'''simple docstring'''




import logging

import torch
from accelerate import Accelerator
from arguments import EvaluationArguments
from datasets import load_dataset
from torch.utils.data import IterableDataset
from torch.utils.data.dataloader import DataLoader

from transformers import AutoModelForCausalLM, AutoTokenizer, HfArgumentParser, set_seed


class       UpperCAmelCase__  (       _snake_case			):






   """simple docstring"""
   def __init__(self       ,				_a       ,				_a       ,				_a=1_024       ,				_a=1_024       ,				_a=3.6				)    ->     int:
        lowercase_					:       Dict     				=					tokenizer
        lowercase_					:       Optional[Any]     				=					tokenizer.bos_token_id
        lowercase_					:       Dict     				=					dataset
        lowercase_					:       Union[str, Any]     				=					seq_length
        lowercase_					:       List[str]     				=					seq_length * chars_per_token * num_of_sequences





   def __iter__(self				)    ->     Optional[Any]:
        lowercase_					:       str     				=					iter(self.dataset				)
        lowercase_					:       Union[str, Any]     				=					True
        while more_examples:
             lowercase_				,lowercase_					:       Optional[int]     				=					[], 0
             while True:
                  if buffer_len >= self.input_characters:
                       break
                  try:
                       buffer.append(next(_a				)['content']				)
                       buffer_len += len(buffer[-1]				)
                  except StopIteration:
                       lowercase_					:       Any     				=					False
                       break
             lowercase_					:       str     				=					tokenizer(_a       ,				truncation=_a				)['input_ids']
             lowercase_					:       int     				=					[]
             for tokenized_input in tokenized_inputs:
                  all_token_ids.extend(tokenized_input + [self.concat_token_id]				)
             for i in range(0       ,				len(_a				)       ,				self.seq_length				):
                  lowercase_					:       Dict     				=					all_token_ids[i : i + self.seq_length]
                  if len(_a				) == self.seq_length:
                       yield torch.tensor(_a				)



def _UpperCamelCase   (       SCREAMING_SNAKE_CASE_							):
     lowercase_					:       Union[str, Any]     				=					{'streaming': True}
     lowercase_					:       Union[str, Any]     				=					load_dataset(args.dataset_name  ,       split='train'  ,       **SCREAMING_SNAKE_CASE_							)
     lowercase_					:       Dict     				=					ConstantLengthDataset(SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_  ,       seq_length=args.seq_length							)
     lowercase_					:       Optional[Any]     				=					DataLoader(SCREAMING_SNAKE_CASE_  ,       batch_size=args.batch_size							)
     return eval_dataloader

def _UpperCamelCase   (       SCREAMING_SNAKE_CASE_							):
     model.eval()
     lowercase_					:       List[Any]     				=					[]
     for step, batch in enumerate(SCREAMING_SNAKE_CASE_							):
          with torch.no_grad():
               lowercase_					:       Union[str, Any]     				=					model(SCREAMING_SNAKE_CASE_  ,       labels=SCREAMING_SNAKE_CASE_							)
          lowercase_					:       int     				=					outputs.loss.repeat(args.batch_size							)
          losses.append(accelerator.gather(SCREAMING_SNAKE_CASE_							)							)

          if args.max_eval_steps > 0 and step >= args.max_eval_steps:
               break
     lowercase_					:       Dict     				=					torch.mean(torch.cat(SCREAMING_SNAKE_CASE_							)							)
     try:
          lowercase_					:       str     				=					torch.exp(SCREAMING_SNAKE_CASE_							)
     except OverflowError:
          lowercase_					:       str     				=					float('inf'							)
     return loss.item(), perplexity.item()


# Setup Accelerator
_A								=   Accelerator()

# Parse configuration
_A								=   HfArgumentParser(EvaluationArguments)
_A								=   parser.parse_args()
set_seed(args.seed)

# Logging
_A								=   logging.getLogger(__name__)
logging.basicConfig(
    format='%(asctime)s - %(levelname)s - %(name)s - %(message)s', datefmt='%m/%d/%Y %H:%M:%S', level=logging.INFO
)

# Load model and tokenizer
_A								=   AutoModelForCausalLM.from_pretrained(args.model_ckpt)
_A								=   AutoTokenizer.from_pretrained(args.model_ckpt)

# Load dataset and dataloader
_A								=   create_dataloader(args)

# Prepare everything with our `accelerator`.
_A				, _A								=   accelerator.prepare(model, eval_dataloader)

# Evaluate and save the last checkpoint
logger.info('Evaluating and saving model after training')
_A				, _A								=   evaluate(args)






logger.info(F"""loss/eval: {eval_loss}, perplexity: {perplexity}""")





'''simple docstring'''




import numpy as np

from transformers import BatchFeature
from transformers.testing_utils import require_tf, require_torch

from .test_feature_extraction_common import FeatureExtractionSavingTestMixin


class       UpperCAmelCase__  (       _snake_case			):






   """simple docstring"""
   # to overwrite at feature extractactor specific tests
   A							:       List[str]     =    None
   A							:       Tuple     =    None
   @property
   def 		_lowerCamelCase (self				)    ->     Any:
        return self.feat_extract_tester.prepare_feat_extract_dict()
   def 		_lowerCamelCase (self				)    ->     str:
        lowercase_					:       Dict     				=					self.feature_extraction_class(**self.feat_extract_dict				)
        self.assertTrue(hasattr(_a       ,				'feature_size'				)				)
        self.assertTrue(hasattr(_a       ,				'sampling_rate'				)				)
        self.assertTrue(hasattr(_a       ,				'padding_value'				)				)
   def 		_lowerCamelCase (self				)    ->     Dict:
        lowercase_					:       List[str]     				=					self.feat_extract_tester.prepare_inputs_for_common()
        lowercase_					:       Union[str, Any]     				=					self.feature_extraction_class(**self.feat_extract_dict				)
        lowercase_					:       Union[str, Any]     				=					feat_extract.model_input_names[0]

        lowercase_					:       Union[str, Any]     				=					BatchFeature({input_name: speech_inputs}				)

        self.assertTrue(all(len(_a				) == len(_a				) for x, y in zip(_a       ,				processed_features[input_name]				)				)				)

        lowercase_					:       Optional[int]     				=					self.feat_extract_tester.prepare_inputs_for_common(equal_length=_a				)
        lowercase_					:       Tuple     				=					BatchFeature({input_name: speech_inputs}       ,				tensor_type='np'				)

        lowercase_					:       Dict     				=					processed_features[input_name]

        if len(batch_features_input.shape				) < 3:
             lowercase_					:       Optional[Any]     				=					batch_features_input[:, :, None]

        self.assertTrue(
            batch_features_input.shape
            == (self.feat_extract_tester.batch_size, len(speech_inputs[0]				), self.feat_extract_tester.feature_size)				)
   @require_torch
   def 		_lowerCamelCase (self				)    ->     Tuple:
        lowercase_					:       List[Any]     				=					self.feat_extract_tester.prepare_inputs_for_common(equal_length=_a				)
        lowercase_					:       Any     				=					self.feature_extraction_class(**self.feat_extract_dict				)
        lowercase_					:       int     				=					feat_extract.model_input_names[0]

        lowercase_					:       Union[str, Any]     				=					BatchFeature({input_name: speech_inputs}       ,				tensor_type='pt'				)

        lowercase_					:       Tuple     				=					processed_features[input_name]

        if len(batch_features_input.shape				) < 3:
             lowercase_					:       List[str]     				=					batch_features_input[:, :, None]

        self.assertTrue(
            batch_features_input.shape
            == (self.feat_extract_tester.batch_size, len(speech_inputs[0]				), self.feat_extract_tester.feature_size)				)
   @require_tf
   def 		_lowerCamelCase (self				)    ->     Union[str, Any]:
        lowercase_					:       List[Any]     				=					self.feat_extract_tester.prepare_inputs_for_common(equal_length=_a				)
        lowercase_					:       int     				=					self.feature_extraction_class(**self.feat_extract_dict				)
        lowercase_					:       Dict     				=					feat_extract.model_input_names[0]

        lowercase_					:       List[Any]     				=					BatchFeature({input_name: speech_inputs}       ,				tensor_type='tf'				)

        lowercase_					:       int     				=					processed_features[input_name]

        if len(batch_features_input.shape				) < 3:
             lowercase_					:       Union[str, Any]     				=					batch_features_input[:, :, None]

        self.assertTrue(
            batch_features_input.shape
            == (self.feat_extract_tester.batch_size, len(speech_inputs[0]				), self.feat_extract_tester.feature_size)				)
   def 		_lowerCamelCase (self       ,				_a=False				)    ->     Union[str, Any]:
        def _inputs_have_equal_length(_a				):
             lowercase_					:       Optional[Any]     				=					len(input[0]				)
             for input_slice in input[1:]:
                  if len(_a				) != length:
                       return False
             return True

        def _inputs_are_equal(_a       ,				_a				):
             if len(_a				) != len(_a				):
                  return False

             for input_slice_a, input_slice_a in zip(_a       ,				_a				):
                  if not np.allclose(np.asarray(_a				)       ,				np.asarray(_a				)       ,				atol=1e-3				):
                       return False
             return True

        lowercase_					:       int     				=					self.feature_extraction_class(**self.feat_extract_dict				)
        lowercase_					:       str     				=					self.feat_extract_tester.prepare_inputs_for_common(numpify=_a				)
        lowercase_					:       Union[str, Any]     				=					feat_extract.model_input_names[0]

        lowercase_					:       Union[str, Any]     				=					BatchFeature({input_name: speech_inputs}				)

        lowercase_					:       Optional[Any]     				=					self.feat_extract_tester.seq_length_diff
        lowercase_					:       Union[str, Any]     				=					self.feat_extract_tester.max_seq_length + pad_diff
        lowercase_					:       str     				=					self.feat_extract_tester.min_seq_length
        lowercase_					:       Optional[Any]     				=					self.feat_extract_tester.batch_size
        lowercase_					:       Any     				=					self.feat_extract_tester.feature_size

        # test padding for List[int] + numpy
        lowercase_					:       List[str]     				=					feat_extract.pad(_a       ,				padding=_a				)
        lowercase_					:       Any     				=					input_a[input_name]

        lowercase_					:       str     				=					feat_extract.pad(_a       ,				padding='longest'				)
        lowercase_					:       List[str]     				=					input_a[input_name]

        lowercase_					:       Dict     				=					feat_extract.pad(_a       ,				padding='max_length'       ,				max_length=len(speech_inputs[-1]				)				)
        lowercase_					:       Any     				=					input_a[input_name]

        lowercase_					:       Any     				=					feat_extract.pad(_a       ,				padding='longest'       ,				return_tensors='np'				)
        lowercase_					:       str     				=					input_a[input_name]

        # max_length parameter has to be provided when setting `padding="max_length"`
        with self.assertRaises(_a				):
             feat_extract.pad(_a       ,				padding='max_length'				)[input_name]

        lowercase_					:       Union[str, Any]     				=					feat_extract.pad(
            _a       ,				padding='max_length'       ,				max_length=_a       ,				return_tensors='np'				)
        lowercase_					:       Any     				=					input_a[input_name]

        self.assertFalse(_inputs_have_equal_length(_a				)				)
        self.assertTrue(_inputs_have_equal_length(_a				)				)
        self.assertTrue(_inputs_have_equal_length(_a				)				)
        self.assertTrue(_inputs_are_equal(_a       ,				_a				)				)
        self.assertTrue(len(input_a[0]				) == pad_min_length				)
        self.assertTrue(len(input_a[1]				) == pad_min_length + pad_diff				)
        self.assertTrue(input_a.shape[:2] == (batch_size, len(input_a[0]				))				)
        self.assertTrue(input_a.shape[:2] == (batch_size, pad_max_length)				)

        if feature_size > 1:
             self.assertTrue(input_a.shape[2] == input_a.shape[2] == feature_size				)

        # test padding for `pad_to_multiple_of` for List[int] + numpy
        lowercase_					:       Tuple     				=					feat_extract.pad(_a       ,				pad_to_multiple_of=10				)
        lowercase_					:       Dict     				=					input_a[input_name]

        lowercase_					:       int     				=					feat_extract.pad(_a       ,				padding='longest'       ,				pad_to_multiple_of=10				)
        lowercase_					:       Union[str, Any]     				=					input_a[input_name]

        lowercase_					:       Optional[int]     				=					feat_extract.pad(
            _a       ,				padding='max_length'       ,				pad_to_multiple_of=10       ,				max_length=_a				)
        lowercase_					:       Tuple     				=					input_a[input_name]

        lowercase_					:       Optional[int]     				=					feat_extract.pad(
            _a       ,				padding='max_length'       ,				pad_to_multiple_of=10       ,				max_length=_a       ,				return_tensors='np'       ,				)
        lowercase_					:       Union[str, Any]     				=					input_a[input_name]

        self.assertTrue(all(len(_a				) % 10 == 0 for x in input_a				)				)
        self.assertTrue(_inputs_are_equal(_a       ,				_a				)				)

        lowercase_					:       str     				=					pad_max_length if pad_max_length % 10 == 0 else (pad_max_length // 10 + 1) * 10
        self.assertTrue(all(len(_a				) == expected_mult_pad_length for x in input_a				)				)
        self.assertEqual(input_a.shape[:2]       ,				(batch_size, expected_mult_pad_length)				)

        if feature_size > 1:
             self.assertTrue(input_a.shape[2] == feature_size				)

        # Check padding value is correct
        lowercase_					:       Optional[int]     				=					(np.ones(self.feat_extract_tester.feature_size				) * feat_extract.padding_value).sum()
        self.assertTrue(
            abs(np.asarray(input_a[0]				)[pad_min_length:].sum() - padding_vector_sum * (pad_max_length - pad_min_length)				)
            < 1e-3				)
        self.assertTrue(
            abs(
                np.asarray(input_a[1]				)[pad_min_length + pad_diff :].sum()
                - padding_vector_sum * (pad_max_length - pad_min_length - pad_diff)				)
            < 1e-3				)
        self.assertTrue(
            abs(
                np.asarray(input_a[2]				)[pad_min_length + 2 * pad_diff :].sum()
                - padding_vector_sum * (pad_max_length - pad_min_length - 2 * pad_diff)				)
            < 1e-3				)
        self.assertTrue(
            abs(input_a[0, pad_min_length:].sum() - padding_vector_sum * (pad_max_length - pad_min_length)				) < 1e-3				)
        self.assertTrue(
            abs(input_a[0, pad_min_length:].sum() - padding_vector_sum * (expected_mult_pad_length - pad_min_length)				)
            < 1e-3				)
   def 		_lowerCamelCase (self       ,				_a=False				)    ->     Union[str, Any]:
        def _inputs_have_equal_length(_a				):
             lowercase_					:       str     				=					len(input[0]				)
             for input_slice in input[1:]:
                  if len(_a				) != length:
                       return False
             return True

        def _inputs_are_equal(_a       ,				_a				):
             if len(_a				) != len(_a				):
                  return False

             for input_slice_a, input_slice_a in zip(_a       ,				_a				):
                  if not np.allclose(np.asarray(_a				)       ,				np.asarray(_a				)       ,				atol=1e-3				):
                       return False
             return True

        lowercase_					:       Any     				=					self.feature_extraction_class(**self.feat_extract_dict				)
        lowercase_					:       List[Any]     				=					self.feat_extract_tester.prepare_inputs_for_common(numpify=_a				)
        lowercase_					:       List[str]     				=					feat_extract.model_input_names[0]

        lowercase_					:       str     				=					BatchFeature({input_name: speech_inputs}				)

        # truncate to smallest
        lowercase_					:       Union[str, Any]     				=					feat_extract.pad(
            _a       ,				padding='max_length'       ,				max_length=len(speech_inputs[0]				)       ,				truncation=_a				)
        lowercase_					:       List[str]     				=					input_a[input_name]

        lowercase_					:       Optional[int]     				=					feat_extract.pad(_a       ,				padding='max_length'       ,				max_length=len(speech_inputs[0]				)				)
        lowercase_					:       Union[str, Any]     				=					input_a[input_name]

        self.assertTrue(_inputs_have_equal_length(_a				)				)
        self.assertFalse(_inputs_have_equal_length(_a				)				)

        # truncate to smallest with np
        lowercase_					:       List[str]     				=					feat_extract.pad(
            _a       ,				padding='max_length'       ,				max_length=len(speech_inputs[0]				)       ,				return_tensors='np'       ,				truncation=_a       ,				)
        lowercase_					:       List[str]     				=					input_a[input_name]

        lowercase_					:       List[Any]     				=					feat_extract.pad(
            _a       ,				padding='max_length'       ,				max_length=len(speech_inputs[0]				)       ,				return_tensors='np'				)
        lowercase_					:       List[Any]     				=					input_a[input_name]

        self.assertTrue(_inputs_have_equal_length(_a				)				)
        self.assertTrue(input_a.shape[1] == len(speech_inputs[0]				)				)

        # since truncation forces padding to be smaller than longest input
        # function can't return `np.ndarray`, but has to return list
        self.assertFalse(_inputs_have_equal_length(_a				)				)

        # truncate to middle
        lowercase_					:       int     				=					feat_extract.pad(
            _a       ,				padding='max_length'       ,				max_length=len(speech_inputs[1]				)       ,				truncation=_a       ,				return_tensors='np'       ,				)
        lowercase_					:       Union[str, Any]     				=					input_a[input_name]

        lowercase_					:       str     				=					feat_extract.pad(
            _a       ,				padding='max_length'       ,				max_length=len(speech_inputs[1]				)       ,				truncation=_a				)
        lowercase_					:       int     				=					input_a[input_name]

        lowercase_					:       Dict     				=					feat_extract.pad(
            _a       ,				padding='max_length'       ,				max_length=len(speech_inputs[1]				)       ,				return_tensors='np'				)
        lowercase_					:       Optional[int]     				=					input_a[input_name]

        self.assertTrue(input_a.shape[1] == len(speech_inputs[1]				)				)
        self.assertTrue(_inputs_have_equal_length(_a				)				)
        self.assertTrue(_inputs_have_equal_length(_a				)				)
        self.assertTrue(_inputs_are_equal(_a       ,				_a				)				)

        # since truncation forces padding to be smaller than longest input
        # function can't return `np.ndarray`, but has to return list
        self.assertFalse(_inputs_have_equal_length(_a				)				)
        self.assertTrue(len(input_a[-1]				) == len(speech_inputs[-1]				)				)

        # padding has to be max_length when setting `truncation=True`
        with self.assertRaises(_a				):
             feat_extract.pad(_a       ,				truncation=_a				)[input_name]

        # padding has to be max_length when setting `truncation=True`
        with self.assertRaises(_a				):
             feat_extract.pad(_a       ,				padding='longest'       ,				truncation=_a				)[input_name]

        # padding has to be max_length when setting `truncation=True`
        with self.assertRaises(_a				):
             feat_extract.pad(_a       ,				padding='longest'       ,				truncation=_a				)[input_name]

        # max_length parameter has to be provided when setting `truncation=True` and padding="max_length"
        with self.assertRaises(_a				):
             feat_extract.pad(_a       ,				padding='max_length'       ,				truncation=_a				)[input_name]

        # test truncation for `pad_to_multiple_of` for List[int] + numpy
        lowercase_					:       str     				=					12
        lowercase_					:       List[Any]     				=					feat_extract.pad(
            _a       ,				padding='max_length'       ,				max_length=len(speech_inputs[0]				)       ,				pad_to_multiple_of=_a       ,				truncation=_a       ,				)
        lowercase_					:       Any     				=					input_a[input_name]

        lowercase_					:       List[Any]     				=					feat_extract.pad(
            _a       ,				padding='max_length'       ,				max_length=len(speech_inputs[0]				)       ,				pad_to_multiple_of=_a       ,				)
        lowercase_					:       str     				=					input_a[input_name]

        # retrieve expected_length as multiple of pad_to_multiple_of
        lowercase_					:       Dict     				=					len(speech_inputs[0]				)
        if expected_length % pad_to_multiple_of != 0:
             lowercase_					:       List[Any]     				=					((len(speech_inputs[0]				) // pad_to_multiple_of) + 1) * pad_to_multiple_of

        self.assertTrue(len(input_a[0]				) == expected_length				)
        self.assertTrue(_inputs_have_equal_length(_a				)				)
        self.assertFalse(_inputs_have_equal_length(_a				)				)
   def 		_lowerCamelCase (self				)    ->     str:
        self._check_padding(numpify=_a				)
   def 		_lowerCamelCase (self				)    ->     Optional[int]:
        self._check_padding(numpify=_a				)
   def 		_lowerCamelCase (self				)    ->     Any:
        self._check_truncation(numpify=_a				)
   def 		_lowerCamelCase (self				)    ->     Any:
        self._check_truncation(numpify=_a				)
   @require_torch
   def 		_lowerCamelCase (self				)    ->     int:
        lowercase_					:       Tuple     				=					self.feature_extraction_class(**self.feat_extract_dict				)
        lowercase_					:       List[Any]     				=					self.feat_extract_tester.prepare_inputs_for_common()
        lowercase_					:       Optional[Any]     				=					feat_extract.model_input_names[0]

        lowercase_					:       Dict     				=					BatchFeature({input_name: speech_inputs}				)

        lowercase_					:       Tuple     				=					feat_extract.pad(_a       ,				padding='longest'       ,				return_tensors='np'				)[input_name]
        lowercase_					:       List[Any]     				=					feat_extract.pad(_a       ,				padding='longest'       ,				return_tensors='pt'				)[input_name]

        self.assertTrue(abs(input_np.astype(np.floataa				).sum() - input_pt.numpy().astype(np.floataa				).sum()				) < 1e-2				)
   @require_tf
   def 		_lowerCamelCase (self				)    ->     List[Any]:
        lowercase_					:       Any     				=					self.feature_extraction_class(**self.feat_extract_dict				)
        lowercase_					:       Optional[int]     				=					self.feat_extract_tester.prepare_inputs_for_common()
        lowercase_					:       List[Any]     				=					feat_extract.model_input_names[0]

        lowercase_					:       Tuple     				=					BatchFeature({input_name: speech_inputs}				)

        lowercase_					:       List[Any]     				=					feat_extract.pad(_a       ,				padding='longest'       ,				return_tensors='np'				)[input_name]
        lowercase_					:       Optional[int]     				=					feat_extract.pad(_a       ,				padding='longest'       ,				return_tensors='tf'				)[input_name]

        self.assertTrue(abs(input_np.astype(np.floataa				).sum() - input_tf.numpy().astype(np.floataa				).sum()				) < 1e-2				)
   def 		_lowerCamelCase (self				)    ->     List[str]:
        lowercase_					:       Any     				=					self.feat_extract_dict
        lowercase_					:       List[Any]     				=					True
        lowercase_					:       Tuple     				=					self.feature_extraction_class(**_a				)
        lowercase_					:       Optional[Any]     				=					self.feat_extract_tester.prepare_inputs_for_common()
        lowercase_					:       str     				=					[len(_a				) for x in speech_inputs]
        lowercase_					:       Union[str, Any]     				=					feat_extract.model_input_names[0]

        lowercase_					:       List[Any]     				=					BatchFeature({input_name: speech_inputs}				)

        lowercase_					:       str     				=					feat_extract.pad(_a       ,				padding='longest'       ,				return_tensors='np'				)
        self.assertIn('attention_mask'       ,				_a				)
        self.assertListEqual(list(processed.attention_mask.shape				)       ,				list(processed[input_name].shape[:2]				)				)
        self.assertListEqual(processed.attention_mask.sum(-1				).tolist()       ,				_a				)





   def 		_lowerCamelCase (self				)    ->     List[Any]:
        lowercase_					:       Union[str, Any]     				=					self.feat_extract_dict
        lowercase_					:       List[Any]     				=					True
        lowercase_					:       List[Any]     				=					self.feature_extraction_class(**_a				)
        lowercase_					:       List[Any]     				=					self.feat_extract_tester.prepare_inputs_for_common()
        lowercase_					:       Optional[int]     				=					[len(_a				) for x in speech_inputs]
        lowercase_					:       int     				=					feat_extract.model_input_names[0]

        lowercase_					:       Tuple     				=					BatchFeature({input_name: speech_inputs}				)
        lowercase_					:       Optional[Any]     				=					min(_a				)

        lowercase_					:       int     				=					feat_extract.pad(
            _a       ,				padding='max_length'       ,				max_length=_a       ,				truncation=_a       ,				return_tensors='np'				)
        self.assertIn('attention_mask'       ,				_a				)
        self.assertListEqual(
            list(processed_pad.attention_mask.shape				)       ,				[processed_pad[input_name].shape[0], max_length]				)
        self.assertListEqual(
            processed_pad.attention_mask[:, :max_length].sum(-1				).tolist()       ,				[max_length for x in speech_inputs]				)




