_type=_A  ,						norm_elementwise_affine=_A  ,						)
              for d in range(_A)
          ])

      # 4. Define output layers
      SCREAMING_SNAKE_CASE_													=  in_channels if out_channels is None else out_channels
      if self.is_input_continuous:
          # TODO: should use out_channels for continuous projections
          if use_linear_projection:
              SCREAMING_SNAKE_CASE_													=  nn.Linear(_A  ,						_A)
          else:
              SCREAMING_SNAKE_CASE_													=  nn.Convad(_A  ,						_A  ,						kernel_size=1  ,						stride=1  ,						padding=0)
      elif self.is_input_vectorized:
          SCREAMING_SNAKE_CASE_													=  nn.LayerNorm(_A)
          SCREAMING_SNAKE_CASE_													=  nn.Linear(_A  ,						self.num_vector_embeds - 1)
      elif self.is_input_patches:
          SCREAMING_SNAKE_CASE_													=  nn.LayerNorm(_A  ,						elementwise_affine=_A  ,						eps=1E-6)
          SCREAMING_SNAKE_CASE_													=  nn.Linear(_A  ,						2 * inner_dim)
          SCREAMING_SNAKE_CASE_													=  nn.Linear(_A  ,						patch_size * patch_size * self.out_channels)


  def   lowerCAmelCase__   (    self  ,						_A  ,						_A = None  ,						_A = None  ,						_A = None  ,						_A = None  ,						_A = None  ,						_A = None  ,						_A = True  ,						):
      # ensure attention_mask is a bias, and give it a singleton query_tokens dimension.
      #   we may have done this conversion already, e.g. if we came here via UNet2DConditionModel#forward.
      #   we can tell by counting dims; if ndim == 2: it's a mask rather than a bias.
      # expects mask of shape:
      #   [batch, key_tokens]
      # adds singleton query_tokens dimension:
      #   [batch,                    1, key_tokens]
      # this helps to broadcast it as a bias over attention scores, which will be in one of the following shapes:
      #   [batch,  heads, query_tokens, key_tokens] (e.g. torch sdp attn)
      #   [batch * heads, query_tokens, key_tokens] (e.g. xformers or classic attn)
      if attention_mask is not None and attention_mask.ndim == 2:
          # assume that mask is expressed as:
          #   (1 = keep,      0 = discard)
          # convert mask into a bias that can be added to attention scores:
          #       (keep = +0,     discard = -10000.0)
          SCREAMING_SNAKE_CASE_													=  (1 - attention_mask.to(hidden_states.dtype)) * -1_0_0_0_0.0
          SCREAMING_SNAKE_CASE_													=  attention_mask.unsqueeze(1)

      # convert encoder_attention_mask to a bias the same way we do for attention_mask
      if encoder_attention_mask is not None and encoder_attention_mask.ndim == 2:
          SCREAMING_SNAKE_CASE_													=  (1 - encoder_attention_mask.to(hidden_states.dtype)) * -1_0_0_0_0.0
          SCREAMING_SNAKE_CASE_													=  encoder_attention_mask.unsqueeze(1)

      # 1. Input
      if self.is_input_continuous:
          SCREAMING_SNAKE_CASE_							,	SCREAMING_SNAKE_CASE_							,	SCREAMING_SNAKE_CASE_							,	SCREAMING_SNAKE_CASE_													=  hidden_states.shape
          SCREAMING_SNAKE_CASE_													=  hidden_states

          SCREAMING_SNAKE_CASE_													=  self.norm(_A)
          if not self.use_linear_projection:
              SCREAMING_SNAKE_CASE_													=  self.proj_in(_A)
              SCREAMING_SNAKE_CASE_													=  hidden_states.shape[1]
              SCREAMING_SNAKE_CASE_													=  hidden_states.permute(0  ,						2  ,						3  ,						1).reshape(_A  ,						height * width  ,						_A)
          else:
              SCREAMING_SNAKE_CASE_													=  hidden_states.shape[1]
              SCREAMING_SNAKE_CASE_													=  hidden_states.permute(0  ,						2  ,						3  ,						1).reshape(_A  ,						height * width  ,						_A)
              SCREAMING_SNAKE_CASE_													=  self.proj_in(_A)
      elif self.is_input_vectorized:
          SCREAMING_SNAKE_CASE_													=  self.latent_image_embedding(_A)
      elif self.is_input_patches:
          SCREAMING_SNAKE_CASE_													=  self.pos_embed(_A)

      # 2. Blocks
      for block in self.transformer_blocks:
          SCREAMING_SNAKE_CASE_													=  block(
              _A  ,						attention_mask=_A  ,						encoder_hidden_states=_A  ,						encoder_attention_mask=_A  ,						timestep=_A  ,						cross_attention_kwargs=_A  ,						class_labels=_A  ,						)

      # 3. Output
      if self.is_input_continuous:
          if not self.use_linear_projection:
              SCREAMING_SNAKE_CASE_													=  hidden_states.reshape(_A  ,						_A  ,						_A  ,						_A).permute(0  ,						3  ,						1  ,						2).contiguous()
              SCREAMING_SNAKE_CASE_													=  self.proj_out(_A)
          else:
              SCREAMING_SNAKE_CASE_													=  self.proj_out(_A)
              SCREAMING_SNAKE_CASE_													=  hidden_states.reshape(_A  ,						_A  ,						_A  ,						_A).permute(0  ,						3  ,						1  ,						2).contiguous()

          SCREAMING_SNAKE_CASE_													=  hidden_states + residual
      elif self.is_input_vectorized:
          SCREAMING_SNAKE_CASE_													=  self.norm_out(_A)
          SCREAMING_SNAKE_CASE_													=  self.out(_A)
          # (batch, self.num_vector_embeds - 1, self.num_latent_pixels)
          SCREAMING_SNAKE_CASE_													=  logits.permute(0  ,						2  ,						1)

          # log(p(x_0))
          SCREAMING_SNAKE_CASE_													=  F.log_softmax(logits.double()  ,						dim=1).float()
      elif self.is_input_patches:
          # TODO: cleanup!
          SCREAMING_SNAKE_CASE_													=  self.transformer_blocks[0].norma.emb(
              _A  ,						_A  ,						hidden_dtype=hidden_states.dtype)
          SCREAMING_SNAKE_CASE_							,	SCREAMING_SNAKE_CASE_													=  self.proj_out_a(F.silu(_A)).chunk(2  ,						dim=1)
          SCREAMING_SNAKE_CASE_													=  self.norm_out(_A) * (1 + scale[:, None]) + shift[:, None]
          SCREAMING_SNAKE_CASE_													=  self.proj_out_a(_A)

          # unpatchify
          SCREAMING_SNAKE_CASE_													=  SCREAMING_SNAKE_CASE_													=  int(hidden_states.shape[1] ** 0.5)
          SCREAMING_SNAKE_CASE_													=  hidden_states.reshape(
              shape=(-1, height, width, self.patch_size, self.patch_size, self.out_channels))
          SCREAMING_SNAKE_CASE_													=  torch.einsum('nhwpqc->nchpwq'  ,						_A)
          SCREAMING_SNAKE_CASE_													=  hidden_states.reshape(
              shape=(-1, self.out_channels, height * self.patch_size, width * self.patch_size))

      if not return_dict:
          return (output,)

      return TransformeraDModelOutput(sample=_A)


