





'''simple docstring'''




import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


__UpperCAmelCase            =       logging.get_logger(__name__)

__UpperCAmelCase            =       """▁"""

__UpperCAmelCase            =       {"""vocab_file""": """sentencepiece.bpe.model"""}

__UpperCAmelCase            =       {
    """vocab_file""": {
        """facebook/xglm-564M""": """https://huggingface.co/facebook/xglm-564M/resolve/main/sentencepiece.bpe.model""",
    }
}

__UpperCAmelCase            =       {
    """facebook/xglm-564M""": 2048,
}




class      UpperCamelCase__					(					lowercase_			):




					"""simple docstring"""
					SCREAMING_SNAKE_CASE__				=	VOCAB_FILES_NAMES
					SCREAMING_SNAKE_CASE__				=	PRETRAINED_VOCAB_FILES_MAP
					SCREAMING_SNAKE_CASE__				=	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					SCREAMING_SNAKE_CASE__				=	["input_ids", "attention_mask"]


					def __init__(						self				:			List[str]      ,						lowerCamelCase_				:			List[str]      ,						lowerCamelCase_				:			int="<s>"      ,						lowerCamelCase_				:			List[str]="</s>"      ,						lowerCamelCase_				:			Union[str, Any]="</s>"      ,						lowerCamelCase_				:			List[str]="<s>"      ,						lowerCamelCase_				:			List[str]="<unk>"      ,						lowerCamelCase_				:			Optional[int]="<pad>"      ,						lowerCamelCase_				:			Optional[Dict[str, Any]] = None      ,						**lowerCamelCase_				:			Tuple      ,						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				Any		=			{} if sp_model_kwargs is None else sp_model_kwargs

							# Compatibility with the original tokenizer
							SCREAMING_SNAKE_CASE :				int		=			7
							SCREAMING_SNAKE_CASE :				List[Any]		=			[f'''<madeupword{i}>''' for i in range(self.num_madeup_words						)]

							SCREAMING_SNAKE_CASE :				List[str]		=			kwargs.get("""additional_special_tokens"""      ,						[]						)
							kwargs["additional_special_tokens"] += [
							    word for word in madeup_words if word not in kwargs["additional_special_tokens"]
							]

							super().__init__(
							    bos_token=snake_case__      ,						eos_token=snake_case__      ,						unk_token=snake_case__      ,						sep_token=snake_case__      ,						cls_token=snake_case__      ,						pad_token=snake_case__      ,						sp_model_kwargs=self.sp_model_kwargs      ,						**snake_case__      ,						)

							SCREAMING_SNAKE_CASE :				List[str]		=			spm.SentencePieceProcessor(**self.sp_model_kwargs						)
							self.sp_model.Load(str(snake_case__						)						)
							SCREAMING_SNAKE_CASE :				List[Any]		=			vocab_file

							# Original fairseq vocab and spm vocab must be "aligned":
							# Vocab    |    0    |    1    |   2    |    3    |  4  |  5  |  6  |   7   |   8   |  9
							# -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
							# fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's'   | '▁de' | '-'
							# spm      | '<unk>' | '<s>'   | '</s>' | ','     | '.' | '▁' | 's' | '▁de' | '-'   | '▁a'

							# The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
							SCREAMING_SNAKE_CASE :				Dict		=			1

							# Mimic fairseq token-to-id alignment for the first 4 token
							SCREAMING_SNAKE_CASE :				List[Any]		=			{"<s>": 0, "<pad>": 1, "</s>": 2, "<unk>": 3}

							SCREAMING_SNAKE_CASE :				int		=			len(self.sp_model						)
							SCREAMING_SNAKE_CASE :				int		=			{f'''<madeupword{i}>''': sp_size + i + self.fairseq_offset for i in range(self.num_madeup_words						)}
							self.fairseq_tokens_to_ids.update(snake_case__						)

							SCREAMING_SNAKE_CASE :				str		=			{v: k for k, v in self.fairseq_tokens_to_ids.items()}


					def __getstate__(						self				:			int						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				str		=			self.__dict__.copy()
							SCREAMING_SNAKE_CASE :				Tuple		=			None
							SCREAMING_SNAKE_CASE :				Optional[Any]		=			self.sp_model.serialized_model_proto()
							return state


					def __setstate__(						self				:			Optional[Any]      ,						lowerCamelCase_				:			Tuple						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				Tuple		=			d

							# for backward compatibility
							if not hasattr(self      ,						"""sp_model_kwargs"""						):
									SCREAMING_SNAKE_CASE :				str		=			{}

							SCREAMING_SNAKE_CASE :				Tuple		=			spm.SentencePieceProcessor(**self.sp_model_kwargs						)
							self.sp_model.LoadFromSerializedProto(self.sp_model_proto						)


					def lowerCamelCase_			(						self				:			Optional[Any]      ,						lowerCamelCase_				:			List[int]      ,						lowerCamelCase_				:			Optional[List[int]] = None						):

							'''simple docstring'''



							if token_ids_a is None:
									return [self.sep_token_id] + token_ids_a
							SCREAMING_SNAKE_CASE :				List[str]		=			[self.sep_token_id]
							return sep + token_ids_a + sep + sep + token_ids_a


					def lowerCamelCase_			(						self				:			List[Any]      ,						lowerCamelCase_				:			List[int]      ,						lowerCamelCase_				:			Optional[List[int]] = None      ,						lowerCamelCase_				:			bool = False						):

							'''simple docstring'''



							if already_has_special_tokens:
									return super().get_special_tokens_mask(
									    token_ids_a=snake_case__      ,						token_ids_a=snake_case__      ,						already_has_special_tokens=snake_case__						)

							if token_ids_a is None:
									return [1] + ([0] * len(snake_case__						))
							return [1] + ([0] * len(snake_case__						)) + [1, 1] + ([0] * len(snake_case__						))


					def lowerCamelCase_			(						self				:			List[str]      ,						lowerCamelCase_				:			List[int]      ,						lowerCamelCase_				:			Optional[List[int]] = None						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				Any		=			[self.sep_token_id]

							if token_ids_a is None:
									return len(sep + token_ids_a						) * [0]
							return len(sep + token_ids_a + sep + sep + token_ids_a						) * [0]


					@property
					def lowerCamelCase_			(						self				:			List[Any]						):

							'''simple docstring'''



							return len(self.sp_model						) + self.fairseq_offset + self.num_madeup_words


					def lowerCamelCase_			(						self				:			Dict						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				Any		=			{self.convert_ids_to_tokens(snake_case__						): i for i in range(self.vocab_size						)}
							vocab.update(self.added_tokens_encoder						)
							return vocab


					def lowerCamelCase_			(						self				:			Tuple      ,						lowerCamelCase_				:			str						):

							'''simple docstring'''



							return self.sp_model.encode(snake_case__      ,						out_type=snake_case__						)


					def lowerCamelCase_			(						self				:			Optional[int]      ,						lowerCamelCase_				:			str						):

							'''simple docstring'''



							if token in self.fairseq_tokens_to_ids:
									return self.fairseq_tokens_to_ids[token]
							SCREAMING_SNAKE_CASE :				int		=			self.sp_model.PieceToId(snake_case__						)

							# Need to return unknown token if the SP model returned 0
							return spm_id + self.fairseq_offset if spm_id else self.unk_token_id


					def lowerCamelCase_			(						self				:			Any      ,						lowerCamelCase_				:			int						):

							'''simple docstring'''



							if index in self.fairseq_ids_to_tokens:
									return self.fairseq_ids_to_tokens[index]
							return self.sp_model.IdToPiece(index - self.fairseq_offset						)


					def lowerCamelCase_			(						self				:			Dict      ,						lowerCamelCase_				:			Any						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				Optional[Any]		=			"".join(snake_case__						).replace(snake_case__      ,						""" """						).strip()
							return out_string



					def lowerCamelCase_			(						self				:			str      ,						lowerCamelCase_				:			str      ,						lowerCamelCase_				:			Optional[str] = None						):

							'''simple docstring'''



							if not os.path.isdir(snake_case__						):
									logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''						)
									return
							SCREAMING_SNAKE_CASE :				Optional[int]		=			os.path.join(
							    snake_case__      ,						(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]						)

							if os.path.abspath(self.vocab_file						) != os.path.abspath(snake_case__						) and os.path.isfile(self.vocab_file						):
									copyfile(self.vocab_file      ,						snake_case__						)
							elif not os.path.isfile(self.vocab_file						):
									with open(snake_case__      ,						"""wb"""						) as fi:
											SCREAMING_SNAKE_CASE :				Union[str, Any]		=			self.sp_model.serialized_model_proto()
											fi.write(snake_case__						)

							return (out_vocab_file,)





'''simple docstring'''





import json
import os
import tempfile
import unittest

import numpy as np
from datasets import load_dataset

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

from ...test_image_processing_common import ImageProcessingSavingTestMixin


if is_torch_available():
      import torch

if is_vision_available():
      from PIL import Image

      from transformers import ImageGPTImageProcessor






class 							UpperCAmelCase_							(    unittest.TestCase				):

 '''simple docstring'''







 def __init__(					self		:  Optional[int]			,				snake_case__		:  int			,				snake_case__		:  Optional[Any]=7			,				snake_case__		:  Tuple=3			,				snake_case__		:  Optional[Any]=18			,				snake_case__		:  Dict=30			,				snake_case__		:  Optional[int]=4_00			,				snake_case__		:  str=True			,				snake_case__		:  str=None			,				snake_case__		:  Optional[int]=True			,				):



   '''simple docstring'''




   UpperCAmelCase__      :					Optional[int]				       =							size if size is not None else {"height": 18, "width": 18}
   UpperCAmelCase__      :					List[str]				       =							parent
   UpperCAmelCase__      :					Union[str, Any]				       =							batch_size
   UpperCAmelCase__      :					List[str]				       =							num_channels
   UpperCAmelCase__      :					List[Any]				       =							image_size
   UpperCAmelCase__      :					Tuple				       =							min_resolution
   UpperCAmelCase__      :					Optional[int]				       =							max_resolution
   UpperCAmelCase__      :					Any				       =							do_resize
   UpperCAmelCase__      :					Any				       =							size
   UpperCAmelCase__      :					int				       =							do_normalize







 def 							UpperCamelCase  (					self		:  List[str] ):



   '''simple docstring'''




   return {
       # here we create 2 clusters for the sake of simplicity
       "clusters": np.asarray(
           [
               [0.8866443634033203, 0.6618829369544983, 0.3891746401786804],
               [-0.6042559146881104, -0.02295008860528469, 0.5423797369003296],
           ] ),
       "do_resize": self.do_resize,
       "size": self.size,
       "do_normalize": self.do_normalize,
   }






@require_torch
@require_vision
class 							UpperCAmelCase_							(    A					,				unittest.TestCase				):

 '''simple docstring'''
 lowercase_					:    Any				=							ImageGPTImageProcessor if is_vision_available() else None







 def 							UpperCamelCase  (					self		:  Dict ):



   '''simple docstring'''




   UpperCAmelCase__      :					List[str]				       =							ImageGPTImageProcessingTester(self )







 @property
 def 							UpperCamelCase  (					self		:  List[Any] ):



   '''simple docstring'''




   return self.image_processor_tester.prepare_image_processor_dict()







 def 							UpperCamelCase  (					self		:  Union[str, Any] ):



   '''simple docstring'''




   UpperCAmelCase__      :					List[str]				       =							self.image_processing_class(**self.image_processor_dict )
   self.assertTrue(hasattr(snake_case__			,				"clusters" ) )
   self.assertTrue(hasattr(snake_case__			,				"do_resize" ) )
   self.assertTrue(hasattr(snake_case__			,				"size" ) )
   self.assertTrue(hasattr(snake_case__			,				"do_normalize" ) )







 def 							UpperCamelCase  (					self		:  Tuple ):



   '''simple docstring'''




   UpperCAmelCase__      :					str				       =							self.image_processing_class.from_dict(self.image_processor_dict )
   self.assertEqual(image_processor.size			,				{"height": 18, "width": 18} )

   UpperCAmelCase__      :					Union[str, Any]				       =							self.image_processing_class.from_dict(self.image_processor_dict			,				size=42 )
   self.assertEqual(image_processor.size			,				{"height": 42, "width": 42} )







 def 							UpperCamelCase  (					self		:  Any ):



   '''simple docstring'''




   UpperCAmelCase__      :					int				       =							self.image_processing_class(**self.image_processor_dict )
   UpperCAmelCase__      :					List[str]				       =							json.loads(image_processor.to_json_string() )
   for key, value in self.image_processor_dict.items():
     if key == "clusters":
       self.assertTrue(np.array_equal(snake_case__			,				obj[key] ) )
     else:
       self.assertEqual(obj[key]			,				snake_case__ )







 def 							UpperCamelCase  (					self		:  Union[str, Any] ):



   '''simple docstring'''




   UpperCAmelCase__      :					Dict				       =							self.image_processing_class(**self.image_processor_dict )

   with tempfile.TemporaryDirectory() as tmpdirname:
     UpperCAmelCase__      :					Union[str, Any]				       =							os.path.join(snake_case__			,				"image_processor.json" )
     image_processor_first.to_json_file(snake_case__ )
     UpperCAmelCase__      :					Any				       =							self.image_processing_class.from_json_file(snake_case__ ).to_dict()

   UpperCAmelCase__      :					Union[str, Any]				       =							image_processor_first.to_dict()
   for key, value in image_processor_first.items():
     if key == "clusters":
       self.assertTrue(np.array_equal(snake_case__			,				image_processor_second[key] ) )
     else:
       self.assertEqual(image_processor_first[key]			,				snake_case__ )







 def 							UpperCamelCase  (					self		:  List[str] ):



   '''simple docstring'''




   UpperCAmelCase__      :					int				       =							self.image_processing_class(**self.image_processor_dict )

   with tempfile.TemporaryDirectory() as tmpdirname:
     image_processor_first.save_pretrained(snake_case__ )
     UpperCAmelCase__      :					List[str]				       =							self.image_processing_class.from_pretrained(snake_case__ ).to_dict()

   UpperCAmelCase__      :					Tuple				       =							image_processor_first.to_dict()
   for key, value in image_processor_first.items():
     if key == "clusters":
       self.assertTrue(np.array_equal(snake_case__			,				image_processor_second[key] ) )
     else:
       self.assertEqual(image_processor_first[key]			,				snake_case__ )







 @unittest.skip("ImageGPT requires clusters at initialization" )
 def 							UpperCamelCase  (					self		:  List[Any] ):



   '''simple docstring'''




   pass







def snake_case_     (					):
  UpperCAmelCase__      :					List[str]				       =							load_dataset("hf-internal-testing/fixtures_image_utils"   ,       split="test"		)

  UpperCAmelCase__      :					Tuple				       =							Image.open(dataset[4]["file"]		)
  UpperCAmelCase__      :					List[Any]				       =							Image.open(dataset[5]["file"]		)

  UpperCAmelCase__      :					Optional[Any]				       =							[imagea, imagea]

  return images






@require_vision
@require_torch
class 							UpperCAmelCase_							(    unittest.TestCase				):

 '''simple docstring'''







 @slow
 def 							UpperCamelCase  (					self		:  str ):



   '''simple docstring'''




   UpperCAmelCase__      :					Dict				       =							ImageGPTImageProcessor.from_pretrained("openai/imagegpt-small" )

   UpperCAmelCase__      :					Union[str, Any]				       =							prepare_images()

   # test non-batched
   UpperCAmelCase__      :					Optional[Any]				       =							image_processing(images[0]			,				return_tensors="pt" )

   self.assertIsInstance(encoding.input_ids			,				torch.LongTensor )
   self.assertEqual(encoding.input_ids.shape			,				(1, 10_24) )

   UpperCAmelCase__      :					Union[str, Any]				       =							[3_06, 1_91, 1_91]
   self.assertEqual(encoding.input_ids[0, :3].tolist()			,				snake_case__ )

   # test batched
   UpperCAmelCase__      :					List[Any]				       =							image_processing(snake_case__			,				return_tensors="pt" )

   self.assertIsInstance(encoding.input_ids			,				torch.LongTensor )
   self.assertEqual(encoding.input_ids.shape			,				(2, 10_24) )

   UpperCAmelCase__      :					List[str]				       =							[3_03, 13, 13]
   self.assertEqual(encoding.input_ids[1, -3:].tolist()			,				snake_case__ )



