

import json
import os
import re
import unittest

from transformers import CodeGenTokenizer, CodeGenTokenizerFast
from transformers.models.codegen.tokenization_codegen import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin






@require_tokenizers
class _UpperCamelCase							(							__A							,	unittest.TestCase   ):
			'''simple docstring'''







			lowerCamelCase__           =CodeGenTokenizer
			lowerCamelCase__           =CodeGenTokenizerFast
			lowerCamelCase__           =True
			lowerCamelCase__           ={'add_prefix_space': True}
			lowerCamelCase__           =False
			def 							__UpperCamelCase (			self							: List[Any]	)							->       Dict:

				"""simple docstring"""

				super().setUp()

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

				SCREAMING_SNAKE_CASE   :      Any				     =       os.path.join(self.tmpdirname      ,      VOCAB_FILES_NAMES["vocab_file"]	)
				SCREAMING_SNAKE_CASE   :      str				     =       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(a	) + "\n"	)
				with open(self.merges_file      ,      "w"      ,      encoding="utf-8"	) as fp:
					fp.write("\n".join(a	)	)
			def 							__UpperCamelCase (			self							: List[Any]      ,      **a							: Any	)							->       str:

				"""simple docstring"""

				kwargs.update(self.special_tokens_map	)
				return CodeGenTokenizer.from_pretrained(self.tmpdirname      ,      **a	)
			def 							__UpperCamelCase (			self							: Tuple      ,      **a							: Tuple	)							->       Dict:

				"""simple docstring"""

				kwargs.update(self.special_tokens_map	)
				return CodeGenTokenizerFast.from_pretrained(self.tmpdirname      ,      **a	)
			def 							__UpperCamelCase (			self							: Tuple      ,      a							: Tuple	)							->       Tuple:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      List[Any]				     =       "lower newer"
				SCREAMING_SNAKE_CASE   :      List[Any]				     =       "lower newer"
				return input_text, output_text
			def 							__UpperCamelCase (			self							: List[str]	)							->       Any:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Optional[int]				     =       CodeGenTokenizer(self.vocab_file      ,      self.merges_file      ,      **self.special_tokens_map	)
				SCREAMING_SNAKE_CASE   :      List[Any]				     =       "lower newer"
				SCREAMING_SNAKE_CASE   :      int				     =       ["\u0120low", "er", "\u0120", "n", "e", "w", "er"]
				SCREAMING_SNAKE_CASE   :      int				     =       tokenizer.tokenize(a      ,      add_prefix_space=a	)
				self.assertListEqual(a      ,      a	)

				SCREAMING_SNAKE_CASE   :      List[Any]				     =       tokens + [tokenizer.unk_token]
				SCREAMING_SNAKE_CASE   :      List[str]				     =       [14, 15, 10, 9, 3, 2, 15, 19]
				self.assertListEqual(tokenizer.convert_tokens_to_ids(a	)      ,      a	)
			def 							__UpperCamelCase (			self							: Optional[Any]	)							->       Dict:

				"""simple docstring"""

				if not self.test_rust_tokenizer:
					return

				SCREAMING_SNAKE_CASE   :      Tuple				     =       self.get_tokenizer()
				SCREAMING_SNAKE_CASE   :      Tuple				     =       self.get_rust_tokenizer(add_prefix_space=a	)

				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       "lower newer"

				# Testing tokenization
				SCREAMING_SNAKE_CASE   :      Tuple				     =       tokenizer.tokenize(a      ,      add_prefix_space=a	)
				SCREAMING_SNAKE_CASE   :      List[str]				     =       rust_tokenizer.tokenize(a	)
				self.assertListEqual(a      ,      a	)

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

				# Testing conversion to ids with special tokens
				SCREAMING_SNAKE_CASE   :      int				     =       self.get_rust_tokenizer(add_prefix_space=a	)
				SCREAMING_SNAKE_CASE   :      Any				     =       tokenizer.encode(a      ,      add_prefix_space=a	)
				SCREAMING_SNAKE_CASE   :      Dict				     =       rust_tokenizer.encode(a	)
				self.assertListEqual(a      ,      a	)

				# Testing the unknown token
				SCREAMING_SNAKE_CASE   :      str				     =       tokens + [rust_tokenizer.unk_token]
				SCREAMING_SNAKE_CASE   :      Optional[int]				     =       [14, 15, 10, 9, 3, 2, 15, 19]
				self.assertListEqual(rust_tokenizer.convert_tokens_to_ids(a	)      ,      a	)
			def 							__UpperCamelCase (			self							: List[str]      ,      *a							: Optional[Any]      ,      **a							: str	)							->       int:

				"""simple docstring"""

				pass
			def 							__UpperCamelCase (			self							: Dict      ,      a							: Optional[int]=15	)							->       List[Any]:

				"""simple docstring"""

				for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
					with self.subTest(F"{tokenizer.__class__.__name__} ({pretrained_name})"	):
						SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       self.rust_tokenizer_class.from_pretrained(a      ,      **a	)

						# Simple input
						SCREAMING_SNAKE_CASE   :      Optional[int]				     =       "This is a simple input"
						SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       ["This is a simple input 1", "This is a simple input 2"]
						SCREAMING_SNAKE_CASE   :      List[Any]				     =       ("This is a simple input", "This is a pair")
						SCREAMING_SNAKE_CASE   :      Optional[int]				     =       [
						    ("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(a      ,      tokenizer_r.encode      ,      a      ,      max_length=a      ,      padding="max_length"	)

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

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

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

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

						# Pair input
						self.assertRaises(
						    a      ,      tokenizer_r.batch_encode_plus      ,      a      ,      max_length=a      ,      padding="max_length"      ,      )
			def 							__UpperCamelCase (			self							: List[Any]	)							->       List[str]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       CodeGenTokenizer.from_pretrained(self.tmpdirname      ,      pad_token="<pad>"	)

				# Simple input
				SCREAMING_SNAKE_CASE   :      List[str]				     =       "This is a simple input"
				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       ["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   :      List[str]				     =       [
				    ("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   :      List[str]				     =       tokenizer.pad_token_id

				SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       tokenizer(a      ,      padding="max_length"      ,      max_length=30      ,      return_tensors="np"	)
				SCREAMING_SNAKE_CASE   :      Any				     =       tokenizer(a      ,      padding=a      ,      truncate=a      ,      return_tensors="np"	)
				SCREAMING_SNAKE_CASE   :      Tuple				     =       tokenizer(*a      ,      padding="max_length"      ,      max_length=60      ,      return_tensors="np"	)
				SCREAMING_SNAKE_CASE   :      Tuple				     =       tokenizer(a      ,      padding=a      ,      truncate=a      ,      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 							__UpperCamelCase (			self							: List[str]	)							->       Optional[Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      List[Any]				     =       "$$$"
				SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       CodeGenTokenizer.from_pretrained(self.tmpdirname      ,      bos_token=a      ,      add_bos_token=a	)

				SCREAMING_SNAKE_CASE   :      int				     =       "This is a simple input"
				SCREAMING_SNAKE_CASE   :      str				     =       ["This is a simple input 1", "This is a simple input 2"]

				SCREAMING_SNAKE_CASE   :      Any				     =       tokenizer.bos_token_id

				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       tokenizer(a	)
				SCREAMING_SNAKE_CASE   :      Optional[int]				     =       tokenizer(a	)

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

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

				self.assertEqual(decode_s.split()[0]      ,      a	)
				self.assertTrue(all(d.split()[0] == bos_token for d in decode_sa	)	)
			@slow
			def 							__UpperCamelCase (			self							: List[str]	)							->       Optional[Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Any				     =       CodeGenTokenizer.from_pretrained("Salesforce/codegen-350M-mono"	)

				SCREAMING_SNAKE_CASE   :      List[Any]				     =       "\nif len_a > len_b:\n    result = a\nelse:\n    result = b\n\n\n\n#"
				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       "\nif len_a > len_b:      result = a\nelse:      result = b"

				SCREAMING_SNAKE_CASE   :      Any				     =       tokenizer.encode(a	)
				SCREAMING_SNAKE_CASE   :      Dict				     =       ["^#", re.escape("<|endoftext|>"	), "^'''", "^\"\"\"", "\n\n\n"]
				SCREAMING_SNAKE_CASE   :      Dict				     =       tokenizer.decode(a      ,      truncate_before_pattern=a	)
				self.assertEqual(a      ,      a	)






			def 							__UpperCamelCase (			self							: Optional[int]	)							->       str:

				"""simple docstring"""

				pass


from typing import TYPE_CHECKING

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


a_						     =    {
    'configuration_bridgetower': [
        'BRIDGETOWER_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'BridgeTowerConfig',
        'BridgeTowerTextConfig',
        'BridgeTowerVisionConfig',
    ],
    'processing_bridgetower': ['BridgeTowerProcessor'],
}

try:
			if not is_vision_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			a_						     =    ['BridgeTowerImageProcessor']

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			a_						     =    [
			    'BRIDGETOWER_PRETRAINED_MODEL_ARCHIVE_LIST',
			    'BridgeTowerForContrastiveLearning',
			    'BridgeTowerForImageAndTextRetrieval',
			    'BridgeTowerForMaskedLM',
			    'BridgeTowerModel',
			    'BridgeTowerPreTrainedModel',
			]


if TYPE_CHECKING:
			from .configuration_bridgetower import (
			    BRIDGETOWER_PRETRAINED_CONFIG_ARCHIVE_MAP,
			    BridgeTowerConfig,
			    BridgeTowerTextConfig,
			    BridgeTowerVisionConfig,
			)
			from .processing_bridgetower import BridgeTowerProcessor

			try:
						if not is_vision_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .image_processing_bridgetower import BridgeTowerImageProcessor

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_bridgetower import (
						    BRIDGETOWER_PRETRAINED_MODEL_ARCHIVE_LIST,
						    BridgeTowerForContrastiveLearning,
						    BridgeTowerForImageAndTextRetrieval,
						    BridgeTowerForMaskedLM,
						    BridgeTowerModel,
						    BridgeTowerPreTrainedModel,
						)


else:
			import sys

			a_						     =    _LazyModule(__name__, globals()['__file__'], _import_structure)