






"""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 PoolFormerImageProcessor





class       snake_case_       (    unittest.TestCase       ):



 """simple docstring"""

 def __init__(      self  ,					__a  ,					__a=7  ,					__a=3  ,					__a=30  ,					__a=400  ,					__a=True  ,					__a=None  ,					__a=0.9  ,					__a=None  ,					__a=True  ,					__a=[0.5, 0.5, 0.5]  ,					__a=[0.5, 0.5, 0.5]  ,					):


     """simple docstring"""

     A__							 =       size if size is not None else {'shortest_edge': 30}
     A__							 =       crop_size if crop_size is not None else {'height': 30, 'width': 30}
     A__							 =       parent
     A__							 =       batch_size
     A__							 =       num_channels
     A__							 =       min_resolution
     A__							 =       max_resolution
     A__							 =       do_resize_and_center_crop
     A__							 =       size
     A__							 =       crop_pct
     A__							 =       crop_size
     A__							 =       do_normalize
     A__							 =       image_mean
     A__							 =       image_std






 def       _UpperCAmelCase						(      self						):


     """simple docstring"""

     return {
         "size": self.size,
         "do_resize_and_center_crop": self.do_resize_and_center_crop,
         "crop_pct": self.crop_pct,
         "crop_size": self.crop_size,
         "do_normalize": self.do_normalize,
         "image_mean": self.image_mean,
         "image_std": self.image_std,
     }





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



 """simple docstring"""

 SCREAMING_SNAKE_CASE_:							List[Any]   			=					PoolFormerImageProcessor if is_vision_available() else None


 def       _UpperCAmelCase						(      self						):


     """simple docstring"""

     A__							 =       PoolFormerImageProcessingTester(self						)


 @property
 def       _UpperCAmelCase						(      self						):


     """simple docstring"""

     return self.image_processor_tester.prepare_image_processor_dict()


 def       _UpperCAmelCase						(      self						):


     """simple docstring"""

     A__							 =       self.image_processing_class(**self.image_processor_dict						)
     self.assertTrue(hasattr(__a  ,					'do_resize_and_center_crop'						)						)
     self.assertTrue(hasattr(__a  ,					'size'						)						)
     self.assertTrue(hasattr(__a  ,					'crop_pct'						)						)
     self.assertTrue(hasattr(__a  ,					'do_normalize'						)						)
     self.assertTrue(hasattr(__a  ,					'image_mean'						)						)
     self.assertTrue(hasattr(__a  ,					'image_std'						)						)


 def       _UpperCAmelCase						(      self						):


     """simple docstring"""

     A__							 =       self.image_processing_class.from_dict(self.image_processor_dict						)
     self.assertEqual(image_processor.size  ,					{'shortest_edge': 30}						)
     self.assertEqual(image_processor.crop_size  ,					{'height': 30, 'width': 30}						)

     A__							 =       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						):


     """simple docstring"""

     pass


 def       _UpperCAmelCase						(      self						):


     """simple docstring"""

     A__							 =       self.image_processing_class(**self.image_processor_dict						)
     # create random PIL images
     A__							 =       prepare_image_inputs(self.image_processor_tester  ,					equal_resolution=__a						)
     for image in image_inputs:
         self.assertIsInstance(__a  ,					Image.Image						)

     # Test not batched input
     A__							 =       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
     A__							 =       image_processing(__a  ,					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						):


     """simple docstring"""

     A__							 =       self.image_processing_class(**self.image_processor_dict						)
     # create random numpy tensors
     A__							 =       prepare_image_inputs(self.image_processor_tester  ,					equal_resolution=__a  ,					numpify=__a						)
     for image in image_inputs:
         self.assertIsInstance(__a  ,					np.ndarray						)

     # Test not batched input
     A__							 =       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
     A__							 =       image_processing(__a  ,					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						):


     """simple docstring"""

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

     # Test not batched input
     A__							 =       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
     A__							 =       image_processing(__a  ,					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


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

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

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

SCREAMING_SNAKE_CASE						:						str					   =       {
    '''gpt-neox-20b''': 2_048,
}





class       snake_case_       (    _lowerCamelCase       ):



	"""simple docstring"""

	SCREAMING_SNAKE_CASE_:							Union[str, Any]   			=					VOCAB_FILES_NAMES
	SCREAMING_SNAKE_CASE_:							Any   			=					PRETRAINED_VOCAB_FILES_MAP
	SCREAMING_SNAKE_CASE_:							List[Any]   			=					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	SCREAMING_SNAKE_CASE_:							Optional[Any]   			=					["""input_ids""", """attention_mask"""]


	def __init__(      self  ,					__a=None  ,					__a=None  ,					__a=None  ,					__a="<|endoftext|>"  ,					__a="<|endoftext|>"  ,					__a="<|endoftext|>"  ,					__a=False  ,					**__a  ,					):


					"""simple docstring"""

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

					A__							 =       json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()						)
					if pre_tok_state.get('add_prefix_space'  ,					__a						) != add_prefix_space:
									A__							 =       getattr(__a  ,					pre_tok_state.pop('type'						)						)
									A__							 =       add_prefix_space
									A__							 =       pre_tok_class(**__a						)

					A__							 =       add_prefix_space


	def       _UpperCAmelCase						(      self  ,					__a  ,					__a = None						):


					"""simple docstring"""

					A__							 =       self._tokenizer.model.save(__a  ,					name=__a						)
					return tuple(__a						)






	def       _UpperCAmelCase						(      self  ,					__a						):


					"""simple docstring"""

					A__							 =       []
					for is_user, text in conversation.iter_texts():
									input_ids.extend(self.encode(__a  ,					add_special_tokens=__a						) + [self.eos_token_id]						)

					if len(__a						) > self.model_max_length:
									A__							 =       input_ids[-self.model_max_length :]
					return input_ids


