


'''simple docstring'''
import unittest

from transformers import TrOCRConfig
from transformers.testing_utils import is_torch_available, require_torch, torch_device

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


if is_torch_available():
   import torch

   from transformers.models.trocr.modeling_trocr import TrOCRDecoder, TrOCRForCausalLM

@require_torch
class 						_lowerCamelCase					:




 '''simple docstring'''


 def __init__(      self      ,      __lowercase      ,      __lowercase=99      ,      __lowercase=13      ,      __lowercase=16      ,      __lowercase=7      ,      __lowercase=True      ,      __lowercase=True      ,      __lowercase=True      ,      __lowercase=False      ,      __lowercase=True      ,      __lowercase=2      ,      __lowercase=32      ,      __lowercase=4      ,      __lowercase=4      ,      __lowercase=30      ,      __lowercase=0      ,      __lowercase=1      ,      __lowercase=2      ,      __lowercase=None      ,      ):

  """simple docstring"""







  __A  :		Optional[Any]       =			parent
  __A  :		List[str]       =			batch_size
  __A  :		str       =			decoder_seq_length
  # For common tests
  __A  :		Any       =			self.decoder_seq_length
  __A  :		Optional[Any]       =			is_training
  __A  :		List[str]       =			use_attention_mask
  __A  :		int       =			use_labels

  __A  :		Optional[Any]       =			vocab_size
  __A  :		Any       =			d_model
  __A  :		Optional[Any]       =			d_model
  __A  :		Optional[Any]       =			decoder_layers
  __A  :		Union[str, Any]       =			decoder_layers
  __A  :		Dict       =			decoder_ffn_dim
  __A  :		int       =			decoder_attention_heads
  __A  :		Any       =			decoder_attention_heads
  __A  :		int       =			eos_token_id
  __A  :		List[str]       =			bos_token_id
  __A  :		List[Any]       =			pad_token_id
  __A  :		List[str]       =			decoder_start_token_id
  __A  :		Dict       =			use_cache
  __A  :		List[Any]       =			max_position_embeddings

  __A  :		Optional[Any]       =			None
  __A  :		Optional[int]       =			decoder_seq_length
  __A  :		Optional[int]       =			2
  __A  :		Union[str, Any]       =			1


 def  snake_case__   (      self						):

  """simple docstring"""







  __A  :		Optional[int]       =			ids_tensor([self.batch_size, self.decoder_seq_length]      ,      self.vocab_size						)

  __A  :		str       =			None
  if self.use_attention_mask:
   __A  :		int       =			ids_tensor([self.batch_size, self.decoder_seq_length]      ,      vocab_size=2						)

  __A  :		Tuple       =			None
  if self.use_labels:
   __A  :		str       =			ids_tensor([self.batch_size, self.decoder_seq_length]      ,      self.vocab_size						)

  __A  :		List[Any]       =			TrOCRConfig(
      vocab_size=self.vocab_size      ,      d_model=self.d_model      ,      decoder_layers=self.decoder_layers      ,      decoder_ffn_dim=self.decoder_ffn_dim      ,      decoder_attention_heads=self.decoder_attention_heads      ,      eos_token_id=self.eos_token_id      ,      bos_token_id=self.bos_token_id      ,      use_cache=self.use_cache      ,      pad_token_id=self.pad_token_id      ,      decoder_start_token_id=self.decoder_start_token_id      ,      max_position_embeddings=self.max_position_embeddings      ,      )

  return (config, input_ids, attention_mask, lm_labels)


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

  """simple docstring"""







  __A  :		Optional[int]       =			True
  __A  :		Dict       =			TrOCRDecoder(config=__lowercase						).to(__lowercase						).eval()
  __A  :		int       =			input_ids[:2]

  input_ids[input_ids == 0] += 1
  # first forward pass
  __A  :		Tuple       =			model(__lowercase      ,      use_cache=__lowercase						)
  __A  :		Tuple       =			model(__lowercase						)
  __A  :		Any       =			model(__lowercase      ,      use_cache=__lowercase						)

  self.parent.assertTrue(len(__lowercase						) == len(__lowercase						)						)
  self.parent.assertTrue(len(__lowercase						) == len(__lowercase						) + 1						)

  __A  :		Any       =			outputs['past_key_values']

  # create hypothetical next token and extent to next_input_ids
  __A  :		List[Any]       =			ids_tensor((2, 1)      ,      config.vocab_size - 1						) + 1

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

  __A  :		Dict       =			model(__lowercase						)['last_hidden_state']
  __A  :		Dict       =			model(__lowercase      ,      past_key_values=__lowercase						)['last_hidden_state']

  # select random slice
  __A  :		Optional[Any]       =			ids_tensor((1,)      ,      output_from_past.shape[-1]						).item()
  __A  :		Dict       =			output_from_no_past[:, next_input_ids.shape[-1] - 1, random_slice_idx].detach()
  __A  :		str       =			output_from_past[:, 0, random_slice_idx].detach()

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







 def  snake_case__   (      self						):

  """simple docstring"""







  __A  :		int       =			self.prepare_config_and_inputs()
  __A  :		int       =			config_and_inputs

  __A  :		List[str]       =			{'input_ids': input_ids, 'attention_mask': attention_mask}
  return config, inputs_dict


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




 '''simple docstring'''
 __lowercase				:		Any    							=						(TrOCRDecoder, TrOCRForCausalLM) if is_torch_available() else ()
 __lowercase				:		Dict    							=						(TrOCRForCausalLM,) if is_torch_available() else ()
 __lowercase				:		Any    							=						{'''text-generation''': TrOCRForCausalLM} if is_torch_available() else {}
 __lowercase				:		str    							=						True
 __lowercase				:		Tuple    							=						False


 def  snake_case__   (      self						):

  """simple docstring"""







  __A  :		Union[str, Any]       =			TrOCRStandaloneDecoderModelTester(self      ,      is_training=__lowercase						)
  __A  :		int       =			ConfigTester(self      ,      config_class=__lowercase						)


 def  snake_case__   (      self						):

  """simple docstring"""







  pass


 def  snake_case__   (      self						):

  """simple docstring"""







  pass


 def  snake_case__   (      self						):

  """simple docstring"""







  pass


 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_decoder_model_past(*__lowercase						)


 def  snake_case__   (      self						):

  """simple docstring"""







  return







 @unittest.skip('The model doesn\'t support left padding'						)  # and it's not used enough to be worth fixing :)
 def  snake_case__   (      self						):

  """simple docstring"""







  pass










'''simple docstring'''
import numpy as np
import torch
from torch.nn import CrossEntropyLoss
from transformers import AutoModelForCausalLM, AutoTokenizer

import datasets
from datasets import logging


UpperCAmelCase_ :     Optional[int]								=      '\\n\n'

UpperCAmelCase_ :     Tuple								=      '\nPerplexity (PPL) is one of the most common metrics for evaluating language models.\nIt is defined as the exponentiated average negative log-likelihood of a sequence.\n\nFor more information, see https://huggingface.co/docs/transformers/perplexity\n'

UpperCAmelCase_ :     str								=      '\nArgs:\n    model_id (str): model used for calculating Perplexity\n            NOTE: Perplexity can only be calculated for causal language models.\n                    This includes models such as gpt2, causal variations of bert,\n                    causal versions of t5, and more (the full list can be found\n                    in the AutoModelForCausalLM documentation here:\n                    https://huggingface.co/docs/transformers/master/en/model_doc/auto#transformers.AutoModelForCausalLM )\n\n    input_texts (list of str): input text, each separate text snippet\n        is one list entry.\n    batch_size (int): the batch size to run texts through the model. Defaults to 16.\n    add_start_token (bool): whether to add the start token to the texts,\n        so the perplexity can include the probability of the first word. Defaults to True.\n    device (str): device to run on, defaults to \'cuda\' when available\nReturns:\n    perplexity: dictionary containing the perplexity scores for the texts\n        in the input list, as well as the mean perplexity. If one of the input texts is\n        longer than the max input length of the model, then it is truncated to the\n        max length for the perplexity computation.\nExamples:\n    Example 1:\n        >>> perplexity = datasets.load_metric("perplexity")\n        >>> input_texts = ["lorem ipsum", "Happy Birthday!", "Bienvenue"]\n        >>> results = perplexity.compute(model_id=\'gpt2\',\n        ...                              add_start_token=False,\n        ...                              input_texts=input_texts) # doctest:+ELLIPSIS\n        >>> print(list(results.keys()))\n        [\'perplexities\', \'mean_perplexity\']\n        >>> print(round(results["mean_perplexity"], 2))\n        78.22\n        >>> print(round(results["perplexities"][0], 2))\n        11.11\n\n    Example 2:\n        >>> perplexity = datasets.load_metric("perplexity")\n        >>> input_texts = datasets.load_dataset("wikitext",\n        ...                                     "wikitext-2-raw-v1",\n        ...                                     split="test")["text"][:50] # doctest:+ELLIPSIS\n        [...]\n        >>> input_texts = [s for s in input_texts if s!=\'\']\n        >>> results = perplexity.compute(model_id=\'gpt2\',\n        ...                              input_texts=input_texts) # doctest:+ELLIPSIS\n        >>> print(list(results.keys()))\n        [\'perplexities\', \'mean_perplexity\']\n        >>> print(round(results["mean_perplexity"], 2))\n        60.35\n        >>> print(round(results["perplexities"][0], 2))\n        81.12\n'

@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION    ,		_KWARGS_DESCRIPTION				)
class 						_lowerCamelCase					(    datasets.Metric				):




 '''simple docstring'''


 def  snake_case__   (      self						):

  """simple docstring"""







  return datasets.MetricInfo(
      description=_DESCRIPTION      ,      citation=_CITATION      ,      inputs_description=_KWARGS_DESCRIPTION      ,      features=datasets.Features(
          {
              'input_texts': datasets.Value('string'						),
          }						)      ,      reference_urls=['https://huggingface.co/docs/transformers/perplexity']      ,      )







 def  snake_case__   (      self      ,      __lowercase      ,      __lowercase      ,      __lowercase = 16      ,      __lowercase = True      ,      __lowercase=None						):

  """simple docstring"""







  if device is not None:
   assert device in ["gpu", "cpu", "cuda"], "device should be either gpu or cpu."
   if device == "gpu":
    __A  :		Optional[Any]       =			'cuda'
  else:
   __A  :		Optional[int]       =			'cuda' if torch.cuda.is_available() else 'cpu'

  __A  :		List[Any]       =			AutoModelForCausalLM.from_pretrained(__lowercase						)
  __A  :		Any       =			model.to(__lowercase						)

  __A  :		List[Any]       =			AutoTokenizer.from_pretrained(__lowercase						)

  # if batch_size > 1 (which generally leads to padding being required), and
  # if there is not an already assigned pad_token, assign an existing
  # special token to also be the padding token
  if tokenizer.pad_token is None and batch_size > 1:
   __A  :		Union[str, Any]       =			list(tokenizer.special_tokens_map_extended.values()						)
   # check that the model already has at least one special token defined
   assert (
       len(__lowercase						) > 0
   ), "If batch_size > 1, model must have at least one special token to use for padding. Please use a different model or set batch_size=1."
   # assign one of the special tokens to also be the pad token
   tokenizer.add_special_tokens({'pad_token': existing_special_tokens[0]}						)

  if add_start_token:
   # leave room for <BOS> token to be added:
   assert (
       tokenizer.bos_token is not None
   ), "Input model must already have a BOS token if using add_start_token=True. Please use a different model, or set add_start_token=False"
   __A  :		Tuple       =			model.config.max_length - 1
  else:
   __A  :		str       =			model.config.max_length

  __A  :		int       =			tokenizer(
      __lowercase      ,      add_special_tokens=__lowercase      ,      padding=__lowercase      ,      truncation=__lowercase      ,      max_length=__lowercase      ,      return_tensors='pt'      ,      return_attention_mask=__lowercase      ,      ).to(__lowercase						)

  __A  :		Union[str, Any]       =			encodings['input_ids']
  __A  :		Optional[int]       =			encodings['attention_mask']

  # check that each input is long enough:
  if add_start_token:
   assert torch.all(torch.ge(attn_masks.sum(1						)      ,      1						)						), "Each input text must be at least one token long."
  else:
   assert torch.all(
       torch.ge(attn_masks.sum(1						)      ,      2						)						), "When add_start_token=False, each input text must be at least two tokens long. Run with add_start_token=True if inputting strings of only one token, and remove all empty input strings."

  __A  :		Union[str, Any]       =			[]
  __A  :		str       =			CrossEntropyLoss(reduction='none'						)

  for start_index in logging.tqdm(range(0      ,      len(__lowercase						)      ,      __lowercase						)						):
   __A  :		str       =			min(start_index + batch_size      ,      len(__lowercase						)						)
   __A  :		Tuple       =			encoded_texts[start_index:end_index]
   __A  :		List[str]       =			attn_masks[start_index:end_index]

   if add_start_token:
    __A  :		str       =			torch.tensor([[tokenizer.bos_token_id]] * encoded_batch.size(dim=0						)						).to(__lowercase						)
    __A  :		str       =			torch.cat([bos_tokens_tensor, encoded_batch]      ,      dim=1						)
    __A  :		Union[str, Any]       =			torch.cat(
        [torch.ones(bos_tokens_tensor.size()      ,      dtype=torch.intaa						).to(__lowercase						), attn_mask]      ,      dim=1						)

   __A  :		Tuple       =			encoded_batch

   with torch.no_grad():
    __A  :		List[str]       =			model(__lowercase      ,      attention_mask=__lowercase						).logits

   __A  :		int       =			out_logits[..., :-1, :].contiguous()
   __A  :		Union[str, Any]       =			labels[..., 1:].contiguous()
   __A  :		List[Any]       =			attn_mask[..., 1:].contiguous()

   __A  :		Tuple       =			torch.expa(
       (loss_fct(shift_logits.transpose(1      ,      2						)      ,      __lowercase						) * shift_attention_mask_batch).sum(1						)
       / shift_attention_mask_batch.sum(1						)						)

   ppls += perplexity_batch.tolist()

  return {"perplexities": ppls, "mean_perplexity": np.mean(__lowercase						)}






