






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  SCREAMING_SNAKE_CASE			(			__a      ,		unittest.TestCase					):
					'''simple docstring'''


					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       :	Optional[Any]      ):
							super().setUp()

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

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


					def 				_A       (				self       :	str					, **UpperCAmelCase_       :	Tuple      ):
							kwargs.update(self.special_tokens_map      )
							return GPTaTokenizer.from_pretrained(self.tmpdirname					, **lowerCAmelCase_      )


					def 				_A       (				self       :	Optional[int]					, **UpperCAmelCase_       :	Any      ):
							kwargs.update(self.special_tokens_map      )
							return GPTaTokenizerFast.from_pretrained(self.tmpdirname					, **lowerCAmelCase_      )


					def 				_A       (				self       :	Optional[Any]					, UpperCAmelCase_       :	int      ):
							SCREAMING_SNAKE_CASE       :      Any							=    "lower newer"
							SCREAMING_SNAKE_CASE       :      List[Any]							=    "lower newer"
							return input_text, output_text


					def 				_A       (				self       :	Any      ):
							SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    GPTaTokenizer(self.vocab_file					, self.merges_file					, **self.special_tokens_map      )
							SCREAMING_SNAKE_CASE       :      List[str]							=    "lower newer"
							SCREAMING_SNAKE_CASE       :      Optional[int]							=    ["\u0120low", "er", "\u0120", "n", "e", "w", "er"]
							SCREAMING_SNAKE_CASE       :      Dict							=    tokenizer.tokenize(lowerCAmelCase_					, add_prefix_space=lowerCAmelCase_      )
							self.assertListEqual(lowerCAmelCase_					, lowerCAmelCase_      )

							SCREAMING_SNAKE_CASE       :      List[str]							=    tokens + [tokenizer.unk_token]
							SCREAMING_SNAKE_CASE       :      int							=    [14, 15, 10, 9, 3, 2, 15, 19]
							self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCAmelCase_      )					, lowerCAmelCase_      )


					def 				_A       (				self       :	Optional[Any]      ):







							if not self.test_rust_tokenizer:
									return

							SCREAMING_SNAKE_CASE       :      List[Any]							=    self.get_tokenizer()
							SCREAMING_SNAKE_CASE       :      Any							=    self.get_rust_tokenizer(add_prefix_space=lowerCAmelCase_      )

							SCREAMING_SNAKE_CASE       :      List[Any]							=    "lower newer"

							# Testing tokenization
							SCREAMING_SNAKE_CASE       :      Any							=    tokenizer.tokenize(lowerCAmelCase_					, add_prefix_space=lowerCAmelCase_      )
							SCREAMING_SNAKE_CASE       :      Any							=    rust_tokenizer.tokenize(lowerCAmelCase_      )
							self.assertListEqual(lowerCAmelCase_					, lowerCAmelCase_      )

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

							# Testing conversion to ids with special tokens
							SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    self.get_rust_tokenizer(add_prefix_space=lowerCAmelCase_      )
							SCREAMING_SNAKE_CASE       :      int							=    tokenizer.encode(lowerCAmelCase_					, add_prefix_space=lowerCAmelCase_      )
							SCREAMING_SNAKE_CASE       :      int							=    rust_tokenizer.encode(lowerCAmelCase_      )
							self.assertListEqual(lowerCAmelCase_					, lowerCAmelCase_      )

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


					def 				_A       (				self       :	str					, *UpperCAmelCase_       :	List[Any]					, **UpperCAmelCase_       :	Optional[Any]      ):
							pass


					def 				_A       (				self       :	Dict					, UpperCAmelCase_       :	Any=15      ):







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

											# Simple input
											SCREAMING_SNAKE_CASE       :      str							=    "This is a simple input"
											SCREAMING_SNAKE_CASE       :      int							=    ["This is a simple input 1", "This is a simple input 2"]
											SCREAMING_SNAKE_CASE       :      List[str]							=    ("This is a simple input", "This is a pair")
											SCREAMING_SNAKE_CASE       :      Optional[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(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       :	Dict      ):
							SCREAMING_SNAKE_CASE       :      List[str]							=    GPTaTokenizer.from_pretrained(self.tmpdirname					, pad_token="<pad>"      )

							# Simple input
							SCREAMING_SNAKE_CASE       :      List[Any]							=    "This is a simple input"
							SCREAMING_SNAKE_CASE       :      Optional[int]							=    ["This is a simple input looooooooong", "This is a simple input"]
							SCREAMING_SNAKE_CASE       :      Dict							=    ("This is a simple input", "This is a pair")
							SCREAMING_SNAKE_CASE       :      int							=    [
							    ("This is a simple input loooooong", "This is a simple input"),
							    ("This is a simple pair loooooong", "This is a simple pair"),
							]

							SCREAMING_SNAKE_CASE       :      Tuple							=    tokenizer.pad_token_id

							SCREAMING_SNAKE_CASE       :      Optional[int]							=    tokenizer(lowerCAmelCase_					, padding="max_length"					, max_length=30					, return_tensors="np"      )
							SCREAMING_SNAKE_CASE       :      Optional[Any]							=    tokenizer(lowerCAmelCase_					, padding=lowerCAmelCase_					, truncate=lowerCAmelCase_					, return_tensors="np"      )
							SCREAMING_SNAKE_CASE       :      Optional[int]							=    tokenizer(*lowerCAmelCase_					, padding="max_length"					, max_length=60					, return_tensors="np"      )
							SCREAMING_SNAKE_CASE       :      Optional[int]							=    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       :	Any      ):
							SCREAMING_SNAKE_CASE       :      Dict							=    "$$$"
							SCREAMING_SNAKE_CASE       :      int							=    GPTaTokenizer.from_pretrained(self.tmpdirname					, bos_token=lowerCAmelCase_					, add_bos_token=lowerCAmelCase_      )

							SCREAMING_SNAKE_CASE       :      List[Any]							=    "This is a simple input"
							SCREAMING_SNAKE_CASE       :      List[Any]							=    ["This is a simple input 1", "This is a simple input 2"]

							SCREAMING_SNAKE_CASE       :      str							=    tokenizer.bos_token_id

							SCREAMING_SNAKE_CASE       :      List[str]							=    tokenizer(lowerCAmelCase_      )
							SCREAMING_SNAKE_CASE       :      Optional[Any]							=    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      )      )

							SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    tokenizer.decode(out_s.input_ids      )
							SCREAMING_SNAKE_CASE       :      Tuple							=    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       :	List[Any]      ):
							pass






					def 				_A       (				self       :	Dict      ):
							SCREAMING_SNAKE_CASE       :      Any							=    [self.get_tokenizer(do_lower_case=lowerCAmelCase_					, add_bos_token=lowerCAmelCase_      )]
							for tokenizer in tokenizers:
									with self.subTest(f'''{tokenizer.__class__.__name__}'''      ):
											SCREAMING_SNAKE_CASE       :      Any							=    "Encode this."
											SCREAMING_SNAKE_CASE       :      int							=    "This one too please."
											SCREAMING_SNAKE_CASE       :      Any							=    tokenizer.encode(lowerCAmelCase_					, add_special_tokens=lowerCAmelCase_      )
											encoded_sequence += tokenizer.encode(lowerCAmelCase_					, add_special_tokens=lowerCAmelCase_      )
											SCREAMING_SNAKE_CASE       :      Any							=    tokenizer.encode_plus(
											    lowerCAmelCase_					, lowerCAmelCase_					, add_special_tokens=lowerCAmelCase_					, return_special_tokens_mask=lowerCAmelCase_					, )
											SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    encoded_sequence_dict["input_ids"]
											SCREAMING_SNAKE_CASE       :      str							=    encoded_sequence_dict["special_tokens_mask"]
											self.assertEqual(len(lowerCAmelCase_      )					, len(lowerCAmelCase_      )      )

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





@require_tokenizers
class  SCREAMING_SNAKE_CASE			(			unittest.TestCase					):
					'''simple docstring'''


					def 				_A       (				self       :	Dict      ):
							SCREAMING_SNAKE_CASE       :      int							=    AutoTokenizer.from_pretrained("facebook/opt-350m"					, from_slow=lowerCAmelCase_      )
							SCREAMING_SNAKE_CASE       :      str							=    "A photo of a cat"

							SCREAMING_SNAKE_CASE       :      int							=    tokenizer.encode(
							    lowerCAmelCase_					, )
							self.assertEqual(lowerCAmelCase_					, [2, 250, 1345, 9, 10, 4758]      )
							tokenizer.save_pretrained("test_opt"      )

							SCREAMING_SNAKE_CASE       :      Optional[int]							=    AutoTokenizer.from_pretrained("./test_opt"      )
							SCREAMING_SNAKE_CASE       :      List[Any]							=    tokenizer.encode(
							    lowerCAmelCase_					, )
							self.assertEqual(lowerCAmelCase_					, [2, 250, 1345, 9, 10, 4758]      )


					def 				_A       (				self       :	Optional[int]      ):
							SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    AutoTokenizer.from_pretrained("facebook/opt-350m"					, use_slow=lowerCAmelCase_      )
							SCREAMING_SNAKE_CASE       :      List[str]							=    "A photo of a cat"

							SCREAMING_SNAKE_CASE       :      Optional[Any]							=    tokenizer.encode(
							    lowerCAmelCase_					, )
							# Same as above
							self.assertEqual(lowerCAmelCase_					, [2, 250, 1345, 9, 10, 4758]      )






					@unittest.skip("This test is failing because of a bug in the fast tokenizer"      )
					def 				_A       (				self       :	Dict      ):
							SCREAMING_SNAKE_CASE       :      List[Any]							=    AutoTokenizer.from_pretrained("facebook/opt-350m"					, from_slow=lowerCAmelCase_      )

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

							SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    "A photo of a cat"
							SCREAMING_SNAKE_CASE       :      str							=    tokenizer.encode(
							    lowerCAmelCase_					, )
							# We changed the bos token
							self.assertEqual(lowerCAmelCase_					, [3_1957, 250, 1345, 9, 10, 4758]      )
							tokenizer.save_pretrained("./tok"      )
							SCREAMING_SNAKE_CASE       :      Dict							=    AutoTokenizer.from_pretrained("./tok"      )
							self.assertTrue(tokenizer.is_fast      )
							SCREAMING_SNAKE_CASE       :      Optional[Any]							=    tokenizer.encode(
							    lowerCAmelCase_					, )
							self.assertEqual(lowerCAmelCase_					, [3_1957, 250, 1345, 9, 10, 4758]      )














from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


snake_case    				=		{"""configuration_swin""": ["""SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP""", """SwinConfig""", """SwinOnnxConfig"""]}


try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   snake_case    				=		[
       """SWIN_PRETRAINED_MODEL_ARCHIVE_LIST""",
       """SwinForImageClassification""",
       """SwinForMaskedImageModeling""",
       """SwinModel""",
       """SwinPreTrainedModel""",
       """SwinBackbone""",
   ]

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   snake_case    				=		[
       """TF_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST""",
       """TFSwinForImageClassification""",
       """TFSwinForMaskedImageModeling""",
       """TFSwinModel""",
       """TFSwinPreTrainedModel""",
   ]

if TYPE_CHECKING:
   from .configuration_swin import SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP, SwinConfig, SwinOnnxConfig

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_swin import (
          SWIN_PRETRAINED_MODEL_ARCHIVE_LIST,
          SwinBackbone,
          SwinForImageClassification,
          SwinForMaskedImageModeling,
          SwinModel,
          SwinPreTrainedModel,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_swin import (
          TF_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFSwinForImageClassification,
          TFSwinForMaskedImageModeling,
          TFSwinModel,
          TFSwinPreTrainedModel,
      )

else:
   import sys

   snake_case    				=		_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)






