
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        lowerCAmelCase__(   __a		,	unittest.TestCase	):
						UpperCamelCase_							: Tuple	=		GPTaTokenizer
						UpperCamelCase_							: Optional[int]	=		GPTaTokenizerFast
						UpperCamelCase_							: Optional[int]	=		True
						UpperCamelCase_							: Optional[Any]	=		{"add_prefix_space": True}
						UpperCamelCase_							: List[str]	=		False

						def  A_				(			self				)					->      Dict:


								'''simple docstring'''

								super().setUp()

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

								_UpperCamelCase						    =	os.path.join(self.tmpdirname       ,							VOCAB_FILES_NAMES["""vocab_file"""]				)
								_UpperCamelCase						    =	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(lowerCAmelCase_				) + """\n"""				)
								with open(self.merges_file       ,							"""w"""       ,							encoding="""utf-8"""				) as fp:
										fp.write("""\n""".join(lowerCAmelCase_				)				)

						def  A_				(			self       ,							**a				)					->      str:


								'''simple docstring'''

								kwargs.update(self.special_tokens_map				)
								return GPTaTokenizer.from_pretrained(self.tmpdirname       ,							**lowerCAmelCase_				)

						def  A_				(			self       ,							**a				)					->      List[str]:


								'''simple docstring'''

								kwargs.update(self.special_tokens_map				)
								return GPTaTokenizerFast.from_pretrained(self.tmpdirname       ,							**lowerCAmelCase_				)

						def  A_				(			self       ,							a				)					->      List[Any]:


								'''simple docstring'''

								_UpperCamelCase						    =	"""lower newer"""
								_UpperCamelCase						    =	"""lower newer"""
								return input_text, output_text

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


								'''simple docstring'''

								_UpperCamelCase						    =	GPTaTokenizer(self.vocab_file       ,							self.merges_file       ,							**self.special_tokens_map				)
								_UpperCamelCase						    =	"""lower newer"""
								_UpperCamelCase						    =	["""\u0120low""", """er""", """\u0120""", """n""", """e""", """w""", """er"""]
								_UpperCamelCase						    =	tokenizer.tokenize(lowerCAmelCase_       ,							add_prefix_space=lowerCAmelCase_				)
								self.assertListEqual(lowerCAmelCase_       ,							lowerCAmelCase_				)

								_UpperCamelCase						    =	tokens + [tokenizer.unk_token]
								_UpperCamelCase						    =	[14, 15, 10, 9, 3, 2, 15, 19]
								self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCAmelCase_				)       ,							lowerCAmelCase_				)

						def  A_				(			self				)					->      Any:


								'''simple docstring'''

								if not self.test_rust_tokenizer:
										return

								_UpperCamelCase						    =	self.get_tokenizer()
								_UpperCamelCase						    =	self.get_rust_tokenizer(add_prefix_space=lowerCAmelCase_				)

								_UpperCamelCase						    =	"""lower newer"""

								# Testing tokenization
								_UpperCamelCase						    =	tokenizer.tokenize(lowerCAmelCase_       ,							add_prefix_space=lowerCAmelCase_				)
								_UpperCamelCase						    =	rust_tokenizer.tokenize(lowerCAmelCase_				)
								self.assertListEqual(lowerCAmelCase_       ,							lowerCAmelCase_				)

								# Testing conversion to ids without special tokens
								_UpperCamelCase						    =	tokenizer.encode(lowerCAmelCase_       ,							add_special_tokens=lowerCAmelCase_       ,							add_prefix_space=lowerCAmelCase_				)
								_UpperCamelCase						    =	rust_tokenizer.encode(lowerCAmelCase_       ,							add_special_tokens=lowerCAmelCase_				)
								self.assertListEqual(lowerCAmelCase_       ,							lowerCAmelCase_				)

								# Testing conversion to ids with special tokens
								_UpperCamelCase						    =	self.get_rust_tokenizer(add_prefix_space=lowerCAmelCase_				)
								_UpperCamelCase						    =	tokenizer.encode(lowerCAmelCase_       ,							add_prefix_space=lowerCAmelCase_				)
								_UpperCamelCase						    =	rust_tokenizer.encode(lowerCAmelCase_				)
								self.assertListEqual(lowerCAmelCase_       ,							lowerCAmelCase_				)

								# Testing the unknown token
								_UpperCamelCase						    =	tokens + [rust_tokenizer.unk_token]
								_UpperCamelCase						    =	[14, 15, 10, 9, 3, 2, 15, 19]
								self.assertListEqual(rust_tokenizer.convert_tokens_to_ids(lowerCAmelCase_				)       ,							lowerCAmelCase_				)

						def  A_				(			self       ,							*a       ,							**a				)					->      Optional[int]:


								'''simple docstring'''

								pass

						def  A_				(			self       ,							a=15				)					->      int:


								'''simple docstring'''

								for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
										with self.subTest(F'{tokenizer.__class__.__name__} ({pretrained_name})'				):
												_UpperCamelCase						    =	self.rust_tokenizer_class.from_pretrained(lowerCAmelCase_       ,							**lowerCAmelCase_				)

												# Simple input
												_UpperCamelCase						    =	"""This is a simple input"""
												_UpperCamelCase						    =	["""This is a simple input 1""", """This is a simple input 2"""]
												_UpperCamelCase						    =	("""This is a simple input""", """This is a pair""")
												_UpperCamelCase						    =	[
												    ("""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(lowerCAmelCase_       ,							tokenizer_r.encode       ,							lowerCAmelCase_       ,							max_length=lowerCAmelCase_       ,							padding="""max_length"""				)

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

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

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

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

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

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


								'''simple docstring'''

								_UpperCamelCase						    =	GPTaTokenizer.from_pretrained(self.tmpdirname       ,							pad_token="""<pad>"""				)

								# Simple input
								_UpperCamelCase						    =	"""This is a simple input"""
								_UpperCamelCase						    =	["""This is a simple input looooooooong""", """This is a simple input"""]
								_UpperCamelCase						    =	("""This is a simple input""", """This is a pair""")
								_UpperCamelCase						    =	[
								    ("""This is a simple input loooooong""", """This is a simple input"""),
								    ("""This is a simple pair loooooong""", """This is a simple pair"""),
								]

								_UpperCamelCase						    =	tokenizer.pad_token_id

								_UpperCamelCase						    =	tokenizer(lowerCAmelCase_       ,							padding="""max_length"""       ,							max_length=30       ,							return_tensors="""np"""				)
								_UpperCamelCase						    =	tokenizer(lowerCAmelCase_       ,							padding=lowerCAmelCase_       ,							truncate=lowerCAmelCase_       ,							return_tensors="""np"""				)
								_UpperCamelCase						    =	tokenizer(*lowerCAmelCase_       ,							padding="""max_length"""       ,							max_length=60       ,							return_tensors="""np"""				)
								_UpperCamelCase						    =	tokenizer(lowerCAmelCase_       ,							padding=lowerCAmelCase_       ,							truncate=lowerCAmelCase_       ,							return_tensors="""np"""				)

								# s
								# test single string max_length padding
								self.assertEqual(out_s["""input_ids"""].shape[-1]       ,							30				)
								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]       ,							33				)
								# 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]       ,							60				)
								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]       ,							52				)
								# 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  A_				(			self				)					->      List[Any]:


								'''simple docstring'''

								_UpperCamelCase						    =	"""$$$"""
								_UpperCamelCase						    =	GPTaTokenizer.from_pretrained(self.tmpdirname       ,							bos_token=lowerCAmelCase_       ,							add_bos_token=lowerCAmelCase_				)

								_UpperCamelCase						    =	"""This is a simple input"""
								_UpperCamelCase						    =	["""This is a simple input 1""", """This is a simple input 2"""]

								_UpperCamelCase						    =	tokenizer.bos_token_id

								_UpperCamelCase						    =	tokenizer(lowerCAmelCase_				)
								_UpperCamelCase						    =	tokenizer(lowerCAmelCase_				)

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

								_UpperCamelCase						    =	tokenizer.decode(out_s.input_ids				)
								_UpperCamelCase						    =	tokenizer.batch_decode(out_sa.input_ids				)

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

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


								'''simple docstring'''

								pass







						def  A_				(			self				)					->      Any:


								'''simple docstring'''

								_UpperCamelCase						    =	[self.get_tokenizer(do_lower_case=lowerCAmelCase_       ,							add_bos_token=lowerCAmelCase_				)]
								for tokenizer in tokenizers:
										with self.subTest(F'{tokenizer.__class__.__name__}'				):
												_UpperCamelCase						    =	"""Encode this."""
												_UpperCamelCase						    =	"""This one too please."""
												_UpperCamelCase						    =	tokenizer.encode(lowerCAmelCase_       ,							add_special_tokens=lowerCAmelCase_				)
												encoded_sequence += tokenizer.encode(lowerCAmelCase_       ,							add_special_tokens=lowerCAmelCase_				)
												_UpperCamelCase						    =	tokenizer.encode_plus(
												    lowerCAmelCase_       ,							lowerCAmelCase_       ,							add_special_tokens=lowerCAmelCase_       ,							return_special_tokens_mask=lowerCAmelCase_       ,							)
												_UpperCamelCase						    =	encoded_sequence_dict["""input_ids"""]
												_UpperCamelCase						    =	encoded_sequence_dict["""special_tokens_mask"""]
												self.assertEqual(len(lowerCAmelCase_				)       ,							len(lowerCAmelCase_				)				)

												_UpperCamelCase						    =	[
												    (x if not special_tokens_mask[i] else None) for i, x in enumerate(lowerCAmelCase_				)
												]
												_UpperCamelCase						    =	[x for x in filtered_sequence if x is not None]
												self.assertEqual(lowerCAmelCase_       ,							lowerCAmelCase_				)







@require_tokenizers
class        lowerCAmelCase__(   unittest.TestCase	):

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


								'''simple docstring'''

								_UpperCamelCase						    =	AutoTokenizer.from_pretrained("""facebook/opt-350m"""       ,							from_slow=lowerCAmelCase_				)
								_UpperCamelCase						    =	"""A photo of a cat"""

								_UpperCamelCase						    =	tokenizer.encode(
								    lowerCAmelCase_       ,							)
								self.assertEqual(lowerCAmelCase_       ,							[2, 2_50, 13_45, 9, 10, 47_58]				)
								tokenizer.save_pretrained("""test_opt"""				)

								_UpperCamelCase						    =	AutoTokenizer.from_pretrained("""./test_opt"""				)
								_UpperCamelCase						    =	tokenizer.encode(
								    lowerCAmelCase_       ,							)
								self.assertEqual(lowerCAmelCase_       ,							[2, 2_50, 13_45, 9, 10, 47_58]				)

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


								'''simple docstring'''

								_UpperCamelCase						    =	AutoTokenizer.from_pretrained("""facebook/opt-350m"""       ,							use_slow=lowerCAmelCase_				)
								_UpperCamelCase						    =	"""A photo of a cat"""

								_UpperCamelCase						    =	tokenizer.encode(
								    lowerCAmelCase_       ,							)
								# Same as above
								self.assertEqual(lowerCAmelCase_       ,							[2, 2_50, 13_45, 9, 10, 47_58]				)







						@unittest.skip("""This test is failing because of a bug in the fast tokenizer"""				)
						def  A_				(			self				)					->      str:


								'''simple docstring'''

								_UpperCamelCase						    =	AutoTokenizer.from_pretrained("""facebook/opt-350m"""       ,							from_slow=lowerCAmelCase_				)

								_UpperCamelCase						    =	"""bos"""
								_UpperCamelCase						    =	tokenizer.get_vocab()["""bos"""]

								_UpperCamelCase						    =	"""A photo of a cat"""
								_UpperCamelCase						    =	tokenizer.encode(
								    lowerCAmelCase_       ,							)
								# We changed the bos token
								self.assertEqual(lowerCAmelCase_       ,							[3_19_57, 2_50, 13_45, 9, 10, 47_58]				)
								tokenizer.save_pretrained("""./tok"""				)
								_UpperCamelCase						    =	AutoTokenizer.from_pretrained("""./tok"""				)
								self.assertTrue(tokenizer.is_fast				)
								_UpperCamelCase						    =	tokenizer.encode(
								    lowerCAmelCase_       ,							)
								self.assertEqual(lowerCAmelCase_       ,							[3_19_57, 2_50, 13_45, 9, 10, 47_58]				)





import warnings
from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType







class        lowerCAmelCase__      (   __lowercase	):
						UpperCamelCase_							: Dict	=		["image_processor", "tokenizer"]
						UpperCamelCase_							: Union[str, Any]	=		"ViltImageProcessor"
						UpperCamelCase_							: List[str]	=		("BertTokenizer", "BertTokenizerFast")

						def __init__(			self       ,							a=None       ,							a=None       ,							**a				)					->      Optional[int]:


								'''simple docstring'''

								_UpperCamelCase						    =	None
								if "feature_extractor" in kwargs:
										warnings.warn(
										    """The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
										    """ instead."""       ,							a       ,							)
										_UpperCamelCase						    =	kwargs.pop("""feature_extractor"""				)

								_UpperCamelCase						    =	image_processor if image_processor is not None else feature_extractor
								if image_processor is None:
										raise ValueError("""You need to specify an `image_processor`."""				)
								if tokenizer is None:
										raise ValueError("""You need to specify a `tokenizer`."""				)

								super().__init__(a       ,							a				)
								_UpperCamelCase						    =	self.image_processor

						def __call__(			self       ,							a       ,							a = None       ,							a = True       ,							a = False       ,							a = None       ,							a = None       ,							a = 0       ,							a = None       ,							a = None       ,							a = None       ,							a = False       ,							a = False       ,							a = False       ,							a = False       ,							a = True       ,							a = None       ,							**a       ,							)					->      BatchEncoding:


								'''simple docstring'''

								_UpperCamelCase						    =	self.tokenizer(
								    text=a       ,							add_special_tokens=a       ,							padding=a       ,							truncation=a       ,							max_length=a       ,							stride=a       ,							pad_to_multiple_of=a       ,							return_token_type_ids=a       ,							return_attention_mask=a       ,							return_overflowing_tokens=a       ,							return_special_tokens_mask=a       ,							return_offsets_mapping=a       ,							return_length=a       ,							verbose=a       ,							return_tensors=a       ,							**a       ,							)
								# add pixel_values + pixel_mask
								_UpperCamelCase						    =	self.image_processor(a       ,							return_tensors=a				)
								encoding.update(a				)

								return encoding

						def  A_				(			self       ,							*a       ,							**a				)					->      Optional[Any]:


								'''simple docstring'''

								return self.tokenizer.batch_decode(*a       ,							**a				)

						def  A_				(			self       ,							*a       ,							**a				)					->      int:


								'''simple docstring'''

								return self.tokenizer.decode(*a       ,							**a				)

						@property
						def  A_				(			self				)					->      Optional[Any]:


								'''simple docstring'''

								_UpperCamelCase						    =	self.tokenizer.model_input_names
								_UpperCamelCase						    =	self.image_processor.model_input_names
								return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names				)				)

						@property
						def  A_				(			self				)					->      Tuple:


								'''simple docstring'''

								warnings.warn(
								    """`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."""       ,							a       ,							)
								return self.image_processor_class







						@property
						def  A_				(			self				)					->      Dict:


								'''simple docstring'''

								warnings.warn(
								    """`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."""       ,							a       ,							)
								return self.image_processor



