


'''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  :		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  :		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()










'''simple docstring'''
import unittest

from parameterized import parameterized

from transformers import OpenLlamaConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
 import torch

 from transformers import OpenLlamaForCausalLM, OpenLlamaForSequenceClassification, OpenLlamaModel

class 						_lowerCamelCase					:




 '''simple docstring'''


 def __init__(      self      ,      __lowercase      ,      __lowercase=13      ,      __lowercase=7      ,      __lowercase=True      ,      __lowercase=True      ,      __lowercase=False      ,      __lowercase=True      ,      __lowercase=99      ,      __lowercase=32      ,      __lowercase=5      ,      __lowercase=4      ,      __lowercase=37      ,      __lowercase="gelu"      ,      __lowercase=0.1      ,      __lowercase=0.1      ,      __lowercase=512      ,      __lowercase=16      ,      __lowercase=2      ,      __lowercase=0.0_2      ,      __lowercase=3      ,      __lowercase=4      ,      __lowercase=None      ,      ):

  """simple docstring"""







  __A  :		Optional[int]       =			parent
  __A  :		Tuple       =			batch_size
  __A  :		Optional[int]       =			seq_length
  __A  :		Tuple       =			is_training
  __A  :		Optional[Any]       =			use_input_mask
  __A  :		Optional[Any]       =			use_token_type_ids
  __A  :		Optional[int]       =			use_labels
  __A  :		str       =			vocab_size
  __A  :		Dict       =			hidden_size
  __A  :		Tuple       =			num_hidden_layers
  __A  :		Optional[int]       =			num_attention_heads
  __A  :		str       =			intermediate_size
  __A  :		List[Any]       =			hidden_act
  __A  :		List[str]       =			hidden_dropout_prob
  __A  :		int       =			attention_probs_dropout_prob
  __A  :		int       =			max_position_embeddings
  __A  :		int       =			type_vocab_size
  __A  :		int       =			type_sequence_label_size
  __A  :		str       =			initializer_range
  __A  :		str       =			num_labels
  __A  :		str       =			num_choices
  __A  :		Any       =			scope


 def  snake_case__   (      self						):

  """simple docstring"""







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

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

  __A  :		str       =			None
  if self.use_token_type_ids:
   __A  :		List[str]       =			ids_tensor([self.batch_size, self.seq_length]      ,      self.type_vocab_size						)

  __A  :		Union[str, Any]       =			None
  __A  :		Optional[int]       =			None
  __A  :		List[str]       =			None
  if self.use_labels:
   __A  :		str       =			ids_tensor([self.batch_size]      ,      self.type_sequence_label_size						)
   __A  :		List[str]       =			ids_tensor([self.batch_size, self.seq_length]      ,      self.num_labels						)
   __A  :		Union[str, Any]       =			ids_tensor([self.batch_size]      ,      self.num_choices						)

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

  return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels


 def  snake_case__   (      self						):

  """simple docstring"""







  return OpenLlamaConfig(
      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      ,      is_decoder=__lowercase      ,      initializer_range=self.initializer_range      ,      use_stable_embedding=__lowercase      ,      )


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

  """simple docstring"""







  __A  :		List[str]       =			OpenLlamaModel(config=__lowercase						)
  model.to(__lowercase						)
  model.eval()
  __A  :		Any       =			model(__lowercase      ,      attention_mask=__lowercase						)
  __A  :		Dict       =			model(__lowercase						)
  self.parent.assertEqual(result.last_hidden_state.shape      ,      (self.batch_size, self.seq_length, self.hidden_size)						)


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

  """simple docstring"""







  __A  :		List[str]       =			True
  __A  :		int       =			OpenLlamaModel(__lowercase						)
  model.to(__lowercase						)
  model.eval()
  __A  :		List[Any]       =			model(
      __lowercase      ,      attention_mask=__lowercase      ,      encoder_hidden_states=__lowercase      ,      encoder_attention_mask=__lowercase      ,      )
  __A  :		Optional[Any]       =			model(
      __lowercase      ,      attention_mask=__lowercase      ,      encoder_hidden_states=__lowercase      ,      )
  __A  :		Optional[Any]       =			model(__lowercase      ,      attention_mask=__lowercase						)
  self.parent.assertEqual(result.last_hidden_state.shape      ,      (self.batch_size, self.seq_length, self.hidden_size)						)


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

  """simple docstring"""







  __A  :		Dict       =			OpenLlamaForCausalLM(config=__lowercase						)
  model.to(__lowercase						)
  model.eval()
  __A  :		Optional[Any]       =			model(__lowercase      ,      attention_mask=__lowercase      ,      labels=__lowercase						)
  self.parent.assertEqual(result.logits.shape      ,      (self.batch_size, self.seq_length, self.vocab_size)						)


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

  """simple docstring"""







  __A  :		List[Any]       =			True
  __A  :		Optional[int]       =			True
  __A  :		Dict       =			OpenLlamaForCausalLM(config=__lowercase						)
  model.to(__lowercase						)
  model.eval()

  # first forward pass
  __A  :		List[str]       =			model(
      __lowercase      ,      attention_mask=__lowercase      ,      encoder_hidden_states=__lowercase      ,      encoder_attention_mask=__lowercase      ,      use_cache=__lowercase      ,      )
  __A  :		Optional[Any]       =			outputs.past_key_values

  # create hypothetical multiple next token and extent to next_input_ids
  __A  :		Dict       =			ids_tensor((self.batch_size, 3)      ,      config.vocab_size						)
  __A  :		Optional[Any]       =			ids_tensor((self.batch_size, 3)      ,      vocab_size=2						)

  # append to next input_ids and
  __A  :		int       =			torch.cat([input_ids, next_tokens]      ,      dim=-1						)
  __A  :		Dict       =			torch.cat([input_mask, next_mask]      ,      dim=-1						)

  __A  :		Tuple       =			model(
      __lowercase      ,      attention_mask=__lowercase      ,      encoder_hidden_states=__lowercase      ,      encoder_attention_mask=__lowercase      ,      output_hidden_states=__lowercase      ,      )['hidden_states'][0]
  __A  :		Any       =			model(
      __lowercase      ,      attention_mask=__lowercase      ,      encoder_hidden_states=__lowercase      ,      encoder_attention_mask=__lowercase      ,      past_key_values=__lowercase      ,      output_hidden_states=__lowercase      ,      )['hidden_states'][0]

  # select random slice
  __A  :		Any       =			ids_tensor((1,)      ,      output_from_past.shape[-1]						).item()
  __A  :		Union[str, Any]       =			output_from_no_past[:, -3:, random_slice_idx].detach()
  __A  :		int       =			output_from_past[:, :, random_slice_idx].detach()

  self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1]						)

  # test that outputs are equal for slice
  self.parent.assertTrue(torch.allclose(__lowercase      ,      __lowercase      ,      atol=1E-3						)						)







 def  snake_case__   (      self						):

  """simple docstring"""







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


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




 '''simple docstring'''
 __lowercase				:		List[str]    							=						(
     (OpenLlamaModel, OpenLlamaForCausalLM, OpenLlamaForSequenceClassification) if is_torch_available() else ()
 )
 __lowercase				:		List[Any]    							=						(OpenLlamaForCausalLM,) if is_torch_available() else ()
 __lowercase				:		Optional[int]    							=						(
     {
         '''feature-extraction''': OpenLlamaModel,
         '''text-classification''': OpenLlamaForSequenceClassification,
         '''text-generation''': OpenLlamaForCausalLM,
         '''zero-shot''': OpenLlamaForSequenceClassification,
     }
     if is_torch_available()
     else {}
 )
 __lowercase				:		Union[str, Any]    							=						False
 __lowercase				:		Union[str, Any]    							=						False


 def  snake_case__   (      self						):

  """simple docstring"""







  __A  :		Optional[int]       =			OpenLlamaModelTester(self						)
  __A  :		List[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  :		int       =			self.model_tester.prepare_config_and_inputs()
  self.model_tester.create_and_check_model(*__lowercase						)


 def  snake_case__   (      self						):

  """simple docstring"""







  __A  :		int       =			self.model_tester.prepare_config_and_inputs()
  for type in ["absolute", "relative_key", "relative_key_query"]:
   __A  :		List[str]       =			type
   self.model_tester.create_and_check_model(*__lowercase						)


 def  snake_case__   (      self						):

  """simple docstring"""







  __A     ,__A  :		Optional[Any]       =			self.model_tester.prepare_config_and_inputs_for_common()
  __A  :		Union[str, Any]       =			3
  __A  :		int       =			input_dict['input_ids']
  __A  :		int       =			input_ids.ne(1						).to(__lowercase						)
  __A  :		Optional[int]       =			ids_tensor([self.model_tester.batch_size]      ,      self.model_tester.type_sequence_label_size						)
  __A  :		Optional[Any]       =			OpenLlamaForSequenceClassification(__lowercase						)
  model.to(__lowercase						)
  model.eval()
  __A  :		Optional[int]       =			model(__lowercase      ,      attention_mask=__lowercase      ,      labels=__lowercase						)
  self.assertEqual(result.logits.shape      ,      (self.model_tester.batch_size, self.model_tester.num_labels)						)


 def  snake_case__   (      self						):

  """simple docstring"""







  __A     ,__A  :		str       =			self.model_tester.prepare_config_and_inputs_for_common()
  __A  :		Any       =			3
  __A  :		List[str]       =			'single_label_classification'
  __A  :		Dict       =			input_dict['input_ids']
  __A  :		Dict       =			input_ids.ne(1						).to(__lowercase						)
  __A  :		List[str]       =			ids_tensor([self.model_tester.batch_size]      ,      self.model_tester.type_sequence_label_size						)
  __A  :		Union[str, Any]       =			OpenLlamaForSequenceClassification(__lowercase						)
  model.to(__lowercase						)
  model.eval()
  __A  :		Dict       =			model(__lowercase      ,      attention_mask=__lowercase      ,      labels=__lowercase						)
  self.assertEqual(result.logits.shape      ,      (self.model_tester.batch_size, self.model_tester.num_labels)						)


 def  snake_case__   (      self						):

  """simple docstring"""







  __A     ,__A  :		Optional[int]       =			self.model_tester.prepare_config_and_inputs_for_common()
  __A  :		str       =			3
  __A  :		int       =			'multi_label_classification'
  __A  :		Union[str, Any]       =			input_dict['input_ids']
  __A  :		str       =			input_ids.ne(1						).to(__lowercase						)
  __A  :		Union[str, Any]       =			ids_tensor(
      [self.model_tester.batch_size, config.num_labels]      ,      self.model_tester.type_sequence_label_size						).to(torch.float						)
  __A  :		int       =			OpenLlamaForSequenceClassification(__lowercase						)
  model.to(__lowercase						)
  model.eval()
  __A  :		Any       =			model(__lowercase      ,      attention_mask=__lowercase      ,      labels=__lowercase						)
  self.assertEqual(result.logits.shape      ,      (self.model_tester.batch_size, self.model_tester.num_labels)						)


 @unittest.skip('Open-Llama buffers include complex numbers, which breaks this test'						)
 def  snake_case__   (      self						):

  """simple docstring"""







  pass







 @parameterized.expand([('linear',), ('dynamic',)]						)
 def  snake_case__   (      self      ,      __lowercase						):

  """simple docstring"""







  __A     ,__A  :		str       =			self.model_tester.prepare_config_and_inputs_for_common()
  __A  :		List[Any]       =			ids_tensor([1, 10]      ,      config.vocab_size						)
  __A  :		str       =			ids_tensor([1, int(config.max_position_embeddings * 1.5						)]      ,      config.vocab_size						)

  set_seed(42						)  # Fixed seed at init time so the two models get the same random weights
  __A  :		Union[str, Any]       =			OpenLlamaModel(__lowercase						)
  original_model.to(__lowercase						)
  original_model.eval()
  __A  :		Optional[int]       =			original_model(__lowercase						).last_hidden_state
  __A  :		int       =			original_model(__lowercase						).last_hidden_state

  set_seed(42						)  # Fixed seed at init time so the two models get the same random weights
  __A  :		List[Any]       =			{'type': scaling_type, 'factor': 1_0.0}
  __A  :		str       =			OpenLlamaModel(__lowercase						)
  scaled_model.to(__lowercase						)
  scaled_model.eval()
  __A  :		Dict       =			scaled_model(__lowercase						).last_hidden_state
  __A  :		List[str]       =			scaled_model(__lowercase						).last_hidden_state

  # Dynamic scaling does not change the RoPE embeddings until it receives an input longer than the original
  # maximum sequence length, so the outputs for the short input should match.
  if scaling_type == "dynamic":
   self.assertTrue(torch.allclose(__lowercase      ,      __lowercase      ,      atol=1E-5						)						)
  else:
   self.assertFalse(torch.allclose(__lowercase      ,      __lowercase      ,      atol=1E-5						)						)

  # The output should be different for long inputs
  self.assertFalse(torch.allclose(__lowercase      ,      __lowercase      ,      atol=1E-5						)						)






