


"""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__      				=   getLogger(__name__)


lowerCAmelCase__      				=   '''cuda''' if torch.cuda.is_available() else '''cpu'''
def 					a__			(				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    :    Union[str, Any]=False				,      SCREAMING_SNAKE_CASE    :    Dict="summarization"				,      SCREAMING_SNAKE_CASE    :    Optional[int]=None				,      **SCREAMING_SNAKE_CASE    :    Optional[Any]				,      ):


  '''simple docstring'''







  lowerCAmelCase     :     Optional[int]								=							Path(SCREAMING_SNAKE_CASE				).open("w"				,      encoding="utf-8"				)
  lowerCAmelCase     :     str								=							str(SCREAMING_SNAKE_CASE				)
  lowerCAmelCase     :     Optional[int]								=							AutoModelForSeqaSeqLM.from_pretrained(SCREAMING_SNAKE_CASE				).to(SCREAMING_SNAKE_CASE				)
  if fpaa:
    lowerCAmelCase     :     Tuple								=							model.half()

  lowerCAmelCase     :     List[Any]								=							AutoTokenizer.from_pretrained(SCREAMING_SNAKE_CASE				)
  logger.info(f"""Inferred tokenizer type: {tokenizer.__class__}"""				)  # if this is wrong, check config.model_type.

  lowerCAmelCase     :     Optional[Any]								=							time.time()
  # update config with task specific params
  use_task_specific_params(SCREAMING_SNAKE_CASE				,      SCREAMING_SNAKE_CASE				)
  if prefix is None:
    lowerCAmelCase     :     List[str]								=							prefix or getattr(model.config				,      "prefix"				,      ""				) or ""
  for examples_chunk in tqdm(list(chunks(SCREAMING_SNAKE_CASE				,      SCREAMING_SNAKE_CASE				)				)				):
    lowerCAmelCase     :     Union[str, Any]								=							[prefix + text for text in examples_chunk]
    lowerCAmelCase     :     Optional[int]								=							tokenizer(SCREAMING_SNAKE_CASE				,      return_tensors="pt"				,      truncation=SCREAMING_SNAKE_CASE				,      padding="longest"				).to(SCREAMING_SNAKE_CASE				)
    lowerCAmelCase     :     Dict								=							model.generate(
        input_ids=batch.input_ids				,      attention_mask=batch.attention_mask				,      **SCREAMING_SNAKE_CASE				,      )
    lowerCAmelCase     :     Union[str, Any]								=							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()
  lowerCAmelCase     :     Tuple								=							int(time.time() - start_time				)  # seconds
  lowerCAmelCase     :     Tuple								=							len(SCREAMING_SNAKE_CASE				)
  return {"n_obs": n_obs, "runtime": runtime, "seconds_per_sample": round(runtime / n_obs				,      4				)}
def 					a__			(				):


  '''simple docstring'''







  return datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S"				)
def 					a__			(				SCREAMING_SNAKE_CASE    :    int=True				):


  '''simple docstring'''







  lowerCAmelCase     :     int								=							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
  lowerCAmelCase						,  lowerCAmelCase     :     Optional[Any]								=							parser.parse_known_args()
  lowerCAmelCase     :     int								=							parse_numeric_n_bool_cl_kwargs(SCREAMING_SNAKE_CASE				)
  if parsed_args and verbose:
    print(f"""parsed the following generate kwargs: {parsed_args}"""				)
  lowerCAmelCase     :     Optional[int]								=							[" " + x.rstrip() if "t5" in args.model_name else x.rstrip() for x in open(args.input_path				).readlines()]
  if args.n_obs > 0:
    lowerCAmelCase     :     Optional[int]								=							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."""				)

  if args.device == "cpu" and args.fpaa:
    # this mix leads to RuntimeError: "threshold_cpu" not implemented for 'Half'
    raise ValueError("Can't mix --fp16 and --device cpu"				)

  lowerCAmelCase     :     Any								=							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
  lowerCAmelCase     :     Dict								=							calculate_bleu if "translation" in args.task else calculate_rouge
  lowerCAmelCase     :     Optional[Any]								=							[x.rstrip() for x in open(args.save_path				).readlines()]
  lowerCAmelCase     :     List[Any]								=							[x.rstrip() for x in open(args.reference_path				).readlines()][: len(SCREAMING_SNAKE_CASE				)]
  lowerCAmelCase     :     dict								=							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:
    lowerCAmelCase     :     Any								=							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)







import copy
import unittest

from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
							import torch

							from transformers import (
							    MODEL_FOR_MULTIPLE_CHOICE_MAPPING,
							    MODEL_FOR_QUESTION_ANSWERING_MAPPING,
							    MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
							    MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
							    LayoutLMvaConfig,
							    LayoutLMvaForQuestionAnswering,
							    LayoutLMvaForSequenceClassification,
							    LayoutLMvaForTokenClassification,
							    LayoutLMvaModel,
							)
							from transformers.models.layoutlmva.modeling_layoutlmva import LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST

if is_vision_available():
							from PIL import Image

							from transformers import LayoutLMvaImageProcessor







class 				lowercase		:




							def __init__(				self   ,A__   ,A__=2   ,A__=3   ,A__=4   ,A__=2   ,A__=7   ,A__=True   ,A__=True   ,A__=True   ,A__=True   ,A__=9_9   ,A__=3_6   ,A__=3   ,A__=4   ,A__=3_7   ,A__="gelu"   ,A__=0.1   ,A__=0.1   ,A__=5_1_2   ,A__=1_6   ,A__=2   ,A__=0.02   ,A__=6   ,A__=6   ,A__=3   ,A__=4   ,A__=None   ,A__=1_0_0_0   ,):
												lowercase							=						parent
												lowercase							=						batch_size
												lowercase							=						num_channels
												lowercase							=						image_size
												lowercase							=						patch_size
												lowercase							=						text_seq_length
												lowercase							=						is_training
												lowercase							=						use_input_mask
												lowercase							=						use_token_type_ids
												lowercase							=						use_labels
												lowercase							=						vocab_size
												lowercase							=						hidden_size
												lowercase							=						num_hidden_layers
												lowercase							=						num_attention_heads
												lowercase							=						intermediate_size
												lowercase							=						hidden_act
												lowercase							=						hidden_dropout_prob
												lowercase							=						attention_probs_dropout_prob
												lowercase							=						max_position_embeddings
												lowercase							=						type_vocab_size
												lowercase							=						type_sequence_label_size
												lowercase							=						initializer_range
												lowercase							=						coordinate_size
												lowercase							=						shape_size
												lowercase							=						num_labels
												lowercase							=						num_choices
												lowercase							=						scope
												lowercase							=						range_bbox

												# LayoutLMv3's sequence length equals the number of text tokens + number of patches + 1 (we add 1 for the CLS token)
												lowercase							=						text_seq_length
												lowercase							=						(image_size // patch_size) ** 2 + 1
												lowercase							=						self.text_seq_length + self.image_seq_length




							def  A__      (				self):
												lowercase							=						ids_tensor([self.batch_size, self.text_seq_length]   ,self.vocab_size)

												lowercase							=						ids_tensor([self.batch_size, self.text_seq_length, 4]   ,self.range_bbox)
												# Ensure that bbox is legal
												for i in range(bbox.shape[0]):
																	for j in range(bbox.shape[1]):
																						if bbox[i, j, 3] < bbox[i, j, 1]:
																											lowercase							=						bbox[i, j, 3]
																											lowercase							=						bbox[i, j, 1]
																											lowercase							=						t
																						if bbox[i, j, 2] < bbox[i, j, 0]:
																											lowercase							=						bbox[i, j, 2]
																											lowercase							=						bbox[i, j, 0]
																											lowercase							=						t

												lowercase							=						floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

												lowercase							=						None
												if self.use_input_mask:
																	lowercase							=						random_attention_mask([self.batch_size, self.text_seq_length])

												lowercase							=						None
												if self.use_token_type_ids:
																	lowercase							=						ids_tensor([self.batch_size, self.text_seq_length]   ,self.type_vocab_size)

												lowercase							=						None
												lowercase							=						None
												if self.use_labels:
																	lowercase							=						ids_tensor([self.batch_size]   ,self.type_sequence_label_size)
																	lowercase							=						ids_tensor([self.batch_size, self.text_seq_length]   ,self.num_labels)

												lowercase							=						LayoutLMvaConfig(
												    vocab_size=self.vocab_size   ,hidden_size=self.hidden_size   ,num_hidden_layers=self.num_hidden_layers   ,num_attention_heads=self.num_attention_heads   ,intermediate_size=self.intermediate_size   ,hidden_act=self.hidden_act   ,hidden_dropout_prob=self.hidden_dropout_prob   ,attention_probs_dropout_prob=self.attention_probs_dropout_prob   ,max_position_embeddings=self.max_position_embeddings   ,type_vocab_size=self.type_vocab_size   ,initializer_range=self.initializer_range   ,coordinate_size=self.coordinate_size   ,shape_size=self.shape_size   ,input_size=self.image_size   ,patch_size=self.patch_size   ,)

												return config, input_ids, bbox, pixel_values, token_type_ids, input_mask, sequence_labels, token_labels




							def  A__      (				self   ,A__   ,A__   ,A__   ,A__   ,A__   ,A__   ,A__   ,A__):
												lowercase							=						LayoutLMvaModel(config=A__)
												model.to(A__)
												model.eval()

												# text + image
												lowercase							=						model(A__   ,pixel_values=A__)
												lowercase							=						model(
												    A__   ,bbox=A__   ,pixel_values=A__   ,attention_mask=A__   ,token_type_ids=A__)
												lowercase							=						model(A__   ,bbox=A__   ,pixel_values=A__   ,token_type_ids=A__)
												lowercase							=						model(A__   ,bbox=A__   ,pixel_values=A__)

												self.parent.assertEqual(result.last_hidden_state.shape   ,(self.batch_size, self.seq_length, self.hidden_size))

												# text only
												lowercase							=						model(A__)
												self.parent.assertEqual(
												    result.last_hidden_state.shape   ,(self.batch_size, self.text_seq_length, self.hidden_size))

												# image only
												lowercase							=						model(pixel_values=A__)
												self.parent.assertEqual(
												    result.last_hidden_state.shape   ,(self.batch_size, self.image_seq_length, self.hidden_size))




							def  A__      (				self   ,A__   ,A__   ,A__   ,A__   ,A__   ,A__   ,A__   ,A__):
												lowercase							=						self.num_labels
												lowercase							=						LayoutLMvaForSequenceClassification(A__)
												model.to(A__)
												model.eval()
												lowercase							=						model(
												    A__   ,bbox=A__   ,pixel_values=A__   ,attention_mask=A__   ,token_type_ids=A__   ,labels=A__   ,)
												self.parent.assertEqual(result.logits.shape   ,(self.batch_size, self.num_labels))




							def  A__      (				self   ,A__   ,A__   ,A__   ,A__   ,A__   ,A__   ,A__   ,A__):
												lowercase							=						self.num_labels
												lowercase							=						LayoutLMvaForTokenClassification(config=A__)
												model.to(A__)
												model.eval()
												lowercase							=						model(
												    A__   ,bbox=A__   ,pixel_values=A__   ,attention_mask=A__   ,token_type_ids=A__   ,labels=A__   ,)
												self.parent.assertEqual(result.logits.shape   ,(self.batch_size, self.text_seq_length, self.num_labels))




							def  A__      (				self   ,A__   ,A__   ,A__   ,A__   ,A__   ,A__   ,A__   ,A__):
												lowercase							=						LayoutLMvaForQuestionAnswering(config=A__)
												model.to(A__)
												model.eval()
												lowercase							=						model(
												    A__   ,bbox=A__   ,pixel_values=A__   ,attention_mask=A__   ,token_type_ids=A__   ,start_positions=A__   ,end_positions=A__   ,)
												self.parent.assertEqual(result.start_logits.shape   ,(self.batch_size, self.seq_length))
												self.parent.assertEqual(result.end_logits.shape   ,(self.batch_size, self.seq_length))





							def  A__      (				self):
												lowercase							=						self.prepare_config_and_inputs()
												(
												    (
												    lowercase
												)	,						(
												    lowercase
												)	,						(
												    lowercase
												)	,						(
												    lowercase
												)	,						(
												    lowercase
												)	,						(
												    lowercase
												)	,						(
												    lowercase
												)	,						(
												    lowercase
												)	,						
												)							=						config_and_inputs
												lowercase							=						{
												    '''input_ids''': input_ids,
												    '''bbox''': bbox,
												    '''pixel_values''': pixel_values,
												    '''token_type_ids''': token_type_ids,
												    '''attention_mask''': input_mask,
												}
												return config, inputs_dict







@require_torch
class 				lowercase		(					SCREAMING_SNAKE_CASE__       ,  SCREAMING_SNAKE_CASE__       ,  unittest.TestCase		):
							lowercase_       :       Dict       					=False
							lowercase_       :       Any       					=False
							lowercase_       :       Tuple       					=False

							lowercase_       :       Any       					=(
							    (
							        LayoutLMvaModel,
							        LayoutLMvaForSequenceClassification,
							        LayoutLMvaForTokenClassification,
							        LayoutLMvaForQuestionAnswering,
							    )
							    if is_torch_available()
							    else ()
							)
							lowercase_       :       int       					=(
							    {'''document-question-answering''': LayoutLMvaForQuestionAnswering, '''feature-extraction''': LayoutLMvaModel}
							    if is_torch_available()
							    else {}
							)




							def  A__      (				self   ,A__   ,A__   ,A__   ,A__   ,A__):
												# `DocumentQuestionAnsweringPipeline` is expected to work with this model, but it combines the text and visual
												# embedding along the sequence dimension (dim 1), which causes an error during post-processing as `p_mask` has
												# the sequence dimension of the text embedding only.
												# (see the line `embedding_output = torch.cat([embedding_output, visual_embeddings], dim=1)`)
												return True




							def  A__      (				self):
												lowercase							=						LayoutLMvaModelTester(self)
												lowercase							=						ConfigTester(self   ,config_class=A__   ,hidden_size=3_7)




							def  A__      (				self   ,A__   ,A__   ,A__=False):
												lowercase							=						copy.deepcopy(A__)
												if model_class in get_values(A__):
																	lowercase							=						{
																	    k: v.unsqueeze(1).expand(-1   ,self.model_tester.num_choices   ,-1).contiguous()
																	    if isinstance(A__   ,torch.Tensor) and v.ndim > 1
																	    else v
																	    for k, v in inputs_dict.items()
																	}
												if return_labels:
																	if model_class in get_values(A__):
																						lowercase							=						torch.ones(self.model_tester.batch_size   ,dtype=torch.long   ,device=A__)
																	elif model_class in get_values(A__):
																						lowercase							=						torch.zeros(
																						    self.model_tester.batch_size   ,dtype=torch.long   ,device=A__)
																						lowercase							=						torch.zeros(
																						    self.model_tester.batch_size   ,dtype=torch.long   ,device=A__)
																	elif model_class in [
																	    *get_values(A__),
																	]:
																						lowercase							=						torch.zeros(
																						    self.model_tester.batch_size   ,dtype=torch.long   ,device=A__)
																	elif model_class in [
																	    *get_values(A__),
																	]:
																						lowercase							=						torch.zeros(
																						    (self.model_tester.batch_size, self.model_tester.text_seq_length)   ,dtype=torch.long   ,device=A__   ,)

												return inputs_dict




							def  A__      (				self):
												self.config_tester.run_common_tests()




							def  A__      (				self):
												lowercase							=						self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_model(*A__)




							def  A__      (				self):
												lowercase							=						self.model_tester.prepare_config_and_inputs()
												for type in ["absolute", "relative_key", "relative_key_query"]:
																	lowercase							=						type
																	self.model_tester.create_and_check_model(*A__)




							def  A__      (				self):
												lowercase							=						self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_for_sequence_classification(*A__)




							def  A__      (				self):
												lowercase							=						self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_for_token_classification(*A__)




							def  A__      (				self):
												lowercase							=						self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_for_question_answering(*A__)





							@slow
							def  A__      (				self):
												for model_name in LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																	lowercase							=						LayoutLMvaModel.from_pretrained(A__)
																	self.assertIsNotNone(A__)



def        UpperCamelCase    (							):
					'''simple docstring'''
					lowercase							=						Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''		)
					return image







@require_torch
class 				lowercase		(					unittest.TestCase		):




							@cached_property
							def  A__      (				self):
												return LayoutLMvaImageProcessor(apply_ocr=A__) if is_vision_available() else None





							@slow
							def  A__      (				self):
												lowercase							=						LayoutLMvaModel.from_pretrained('''microsoft/layoutlmv3-base''').to(A__)

												lowercase							=						self.default_image_processor
												lowercase							=						prepare_img()
												lowercase							=						image_processor(images=A__   ,return_tensors='''pt''').pixel_values.to(A__)

												lowercase							=						torch.tensor([[1, 2]])
												lowercase							=						torch.tensor([[1, 2, 3, 4], [5, 6, 7, 8]]).unsqueeze(0)

												# forward pass
												lowercase							=						model(
												    input_ids=input_ids.to(A__)   ,bbox=bbox.to(A__)   ,pixel_values=pixel_values.to(A__)   ,)

												# verify the logits
												lowercase							=						torch.Size((1, 1_9_9, 7_6_8))
												self.assertEqual(outputs.last_hidden_state.shape   ,A__)

												lowercase							=						torch.tensor(
												    [[-0.0529, 0.3618, 0.1632], [-0.1587, -0.1667, -0.0400], [-0.1557, -0.1671, -0.0505]]).to(A__)

												self.assertTrue(torch.allclose(outputs.last_hidden_state[0, :3, :3]   ,A__   ,atol=1E-4))




