

import json
import os
import unittest

from transformers import AutoTokenizer, GPTaTokenizer, GPTaTokenizerFast
from transformers.models.gpta.tokenization_gpta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin

@require_tokenizers
class __UpperCamelCase     (				_lowerCAmelCase					,unittest.TestCase		):






					'''simple docstring'''



					__magic_name__        =   GPTaTokenizer
					__magic_name__        =   GPTaTokenizerFast
					__magic_name__        =   True
					__magic_name__        =   {"add_prefix_space": True}
					__magic_name__        =   False





					def    _UpperCAmelCase    (				self						):
						super().setUp()

						# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
						UpperCAmelCase__:	Dict									= [
						    "l",
						    "o",
						    "w",
						    "e",
						    "r",
						    "s",
						    "t",
						    "i",
						    "d",
						    "n",
						    "\u0120",
						    "\u0120l",
						    "\u0120n",
						    "\u0120lo",
						    "\u0120low",
						    "er",
						    "\u0120lowest",
						    "\u0120newer",
						    "\u0120wider",
						    "<unk>",
						    "<|endoftext|>",
						]
						UpperCAmelCase__:	Optional[Any]									= dict(zip(SCREAMING_SNAKE_CASE__				,      range(len(SCREAMING_SNAKE_CASE__						)						)						)						)
						UpperCAmelCase__:	Tuple									= ["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""]
						UpperCAmelCase__:	Optional[Any]									= {"unk_token": "<unk>"}

						UpperCAmelCase__:	Dict									= os.path.join(self.tmpdirname				,      VOCAB_FILES_NAMES["vocab_file"]						)
						UpperCAmelCase__:	Union[str, Any]									= os.path.join(self.tmpdirname				,      VOCAB_FILES_NAMES["merges_file"]						)
						with open(self.vocab_file				,      "w"				,      encoding="utf-8"						) as fp:
							fp.write(json.dumps(SCREAMING_SNAKE_CASE__						) + "\n"						)
						with open(self.merges_file				,      "w"				,      encoding="utf-8"						) as fp:
							fp.write("\n".join(SCREAMING_SNAKE_CASE__						)						)





					def    _UpperCAmelCase    (				self				,      **lowerCamelCase__						):
						kwargs.update(self.special_tokens_map						)
						return GPTaTokenizer.from_pretrained(self.tmpdirname				,      **SCREAMING_SNAKE_CASE__						)





					def    _UpperCAmelCase    (				self				,      **lowerCamelCase__						):
						kwargs.update(self.special_tokens_map						)
						return GPTaTokenizerFast.from_pretrained(self.tmpdirname				,      **SCREAMING_SNAKE_CASE__						)





					def    _UpperCAmelCase    (				self				,      lowerCamelCase__						):
						UpperCAmelCase__:	Any									= "lower newer"
						UpperCAmelCase__:	List[Any]									= "lower newer"
						return input_text, output_text





					def    _UpperCAmelCase    (				self						):
						UpperCAmelCase__:	Union[str, Any]									= GPTaTokenizer(self.vocab_file				,      self.merges_file				,      **self.special_tokens_map						)
						UpperCAmelCase__:	str									= "lower newer"
						UpperCAmelCase__:	Dict									= ["\u0120low", "er", "\u0120", "n", "e", "w", "er"]
						UpperCAmelCase__:	Any									= tokenizer.tokenize(SCREAMING_SNAKE_CASE__				,      add_prefix_space=SCREAMING_SNAKE_CASE__						)
						self.assertListEqual(SCREAMING_SNAKE_CASE__				,      SCREAMING_SNAKE_CASE__						)

						UpperCAmelCase__:	Any									= tokens + [tokenizer.unk_token]
						UpperCAmelCase__:	int									= [1_4, 1_5, 1_0, 9, 3, 2, 1_5, 1_9]
						self.assertListEqual(tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE__						)				,      SCREAMING_SNAKE_CASE__						)





					def    _UpperCAmelCase    (				self						):
						if not self.test_rust_tokenizer:
							return

						UpperCAmelCase__:	Optional[int]									= self.get_tokenizer()
						UpperCAmelCase__:	List[Any]									= self.get_rust_tokenizer(add_prefix_space=SCREAMING_SNAKE_CASE__						)

						UpperCAmelCase__:	Any									= "lower newer"

						# Testing tokenization
						UpperCAmelCase__:	Optional[int]									= tokenizer.tokenize(SCREAMING_SNAKE_CASE__				,      add_prefix_space=SCREAMING_SNAKE_CASE__						)
						UpperCAmelCase__:	Tuple									= rust_tokenizer.tokenize(SCREAMING_SNAKE_CASE__						)
						self.assertListEqual(SCREAMING_SNAKE_CASE__				,      SCREAMING_SNAKE_CASE__						)

						# Testing conversion to ids without special tokens
						UpperCAmelCase__:	Union[str, Any]									= tokenizer.encode(SCREAMING_SNAKE_CASE__				,      add_special_tokens=SCREAMING_SNAKE_CASE__				,      add_prefix_space=SCREAMING_SNAKE_CASE__						)
						UpperCAmelCase__:	int									= rust_tokenizer.encode(SCREAMING_SNAKE_CASE__				,      add_special_tokens=SCREAMING_SNAKE_CASE__						)
						self.assertListEqual(SCREAMING_SNAKE_CASE__				,      SCREAMING_SNAKE_CASE__						)

						# Testing conversion to ids with special tokens
						UpperCAmelCase__:	Optional[int]									= self.get_rust_tokenizer(add_prefix_space=SCREAMING_SNAKE_CASE__						)
						UpperCAmelCase__:	Optional[Any]									= tokenizer.encode(SCREAMING_SNAKE_CASE__				,      add_prefix_space=SCREAMING_SNAKE_CASE__						)
						UpperCAmelCase__:	int									= rust_tokenizer.encode(SCREAMING_SNAKE_CASE__						)
						self.assertListEqual(SCREAMING_SNAKE_CASE__				,      SCREAMING_SNAKE_CASE__						)

						# Testing the unknown token
						UpperCAmelCase__:	Union[str, Any]									= tokens + [rust_tokenizer.unk_token]
						UpperCAmelCase__:	List[Any]									= [1_4, 1_5, 1_0, 9, 3, 2, 1_5, 1_9]
						self.assertListEqual(rust_tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE__						)				,      SCREAMING_SNAKE_CASE__						)





					def    _UpperCAmelCase    (				self				,      *lowerCamelCase__				,      **lowerCamelCase__						):
						# It's very difficult to mix/test pretokenization with byte-level
						# And get both GPT2 and Roberta to work at the same time (mostly an issue of adding a space before the string)
						pass





					def    _UpperCAmelCase    (				self				,      lowerCamelCase__=1_5						):
						for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
							with self.subTest(F"{tokenizer.__class__.__name__} ({pretrained_name})"						):
								UpperCAmelCase__:	Optional[int]									= self.rust_tokenizer_class.from_pretrained(SCREAMING_SNAKE_CASE__				,      **SCREAMING_SNAKE_CASE__						)

								# Simple input
								UpperCAmelCase__:	Union[str, Any]									= "This is a simple input"
								UpperCAmelCase__:	int									= ["This is a simple input 1", "This is a simple input 2"]
								UpperCAmelCase__:	Tuple									= ("This is a simple input", "This is a pair")
								UpperCAmelCase__:	Union[str, Any]									= [
								    ("This is a simple input 1", "This is a simple input 2"),
								    ("This is a simple pair 1", "This is a simple pair 2"),
								]

								# Simple input tests
								self.assertRaises(SCREAMING_SNAKE_CASE__				,      tokenizer_r.encode				,      SCREAMING_SNAKE_CASE__				,      max_length=SCREAMING_SNAKE_CASE__				,      padding="max_length"						)

								# Simple input
								self.assertRaises(SCREAMING_SNAKE_CASE__				,      tokenizer_r.encode_plus				,      SCREAMING_SNAKE_CASE__				,      max_length=SCREAMING_SNAKE_CASE__				,      padding="max_length"						)

								# Simple input
								self.assertRaises(
								    SCREAMING_SNAKE_CASE__				,      tokenizer_r.batch_encode_plus				,      SCREAMING_SNAKE_CASE__				,      max_length=SCREAMING_SNAKE_CASE__				,      padding="max_length"				,      )

								# Pair input
								self.assertRaises(SCREAMING_SNAKE_CASE__				,      tokenizer_r.encode				,      SCREAMING_SNAKE_CASE__				,      max_length=SCREAMING_SNAKE_CASE__				,      padding="max_length"						)

								# Pair input
								self.assertRaises(SCREAMING_SNAKE_CASE__				,      tokenizer_r.encode_plus				,      SCREAMING_SNAKE_CASE__				,      max_length=SCREAMING_SNAKE_CASE__				,      padding="max_length"						)

								# Pair input
								self.assertRaises(
								    SCREAMING_SNAKE_CASE__				,      tokenizer_r.batch_encode_plus				,      SCREAMING_SNAKE_CASE__				,      max_length=SCREAMING_SNAKE_CASE__				,      padding="max_length"				,      )





					def    _UpperCAmelCase    (				self						):
						UpperCAmelCase__:	int									= GPTaTokenizer.from_pretrained(self.tmpdirname				,      pad_token="<pad>"						)

						# Simple input
						UpperCAmelCase__:	Optional[Any]									= "This is a simple input"
						UpperCAmelCase__:	Union[str, Any]									= ["This is a simple input looooooooong", "This is a simple input"]
						UpperCAmelCase__:	List[str]									= ("This is a simple input", "This is a pair")
						UpperCAmelCase__:	Tuple									= [
						    ("This is a simple input loooooong", "This is a simple input"),
						    ("This is a simple pair loooooong", "This is a simple pair"),
						]

						UpperCAmelCase__:	str									= tokenizer.pad_token_id

						UpperCAmelCase__:	Any									= tokenizer(SCREAMING_SNAKE_CASE__				,      padding="max_length"				,      max_length=3_0				,      return_tensors="np"						)
						UpperCAmelCase__:	Any									= tokenizer(SCREAMING_SNAKE_CASE__				,      padding=SCREAMING_SNAKE_CASE__				,      truncate=SCREAMING_SNAKE_CASE__				,      return_tensors="np"						)
						UpperCAmelCase__:	Optional[Any]									= tokenizer(*SCREAMING_SNAKE_CASE__				,      padding="max_length"				,      max_length=6_0				,      return_tensors="np"						)
						UpperCAmelCase__:	List[str]									= tokenizer(SCREAMING_SNAKE_CASE__				,      padding=SCREAMING_SNAKE_CASE__				,      truncate=SCREAMING_SNAKE_CASE__				,      return_tensors="np"						)

						# s
						# test single string max_length padding
						self.assertEqual(out_s["input_ids"].shape[-1]				,      3_0						)
						self.assertTrue(pad_token_id in out_s["input_ids"]						)
						self.assertTrue(0 in out_s["attention_mask"]						)

						# s2
						# test automatic padding
						self.assertEqual(out_sa["input_ids"].shape[-1]				,      3_3						)
						# long slice doesn't have padding
						self.assertFalse(pad_token_id in out_sa["input_ids"][0]						)
						self.assertFalse(0 in out_sa["attention_mask"][0]						)
						# short slice does have padding
						self.assertTrue(pad_token_id in out_sa["input_ids"][1]						)
						self.assertTrue(0 in out_sa["attention_mask"][1]						)

						# p
						# test single pair max_length padding
						self.assertEqual(out_p["input_ids"].shape[-1]				,      6_0						)
						self.assertTrue(pad_token_id in out_p["input_ids"]						)
						self.assertTrue(0 in out_p["attention_mask"]						)

						# p2
						# test automatic padding pair
						self.assertEqual(out_pa["input_ids"].shape[-1]				,      5_2						)
						# long slice pair doesn't have padding
						self.assertFalse(pad_token_id in out_pa["input_ids"][0]						)
						self.assertFalse(0 in out_pa["attention_mask"][0]						)
						# short slice pair does have padding
						self.assertTrue(pad_token_id in out_pa["input_ids"][1]						)
						self.assertTrue(0 in out_pa["attention_mask"][1]						)





					def    _UpperCAmelCase    (				self						):
						UpperCAmelCase__:	Any									= "$$$"
						UpperCAmelCase__:	str									= GPTaTokenizer.from_pretrained(self.tmpdirname				,      bos_token=SCREAMING_SNAKE_CASE__				,      add_bos_token=SCREAMING_SNAKE_CASE__						)

						UpperCAmelCase__:	List[str]									= "This is a simple input"
						UpperCAmelCase__:	str									= ["This is a simple input 1", "This is a simple input 2"]

						UpperCAmelCase__:	int									= tokenizer.bos_token_id

						UpperCAmelCase__:	int									= tokenizer(SCREAMING_SNAKE_CASE__						)
						UpperCAmelCase__:	List[Any]									= tokenizer(SCREAMING_SNAKE_CASE__						)

						self.assertEqual(out_s.input_ids[0]				,      SCREAMING_SNAKE_CASE__						)
						self.assertTrue(all(o[0] == bos_token_id for o in out_sa.input_ids						)						)

						UpperCAmelCase__:	Any									= tokenizer.decode(out_s.input_ids						)
						UpperCAmelCase__:	List[str]									= tokenizer.batch_decode(out_sa.input_ids						)

						self.assertEqual(decode_s.split()[0]				,      SCREAMING_SNAKE_CASE__						)
						self.assertTrue(all(d.split()[0] == bos_token for d in decode_sa						)						)





					def    _UpperCAmelCase    (				self						):
						pass





					def    _UpperCAmelCase    (				self						):
						# TODO: change to self.get_tokenizers() when the fast version is implemented
						UpperCAmelCase__:	List[Any]									= [self.get_tokenizer(do_lower_case=SCREAMING_SNAKE_CASE__				,      add_bos_token=SCREAMING_SNAKE_CASE__						)]
						for tokenizer in tokenizers:
							with self.subTest(F"{tokenizer.__class__.__name__}"						):
								UpperCAmelCase__:	Dict									= "Encode this."
								UpperCAmelCase__:	List[str]									= "This one too please."
								UpperCAmelCase__:	Dict									= tokenizer.encode(SCREAMING_SNAKE_CASE__				,      add_special_tokens=SCREAMING_SNAKE_CASE__						)
								encoded_sequence += tokenizer.encode(SCREAMING_SNAKE_CASE__				,      add_special_tokens=SCREAMING_SNAKE_CASE__						)
								UpperCAmelCase__:	Dict									= tokenizer.encode_plus(
								    SCREAMING_SNAKE_CASE__				,      SCREAMING_SNAKE_CASE__				,      add_special_tokens=SCREAMING_SNAKE_CASE__				,      return_special_tokens_mask=SCREAMING_SNAKE_CASE__				,      )
								UpperCAmelCase__:	Tuple									= encoded_sequence_dict["input_ids"]
								UpperCAmelCase__:	Tuple									= encoded_sequence_dict["special_tokens_mask"]
								self.assertEqual(len(SCREAMING_SNAKE_CASE__						)				,      len(SCREAMING_SNAKE_CASE__						)						)

								UpperCAmelCase__:	Tuple									= [
								    (x if not special_tokens_mask[i] else None) for i, x in enumerate(SCREAMING_SNAKE_CASE__						)
								]
								UpperCAmelCase__:	Union[str, Any]									= [x for x in filtered_sequence if x is not None]
								self.assertEqual(SCREAMING_SNAKE_CASE__				,      SCREAMING_SNAKE_CASE__						)

@require_tokenizers
class __UpperCamelCase     (				unittest.TestCase		):






					'''simple docstring'''



					def    _UpperCAmelCase    (				self						):
						# More context:
						# https://huggingface.co/wjmcat/opt-350m-paddle/discussions/1
						# https://huggingface.slack.com/archives/C01N44FJDHT/p1653511495183519
						# https://github.com/huggingface/transformers/pull/17088#discussion_r871246439

						UpperCAmelCase__:	Tuple									= AutoTokenizer.from_pretrained("facebook/opt-350m"				,      from_slow=SCREAMING_SNAKE_CASE__						)
						UpperCAmelCase__:	Dict									= "A photo of a cat"

						UpperCAmelCase__:	List[Any]									= tokenizer.encode(
						    SCREAMING_SNAKE_CASE__				,      )
						self.assertEqual(SCREAMING_SNAKE_CASE__				,      [2, 2_5_0, 1_3_4_5, 9, 1_0, 4_7_5_8]						)
						tokenizer.save_pretrained("test_opt"						)

						UpperCAmelCase__:	int									= AutoTokenizer.from_pretrained("./test_opt"						)
						UpperCAmelCase__:	Tuple									= tokenizer.encode(
						    SCREAMING_SNAKE_CASE__				,      )
						self.assertEqual(SCREAMING_SNAKE_CASE__				,      [2, 2_5_0, 1_3_4_5, 9, 1_0, 4_7_5_8]						)





					def    _UpperCAmelCase    (				self						):
						UpperCAmelCase__:	Dict									= AutoTokenizer.from_pretrained("facebook/opt-350m"				,      use_slow=SCREAMING_SNAKE_CASE__						)
						UpperCAmelCase__:	Optional[Any]									= "A photo of a cat"

						UpperCAmelCase__:	Optional[Any]									= tokenizer.encode(
						    SCREAMING_SNAKE_CASE__				,      )
						# Same as above
						self.assertEqual(SCREAMING_SNAKE_CASE__				,      [2, 2_5_0, 1_3_4_5, 9, 1_0, 4_7_5_8]						)





					@unittest.skip("This test is failing because of a bug in the fast tokenizer"						)
					def    _UpperCAmelCase    (				self						):
						UpperCAmelCase__:	int									= AutoTokenizer.from_pretrained("facebook/opt-350m"				,      from_slow=SCREAMING_SNAKE_CASE__						)

						UpperCAmelCase__:	Union[str, Any]									= "bos"
						UpperCAmelCase__:	int									= tokenizer.get_vocab()["bos"]

						UpperCAmelCase__:	int									= "A photo of a cat"
						UpperCAmelCase__:	str									= tokenizer.encode(
						    SCREAMING_SNAKE_CASE__				,      )
						# We changed the bos token
						self.assertEqual(SCREAMING_SNAKE_CASE__				,      [3_1_9_5_7, 2_5_0, 1_3_4_5, 9, 1_0, 4_7_5_8]						)
						tokenizer.save_pretrained("./tok"						)
						UpperCAmelCase__:	Tuple									= AutoTokenizer.from_pretrained("./tok"						)
						self.assertTrue(tokenizer.is_fast						)
						UpperCAmelCase__:	int									= tokenizer.encode(
						    SCREAMING_SNAKE_CASE__				,      )
						self.assertEqual(SCREAMING_SNAKE_CASE__				,      [3_1_9_5_7, 2_5_0, 1_3_4_5, 9, 1_0, 4_7_5_8]						)




"""simple docstring"""







import warnings

from ...utils import logging
from .image_processing_deit import DeiTImageProcessor


UpperCamelCase     =							logging.get_logger(__name__)






class       UpperCamelCase__			(   _lowerCAmelCase      ):

			"""simple docstring"""


			def __init__(      self						,	*SCREAMING_SNAKE_CASE__						,	**SCREAMING_SNAKE_CASE__		)      ->	None:
				warnings.warn(
				    "The class DeiTFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"
				    " use DeiTImageProcessor instead."						,	SCREAMING_SNAKE_CASE__						,	)
				super().__init__(*SCREAMING_SNAKE_CASE__						,	**SCREAMING_SNAKE_CASE__		)




