


'''simple docstring'''
from __future__ import annotations

import unittest

import numpy as np

from transformers import BlipTextConfig
from transformers.testing_utils import require_tf, slow
from transformers.utils import is_tf_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask


if is_tf_available():
 import tensorflow as tf

 from transformers import TFBlipTextModel
 from transformers.models.blip.modeling_tf_blip import TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST

class 						_lowerCamelCase					:




 '''simple docstring'''


 def __init__(      self      ,      __lowercase      ,      __lowercase=12      ,      __lowercase=7      ,      __lowercase=True      ,      __lowercase=True      ,      __lowercase=True      ,      __lowercase=99      ,      __lowercase=32      ,      __lowercase=32      ,      __lowercase=2      ,      __lowercase=4      ,      __lowercase=37      ,      __lowercase=0.1      ,      __lowercase=0.1      ,      __lowercase=512      ,      __lowercase=0.0_2      ,      __lowercase=0      ,      __lowercase=None      ,      ):

  """simple docstring"""







  __A  :		List[str]       =			parent
  __A  :		Union[str, Any]       =			batch_size
  __A  :		int       =			seq_length
  __A  :		Any       =			is_training
  __A  :		List[Any]       =			use_input_mask
  __A  :		Tuple       =			use_labels
  __A  :		List[Any]       =			vocab_size
  __A  :		Any       =			hidden_size
  __A  :		Any       =			projection_dim
  __A  :		Dict       =			num_hidden_layers
  __A  :		str       =			num_attention_heads
  __A  :		Union[str, Any]       =			intermediate_size
  __A  :		List[Any]       =			dropout
  __A  :		int       =			attention_dropout
  __A  :		Optional[Any]       =			max_position_embeddings
  __A  :		str       =			initializer_range
  __A  :		Optional[int]       =			scope
  __A  :		Dict       =			bos_token_id


 def  snake_case__   (      self						):

  """simple docstring"""







  __A  :		Union[str, Any]       =			ids_tensor([self.batch_size, self.seq_length]      ,      self.vocab_size						)

  __A  :		List[Any]       =			None
  if self.use_input_mask:
   __A  :		Union[str, Any]       =			random_attention_mask([self.batch_size, self.seq_length]						)

  if input_mask is not None:
   __A  :		Optional[Any]       =			input_mask.numpy()
   __A     ,__A  :		List[str]       =			input_mask.shape
   __A  :		List[str]       =			np.random.randint(1      ,      seq_length - 1      ,      size=(batch_size,)						)
   for batch_idx, start_index in enumerate(__lowercase						):
    __A  :		str       =			1
    __A  :		str       =			0

  __A  :		Optional[Any]       =			self.get_config()

  return config, input_ids, tf.convert_to_tensor(__lowercase						)


 def  snake_case__   (      self						):

  """simple docstring"""







  return BlipTextConfig(
      vocab_size=self.vocab_size      ,      hidden_size=self.hidden_size      ,      projection_dim=self.projection_dim      ,      num_hidden_layers=self.num_hidden_layers      ,      num_attention_heads=self.num_attention_heads      ,      intermediate_size=self.intermediate_size      ,      dropout=self.dropout      ,      attention_dropout=self.attention_dropout      ,      max_position_embeddings=self.max_position_embeddings      ,      initializer_range=self.initializer_range      ,      bos_token_id=self.bos_token_id      ,      )


 def  snake_case__   (      self      ,      __lowercase      ,      __lowercase      ,      __lowercase						):

  """simple docstring"""







  __A  :		Union[str, Any]       =			TFBlipTextModel(config=__lowercase						)
  __A  :		Optional[Any]       =			model(__lowercase      ,      attention_mask=__lowercase      ,      training=__lowercase						)
  __A  :		Optional[int]       =			model(__lowercase      ,      training=__lowercase						)
  self.parent.assertEqual(result.last_hidden_state.shape      ,      (self.batch_size, self.seq_length, self.hidden_size)						)
  self.parent.assertEqual(result.pooler_output.shape      ,      (self.batch_size, self.hidden_size)						)







 def  snake_case__   (      self						):

  """simple docstring"""







  __A  :		Dict       =			self.prepare_config_and_inputs()
  __A     ,__A     ,__A  :		Union[str, Any]       =			config_and_inputs
  __A  :		str       =			{'input_ids': input_ids, 'attention_mask': input_mask}
  return config, inputs_dict


@require_tf
class 						_lowerCamelCase					(    snake_case_    ,		unittest.TestCase				):




 '''simple docstring'''
 __lowercase				:		Optional[Any]    							=						(TFBlipTextModel,) if is_tf_available() else ()
 __lowercase				:		List[Any]    							=						False
 __lowercase				:		Optional[Any]    							=						False
 __lowercase				:		Optional[Any]    							=						False


 def  snake_case__   (      self						):

  """simple docstring"""







  __A  :		Tuple       =			BlipTextModelTester(self						)
  __A  :		str       =			ConfigTester(self      ,      config_class=__lowercase      ,      hidden_size=37						)


 def  snake_case__   (      self						):

  """simple docstring"""







  self.config_tester.run_common_tests()


 def  snake_case__   (      self						):

  """simple docstring"""







  __A  :		List[str]       =			self.model_tester.prepare_config_and_inputs()
  self.model_tester.create_and_check_model(*__lowercase						)


 def  snake_case__   (      self						):

  """simple docstring"""







  pass


 def  snake_case__   (      self						):

  """simple docstring"""







  pass


 @unittest.skip(reason='Blip does not use inputs_embeds'						)
 def  snake_case__   (      self						):

  """simple docstring"""







  pass


 @unittest.skip(reason='BlipTextModel has no base class and is not available in MODEL_MAPPING'						)
 def  snake_case__   (      self						):

  """simple docstring"""







  pass


 @unittest.skip(reason='BlipTextModel has no base class and is not available in MODEL_MAPPING'						)
 def  snake_case__   (      self						):

  """simple docstring"""







  pass


 @slow
 def  snake_case__   (      self						):

  """simple docstring"""







  for model_name in TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
   __A  :		Any       =			TFBlipTextModel.from_pretrained(__lowercase						)
   self.assertIsNotNone(__lowercase						)







 def  snake_case__   (      self      ,      __lowercase=True						):

  """simple docstring"""







  super().test_pt_tf_model_equivalence(allow_missing_keys=__lowercase						)










'''simple docstring'''
import argparse
import shutil
import time
from json import JSONDecodeError
from logging import getLogger
from pathlib import Path
from typing import Dict, List

import torch
from torch.utils.data import DataLoader
from tqdm import tqdm

from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from utils import (
    SeqaSeqDataset,
    calculate_bleu,
    calculate_rouge,
    chunks,
    lmap,
    load_json,
    parse_numeric_n_bool_cl_kwargs,
    save_json,
    use_task_specific_params,
    write_txt_file,
)


UpperCAmelCase_ :     Dict								=      getLogger(__name__)


def 			_lowercase       (				UpperCamelCase__       :					List[Any],				UpperCamelCase__       :					str,				UpperCamelCase__       :					str,				UpperCamelCase__       :					int = 8,				UpperCamelCase__       :					int = 1024,				UpperCamelCase__       :					List[Any]="val",				UpperCamelCase__       :					int=None,				UpperCamelCase__       :					str=False,				UpperCamelCase__       :					int="summarization",				UpperCamelCase__       :					List[Any]=None,				UpperCamelCase__       :					List[Any]=1,				UpperCamelCase__       :					Dict = None,				UpperCamelCase__       :					Optional[int]="",				**UpperCamelCase__       :					str,				):
 __A  :		Dict       =			str(UpperCamelCase__			)
 assert local_rank is not None
 torch.distributed.init_process_group(backend='nccl',				rank=UpperCamelCase__			)

 __A  :		Union[str, Any]       =			Path(UpperCamelCase__			)
 __A  :		Optional[int]       =			save_dir.joinpath(f"""rank_{local_rank}_output.json"""			)
 torch.cuda.set_device(UpperCamelCase__			)
 __A  :		Dict       =			AutoModelForSeqaSeqLM.from_pretrained(UpperCamelCase__			).cuda()
 if fpaa:
  __A  :		Optional[Any]       =			model.half()
 # determine if we need to increase num_beams
 use_task_specific_params(UpperCamelCase__,				UpperCamelCase__			)  # update config with task specific params
 __A  :		Any       =			generate_kwargs.pop('num_beams',				model.config.num_beams			)  # AttributeError risk?
 if num_return_sequences > num_beams:
  __A  :		int       =			num_return_sequences

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

 if max_source_length is None:
  __A  :		Union[str, Any]       =			tokenizer.model_max_length
 if prefix is None:
  __A  :		List[Any]       =			prefix or getattr(model.config,				'prefix',				''			) or ''
 __A  :		Tuple       =			SeqaSeqDataset(
     UpperCamelCase__,				UpperCamelCase__,				UpperCamelCase__,				max_target_length=1024,				type_path=UpperCamelCase__,				n_obs=UpperCamelCase__,				prefix=UpperCamelCase__,				**UpperCamelCase__,				)
 # I set shuffle=True for a more accurate progress bar.
 # If all the longest samples are first, the prog bar estimate is too high at the beginning.
 __A  :		Any       =			ds.make_sortish_sampler(UpperCamelCase__,				distributed=UpperCamelCase__,				add_extra_examples=UpperCamelCase__,				shuffle=UpperCamelCase__			)
 __A  :		Union[str, Any]       =			DataLoader(UpperCamelCase__,				sampler=UpperCamelCase__,				batch_size=UpperCamelCase__,				collate_fn=ds.collate_fn			)
 __A  :		Tuple       =			[]
 for batch in tqdm(UpperCamelCase__			):
  __A  :		Any       =			model.generate(
      input_ids=batch['input_ids'].to(model.device			),				attention_mask=batch['attention_mask'].to(model.device			),				num_return_sequences=UpperCamelCase__,				num_beams=UpperCamelCase__,				**UpperCamelCase__,				)
  __A  :		Dict       =			tokenizer.batch_decode(UpperCamelCase__,				skip_special_tokens=UpperCamelCase__,				clean_up_tokenization_spaces=UpperCamelCase__			)
  __A  :		List[str]       =			batch['ids']
  if num_return_sequences > 1:
   __A  :		str       =			chunks(UpperCamelCase__,				UpperCamelCase__			)  # batch size chunks, each of size num_return_seq
  for i, pred in enumerate(UpperCamelCase__			):
   results.append({'pred': pred, 'id': ids[i].item()}			)
 save_json(UpperCamelCase__,				UpperCamelCase__			)
 return results, sampler.num_replicas


def 			_lowercase       (				):
 __A  :		Optional[Any]       =			argparse.ArgumentParser(
     epilog='Unspecified args like --num_beams=2 --decoder_start_token_id=4 are passed to model.generate'			)
 parser.add_argument('--data_dir',				type=UpperCamelCase__,				help='like cnn_dm/test.source'			)
 parser.add_argument(
     '--model_name',				type=UpperCamelCase__,				help='like facebook/bart-large-cnn,t5-base, etc.',				default='sshleifer/distilbart-xsum-12-3',				)
 parser.add_argument('--save_dir',				type=UpperCamelCase__,				help='where to save',				default='tmp_gen'			)
 parser.add_argument('--max_source_length',				type=UpperCamelCase__,				default=UpperCamelCase__			)
 parser.add_argument(
     '--type_path',				type=UpperCamelCase__,				default='test',				help='which subset to evaluate typically train/val/test'			)
 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(
     '--local_rank',				type=UpperCamelCase__,				default=-1,				required=UpperCamelCase__,				help='should be passed by distributed.launch'			)

 parser.add_argument(
     '--n_obs',				type=UpperCamelCase__,				default=UpperCamelCase__,				required=UpperCamelCase__,				help='How many observations. Defaults to all.'			)
 parser.add_argument(
     '--num_return_sequences',				type=UpperCamelCase__,				default=1,				required=UpperCamelCase__,				help='How many sequences to return'			)
 parser.add_argument(
     '--sync_timeout',				type=UpperCamelCase__,				default=600,				required=UpperCamelCase__,				help='How long should master process wait for other processes to finish.',				)
 parser.add_argument('--src_lang',				type=UpperCamelCase__,				default=UpperCamelCase__,				required=UpperCamelCase__			)
 parser.add_argument('--tgt_lang',				type=UpperCamelCase__,				default=UpperCamelCase__,				required=UpperCamelCase__			)
 parser.add_argument(
     '--prefix',				type=UpperCamelCase__,				required=UpperCamelCase__,				default=UpperCamelCase__,				help='will be added to the begininng of src examples'			)
 parser.add_argument('--fp16',				action='store_true'			)
 parser.add_argument('--debug',				action='store_true'			)
 __A  :		int       =			time.time()
 __A     ,__A  :		int       =			parser.parse_known_args()
 __A  :		List[str]       =			parse_numeric_n_bool_cl_kwargs(UpperCamelCase__			)
 if generate_kwargs and args.local_rank <= 0:
  print(f"""parsed the following generate kwargs: {generate_kwargs}"""			)
 __A  :		List[str]       =			Path(args.save_dir + '_tmp'			)
 Path(UpperCamelCase__			).mkdir(exist_ok=UpperCamelCase__			)  # this handles locking.
 __A  :		Optional[Any]       =			list(json_save_dir.glob('rank_*.json'			)			)
 if intermediate_files:
  raise ValueError(f"""Found files at {json_save_dir} please move or remove them."""			)
  # In theory, a node could finish and save before another node hits this. If this happens, we can address later.
 __A  :		List[str]       =			{}
 if args.src_lang is not None:
  __A  :		Dict       =			args.src_lang
 if args.tgt_lang is not None:
  __A  :		Optional[Any]       =			args.tgt_lang

 Path(args.save_dir			).mkdir(exist_ok=UpperCamelCase__			)
 __A     ,__A  :		List[Any]       =			eval_data_dir(
     args.data_dir,				UpperCamelCase__,				args.model_name,				type_path=args.type_path,				bs=args.bs,				fpaa=args.fpaa,				task=args.task,				local_rank=args.local_rank,				n_obs=args.n_obs,				max_source_length=args.max_source_length,				num_return_sequences=args.num_return_sequences,				prefix=args.prefix,				dataset_kwargs=UpperCamelCase__,				**UpperCamelCase__,				)

 if args.local_rank <= 0:
  __A  :		Tuple       =			Path(args.save_dir			)
  save_dir.mkdir(exist_ok=UpperCamelCase__			)
  __A  :		Dict       =			gather_results_from_each_node(UpperCamelCase__,				UpperCamelCase__,				args.sync_timeout			)
  __A  :		Union[str, Any]       =			combine_partial_results(UpperCamelCase__			)
  if args.num_return_sequences > 1:
   __A  :		Any       =			save_dir.joinpath('pseudolabel_results.json'			)
   print(f"""Saving aggregated results at {save_path}, intermediate in {json_save_dir}/"""			)
   save_json(UpperCamelCase__,				UpperCamelCase__			)
   return
  __A  :		int       =			Path(args.data_dir			).joinpath(args.type_path + '.target'			)
  with open(UpperCamelCase__			) as f:
   __A  :		str       =			[x.rstrip() for x in f.readlines()][: len(UpperCamelCase__			)]

  # Calculate metrics, save metrics,  and save _generations.txt
  __A  :		Tuple       =			'translation' in args.task
  __A  :		Union[str, Any]       =			calculate_bleu if calc_bleu else calculate_rouge
  __A  :		Optional[Any]       =			'bleu' if calc_bleu else 'rouge'
  __A  :		Dict       =			score_fn(UpperCamelCase__,				UpperCamelCase__			)
  __A  :		Any       =			len(UpperCamelCase__			)
  __A  :		Tuple       =			time.time() - start_time
  __A  :		Tuple       =			round(runtime / metrics['n_obs'],				4			)
  __A  :		int       =			num_replicas
  # TODO(@stas00): add whatever metadata to metrics
  __A  :		List[Any]       =			save_dir.joinpath(f"""{args.type_path}_{metric_name}.json"""			)
  save_json(UpperCamelCase__,				UpperCamelCase__,				indent=UpperCamelCase__			)
  print(UpperCamelCase__			)
  write_txt_file(UpperCamelCase__,				save_dir.joinpath(f"""{args.type_path}_generations.txt"""			)			)
  if args.debug:
   write_txt_file(UpperCamelCase__,				save_dir.joinpath(f"""{args.type_path}.target"""			)			)
  else:
   shutil.rmtree(UpperCamelCase__			)


def 			_lowercase       (				UpperCamelCase__       :					List[str]			):
 __A  :		List[str]       =			[]
 for partial_result in partial_results:
  records.extend(UpperCamelCase__			)
 __A  :		List[Any]       =			sorted(UpperCamelCase__,				key=lambda UpperCamelCase__			: x["id"]			)
 __A  :		Dict       =			[x['pred'] for x in records]
 return preds


def 			_lowercase       (				UpperCamelCase__       :					Dict,				UpperCamelCase__       :					List[Any],				UpperCamelCase__       :					int			):
 # WAIT FOR lots of .json files
 __A  :		Dict       =			time.time()
 logger.info('waiting for all nodes to finish'			)
 __A  :		int       =			None
 while (time.time() - start_wait) < timeout:
  __A  :		List[Any]       =			list(save_dir.glob('rank_*.json'			)			)
  if len(UpperCamelCase__			) < num_replicas:
   continue
  try:
   # make sure all json files are fully saved
   __A  :		List[Any]       =			lmap(UpperCamelCase__,				UpperCamelCase__			)
   return json_data
  except JSONDecodeError:
   continue
 else:
  raise TimeoutError('Rank 0 gave up on waiting for other processes'			)
 # Unreachable


if __name__ == "__main__":
 # Usage for MT:
 run_generate()






