





'''simple docstring'''

import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
   import torch

if is_vision_available():
   from PIL import Image

   from transformers import LevitImageProcessor





class lowerCamelCase						(unittest.TestCase					):







  def __init__(		self     ,     lowercase__     ,     lowercase__=7     ,     lowercase__=3     ,     lowercase__=18     ,     lowercase__=30     ,     lowercase__=400     ,     lowercase__=True     ,     lowercase__=None     ,     lowercase__=True     ,     lowercase__=None     ,     lowercase__=True     ,     lowercase__=[0.5, 0.5, 0.5]     ,     lowercase__=[0.5, 0.5, 0.5]     ,     )		->						List[Any]:






        """simple docstring"""





        _snake_case		:	int					     =     size if size is not None else {'''shortest_edge''': 18}
        _snake_case		:	Tuple					     =     crop_size if crop_size is not None else {'''height''': 18, '''width''': 18}
        _snake_case		:	Union[str, Any]					     =     parent
        _snake_case		:	List[str]					     =     batch_size
        _snake_case		:	Optional[Any]					     =     num_channels
        _snake_case		:	Any					     =     image_size
        _snake_case		:	Union[str, Any]					     =     min_resolution
        _snake_case		:	Optional[Any]					     =     max_resolution
        _snake_case		:	Any					     =     do_resize
        _snake_case		:	Union[str, Any]					     =     size
        _snake_case		:	Optional[int]					     =     do_center_crop
        _snake_case		:	Tuple					     =     crop_size
        _snake_case		:	List[Any]					     =     do_normalize
        _snake_case		:	Dict					     =     image_mean
        _snake_case		:	str					     =     image_std







  def 				UpperCAmelCase_				(		self		)		->						Dict:






        """simple docstring"""





        return {
            "image_mean": self.image_mean,
            "image_std": self.image_std,
            "do_normalize": self.do_normalize,
            "do_resize": self.do_resize,
            "do_center_crop": self.do_center_crop,
            "size": self.size,
            "crop_size": self.crop_size,
        }





@require_torch
@require_vision
class lowerCamelCase						(a__						,      unittest.TestCase					):
  _lowercase  :	Tuple       					=				LevitImageProcessor if is_vision_available() else None







  def 				UpperCAmelCase_				(		self		)		->						List[str]:






        """simple docstring"""





        _snake_case		:	int					     =     LevitImageProcessingTester(self		)







  @property
  def 				UpperCAmelCase_				(		self		)		->						List[str]:






        """simple docstring"""





        return self.image_processor_tester.prepare_image_processor_dict()







  def 				UpperCAmelCase_				(		self		)		->						Tuple:






        """simple docstring"""





        _snake_case		:	Tuple					     =     self.image_processing_class(**self.image_processor_dict		)
        self.assertTrue(hasattr(lowercase__     ,     '''image_mean'''		)		)
        self.assertTrue(hasattr(lowercase__     ,     '''image_std'''		)		)
        self.assertTrue(hasattr(lowercase__     ,     '''do_normalize'''		)		)
        self.assertTrue(hasattr(lowercase__     ,     '''do_resize'''		)		)
        self.assertTrue(hasattr(lowercase__     ,     '''do_center_crop'''		)		)
        self.assertTrue(hasattr(lowercase__     ,     '''size'''		)		)







  def 				UpperCAmelCase_				(		self		)		->						Optional[Any]:






        """simple docstring"""





        _snake_case		:	str					     =     self.image_processing_class.from_dict(self.image_processor_dict		)
        self.assertEqual(image_processor.size     ,     {'''shortest_edge''': 18}		)
        self.assertEqual(image_processor.crop_size     ,     {'''height''': 18, '''width''': 18}		)

        _snake_case		:	Tuple					     =     self.image_processing_class.from_dict(self.image_processor_dict     ,     size=42     ,     crop_size=84		)
        self.assertEqual(image_processor.size     ,     {'''shortest_edge''': 42}		)
        self.assertEqual(image_processor.crop_size     ,     {'''height''': 84, '''width''': 84}		)







  def 				UpperCAmelCase_				(		self		)		->						Tuple:






        """simple docstring"""





        pass







  def 				UpperCAmelCase_				(		self		)		->						Optional[int]:






        """simple docstring"""





        _snake_case		:	Dict					     =     self.image_processing_class(**self.image_processor_dict		)
        # create random PIL images
        _snake_case		:	Optional[Any]					     =     prepare_image_inputs(self.image_processor_tester     ,     equal_resolution=lowercase__		)
        for image in image_inputs:
              self.assertIsInstance(lowercase__     ,     Image.Image		)

        # Test not batched input
        _snake_case		:	List[str]					     =     image_processing(image_inputs[0]     ,     return_tensors='''pt'''		).pixel_values
        self.assertEqual(
            encoded_images.shape     ,     (
                1,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.crop_size['''height'''],
                self.image_processor_tester.crop_size['''width'''],
            )     ,     )

        # Test batched
        _snake_case		:	Any					     =     image_processing(lowercase__     ,     return_tensors='''pt'''		).pixel_values
        self.assertEqual(
            encoded_images.shape     ,     (
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.crop_size['''height'''],
                self.image_processor_tester.crop_size['''width'''],
            )     ,     )







  def 				UpperCAmelCase_				(		self		)		->						List[Any]:






        """simple docstring"""





        _snake_case		:	List[str]					     =     self.image_processing_class(**self.image_processor_dict		)
        # create random numpy tensors
        _snake_case		:	Optional[int]					     =     prepare_image_inputs(self.image_processor_tester     ,     equal_resolution=lowercase__     ,     numpify=lowercase__		)
        for image in image_inputs:
              self.assertIsInstance(lowercase__     ,     np.ndarray		)

        # Test not batched input
        _snake_case		:	Union[str, Any]					     =     image_processing(image_inputs[0]     ,     return_tensors='''pt'''		).pixel_values
        self.assertEqual(
            encoded_images.shape     ,     (
                1,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.crop_size['''height'''],
                self.image_processor_tester.crop_size['''width'''],
            )     ,     )

        # Test batched
        _snake_case		:	List[str]					     =     image_processing(lowercase__     ,     return_tensors='''pt'''		).pixel_values
        self.assertEqual(
            encoded_images.shape     ,     (
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.crop_size['''height'''],
                self.image_processor_tester.crop_size['''width'''],
            )     ,     )







  def 				UpperCAmelCase_				(		self		)		->						Optional[Any]:






        """simple docstring"""





        _snake_case		:	Dict					     =     self.image_processing_class(**self.image_processor_dict		)
        # create random PyTorch tensors
        _snake_case		:	str					     =     prepare_image_inputs(self.image_processor_tester     ,     equal_resolution=lowercase__     ,     torchify=lowercase__		)
        for image in image_inputs:
              self.assertIsInstance(lowercase__     ,     torch.Tensor		)

        # Test not batched input
        _snake_case		:	Dict					     =     image_processing(image_inputs[0]     ,     return_tensors='''pt'''		).pixel_values
        self.assertEqual(
            encoded_images.shape     ,     (
                1,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.crop_size['''height'''],
                self.image_processor_tester.crop_size['''width'''],
            )     ,     )

        # Test batched
        _snake_case		:	Dict					     =     image_processing(lowercase__     ,     return_tensors='''pt'''		).pixel_values
        self.assertEqual(
            encoded_images.shape     ,     (
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.crop_size['''height'''],
                self.image_processor_tester.crop_size['''width'''],
            )     ,     )









'''simple docstring'''

import json
from typing import TYPE_CHECKING, List, Optional, Tuple

from tokenizers import pre_tokenizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging


if TYPE_CHECKING:
   from transformers.pipelines.conversational import Conversation


UpperCAmelCase					:		Optional[int]		=							logging.get_logger(__name__)

UpperCAmelCase					:		List[Any]		=							{'vocab_file': 'vocab.json', 'merges_file': 'merges.txt', 'tokenizer_file': 'tokenizer.json'}

UpperCAmelCase					:		Any		=							{
    'tokenizer_file': {
        'EleutherAI/gpt-neox-20b': 'https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/main/tokenizer.json',
    },
}

UpperCAmelCase					:		Optional[Any]		=							{
    'gpt-neox-20b': 2_0_4_8,
}





class lowerCamelCase						(a__					):
  _lowercase  :	Optional[int]       					=				VOCAB_FILES_NAMES
  _lowercase  :	str       					=				PRETRAINED_VOCAB_FILES_MAP
  _lowercase  :	Dict       					=				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  _lowercase  :	Optional[int]       					=				["""input_ids""", """attention_mask"""]







  def __init__(		self     ,     lowercase__=None     ,     lowercase__=None     ,     lowercase__=None     ,     lowercase__="<|endoftext|>"     ,     lowercase__="<|endoftext|>"     ,     lowercase__="<|endoftext|>"     ,     lowercase__=False     ,     **lowercase__     ,     )		->						List[Any]:






        """simple docstring"""





        super().__init__(
            lowercase__     ,     lowercase__     ,     tokenizer_file=lowercase__     ,     unk_token=lowercase__     ,     bos_token=lowercase__     ,     eos_token=lowercase__     ,     add_prefix_space=lowercase__     ,     **lowercase__     ,     )

        _snake_case		:	Optional[int]					     =     json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()		)
        if pre_tok_state.get('''add_prefix_space'''     ,     lowercase__		) != add_prefix_space:
              _snake_case		:	int					     =     getattr(lowercase__     ,     pre_tok_state.pop('''type'''		)		)
              _snake_case		:	int					     =     add_prefix_space
              _snake_case		:	Optional[Any]					     =     pre_tok_class(**lowercase__		)

        _snake_case		:	List[str]					     =     add_prefix_space







  def 				UpperCAmelCase_				(		self     ,     lowercase__     ,     lowercase__ = None		)		->						Tuple[str]:






        """simple docstring"""





        _snake_case		:	Optional[int]					     =     self._tokenizer.model.save(lowercase__     ,     name=lowercase__		)
        return tuple(lowercase__		)







  def 				UpperCAmelCase_				(		self     ,     lowercase__		)		->						List[int]:






        """simple docstring"""





        _snake_case		:	List[str]					     =     []
        for is_user, text in conversation.iter_texts():
              input_ids.extend(self.encode(lowercase__     ,     add_special_tokens=lowercase__		) + [self.eos_token_id]		)

        if len(lowercase__		) > self.model_max_length:
              _snake_case		:	Dict					     =     input_ids[-self.model_max_length :]
        return input_ids


