

import tempfile
import unittest

from transformers import SPIECE_UNDERLINE, BatchEncoding, PLBartTokenizer, is_torch_available
from transformers.testing_utils import (
    get_tests_dir,
    nested_simplify,
    require_sentencepiece,
    require_tokenizers,
    require_torch,
)

from ...test_tokenization_common import TokenizerTesterMixin


a_						     =    get_tests_dir('fixtures/test_sentencepiece.model')


if is_torch_available():
			from transformers.models.plbart.modeling_plbart import shift_tokens_right

a_						     =    5_0003
a_						     =    5_0002






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







			lowerCamelCase__           =PLBartTokenizer
			lowerCamelCase__           =None
			lowerCamelCase__           =False
			def 							__UpperCamelCase (			self							: Union[str, Any]	)							->       Tuple:

				"""simple docstring"""

				super().setUp()

				# We have a SentencePiece fixture for testing
				SCREAMING_SNAKE_CASE   :      List[Any]				     =       PLBartTokenizer(a      ,      language_codes="base"      ,      keep_accents=a	)
				tokenizer.save_pretrained(self.tmpdirname	)
			def 							__UpperCamelCase (			self							: List[str]	)							->       int:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      List[Any]				     =       PLBartTokenizer(a      ,      language_codes="base"      ,      keep_accents=a	)

				SCREAMING_SNAKE_CASE   :      Tuple				     =       tokenizer.tokenize("This is a test"	)
				self.assertListEqual(a      ,      ["▁This", "▁is", "▁a", "▁t", "est"]	)

				self.assertListEqual(
				    tokenizer.convert_tokens_to_ids(a	)      ,      [value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]]      ,      )

				SCREAMING_SNAKE_CASE   :      List[Any]				     =       tokenizer.tokenize("I was born in 92000, and this is falsé."	)
				self.assertListEqual(
				    a      ,      [
				        SPIECE_UNDERLINE + "I",
				        SPIECE_UNDERLINE + "was",
				        SPIECE_UNDERLINE + "b",
				        "or",
				        "n",
				        SPIECE_UNDERLINE + "in",
				        SPIECE_UNDERLINE + "",
				        "9",
				        "2",
				        "0",
				        "0",
				        "0",
				        ",",
				        SPIECE_UNDERLINE + "and",
				        SPIECE_UNDERLINE + "this",
				        SPIECE_UNDERLINE + "is",
				        SPIECE_UNDERLINE + "f",
				        "al",
				        "s",
				        "é",
				        ".",
				    ]      ,      )
				SCREAMING_SNAKE_CASE   :      Optional[int]				     =       tokenizer.convert_tokens_to_ids(a	)
				self.assertListEqual(
				    a      ,      [
				        value + tokenizer.fairseq_offset
				        for value in [8, 21, 84, 55, 24, 19, 7, 2, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 2, 4]
				    ]      ,      )

				SCREAMING_SNAKE_CASE   :      Optional[int]				     =       tokenizer.convert_ids_to_tokens(a	)
				self.assertListEqual(
				    a      ,      [
				        SPIECE_UNDERLINE + "I",
				        SPIECE_UNDERLINE + "was",
				        SPIECE_UNDERLINE + "b",
				        "or",
				        "n",
				        SPIECE_UNDERLINE + "in",
				        SPIECE_UNDERLINE + "",
				        "<unk>",
				        "2",
				        "0",
				        "0",
				        "0",
				        ",",
				        SPIECE_UNDERLINE + "and",
				        SPIECE_UNDERLINE + "this",
				        SPIECE_UNDERLINE + "is",
				        SPIECE_UNDERLINE + "f",
				        "al",
				        "s",
				        "<unk>",
				        ".",
				    ]      ,      )

				SCREAMING_SNAKE_CASE   :      Tuple				     =       tokenizer.vocab_size
				SCREAMING_SNAKE_CASE   :      Dict				     =       [tokenizer.convert_ids_to_tokens(a	) for x in range(end - 4      ,      a	)]

				self.assertListEqual(a      ,      ["__java__", "__python__", "__en_XX__", "<mask>"]	)

				SCREAMING_SNAKE_CASE   :      Optional[int]				     =       "java.lang.Exception, python.lang.Exception, javascript, php, ruby, go"
				SCREAMING_SNAKE_CASE   :      List[str]				     =       tokenizer(a	).input_ids
				self.assertEqual(
				    tokenizer.decode(a      ,      skip_special_tokens=a      ,      clean_up_tokenization_spaces=a	)      ,      a      ,      )






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

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Tuple				     =       PLBartTokenizer(a      ,      language_codes="multi"      ,      keep_accents=a	)

				SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       tokenizer.tokenize("This is a test"	)
				self.assertListEqual(a      ,      ["▁This", "▁is", "▁a", "▁t", "est"]	)

				self.assertListEqual(
				    tokenizer.convert_tokens_to_ids(a	)      ,      [value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]]      ,      )

				SCREAMING_SNAKE_CASE   :      Tuple				     =       tokenizer.tokenize("I was born in 92000, and this is falsé."	)
				self.assertListEqual(
				    a      ,      [
				        SPIECE_UNDERLINE + "I",
				        SPIECE_UNDERLINE + "was",
				        SPIECE_UNDERLINE + "b",
				        "or",
				        "n",
				        SPIECE_UNDERLINE + "in",
				        SPIECE_UNDERLINE + "",
				        "9",
				        "2",
				        "0",
				        "0",
				        "0",
				        ",",
				        SPIECE_UNDERLINE + "and",
				        SPIECE_UNDERLINE + "this",
				        SPIECE_UNDERLINE + "is",
				        SPIECE_UNDERLINE + "f",
				        "al",
				        "s",
				        "é",
				        ".",
				    ]      ,      )
				SCREAMING_SNAKE_CASE   :      str				     =       tokenizer.convert_tokens_to_ids(a	)
				self.assertListEqual(
				    a      ,      [
				        value + tokenizer.fairseq_offset
				        for value in [8, 21, 84, 55, 24, 19, 7, 2, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 2, 4]
				    ]      ,      )

				SCREAMING_SNAKE_CASE   :      Optional[int]				     =       tokenizer.convert_ids_to_tokens(a	)
				self.assertListEqual(
				    a      ,      [
				        SPIECE_UNDERLINE + "I",
				        SPIECE_UNDERLINE + "was",
				        SPIECE_UNDERLINE + "b",
				        "or",
				        "n",
				        SPIECE_UNDERLINE + "in",
				        SPIECE_UNDERLINE + "",
				        "<unk>",
				        "2",
				        "0",
				        "0",
				        "0",
				        ",",
				        SPIECE_UNDERLINE + "and",
				        SPIECE_UNDERLINE + "this",
				        SPIECE_UNDERLINE + "is",
				        SPIECE_UNDERLINE + "f",
				        "al",
				        "s",
				        "<unk>",
				        ".",
				    ]      ,      )
				SCREAMING_SNAKE_CASE   :      int				     =       tokenizer.vocab_size
				SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       [tokenizer.convert_ids_to_tokens(a	) for x in range(end - 7      ,      a	)]

				self.assertListEqual(
				    a      ,      ["__java__", "__python__", "__en_XX__", "__javascript__", "__php__", "__ruby__", "__go__"]	)
				SCREAMING_SNAKE_CASE   :      int				     =       "java.lang.Exception, python.lang.Exception, javascript, php, ruby, go"
				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       tokenizer(a	).input_ids
				self.assertEqual(
				    tokenizer.decode(a      ,      skip_special_tokens=a      ,      clean_up_tokenization_spaces=a	)      ,      a      ,      )






@require_torch
@require_sentencepiece
@require_tokenizers
class _UpperCamelCase							(							unittest.TestCase   ):
			'''simple docstring'''







			lowerCamelCase__           ='uclanlp/plbart-python-en_XX'
			lowerCamelCase__           =[
			    'def maximum(a,b,c):NEW_LINE_INDENTreturn max([a,b,c])',
			    'def sum(a,b,c):NEW_LINE_INDENTreturn sum([a,b,c])',
			]
			lowerCamelCase__           =[
			    'Returns the maximum value of a b c.',
			    'Sums the values of a b c.',
			]
			lowerCamelCase__           =[
			    134,
			    5452,
			    33460,
			    33441,
			    33463,
			    33465,
			    33463,
			    33449,
			    988,
			    20,
			    33456,
			    19,
			    33456,
			    771,
			    39,
			    4258,
			    889,
			    3318,
			    33441,
			    33463,
			    33465,
			    33463,
			    33449,
			    2471,
			    2,
			    PYTHON_CODE,
			]
			@classmethod
			def 							__UpperCamelCase (			cls							: int	)							->       int:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      PLBartTokenizer				     =       PLBartTokenizer.from_pretrained(
				    cls.checkpoint_name      ,      language_codes="base"      ,      src_lang="python"      ,      tgt_lang="en_XX"	)
				SCREAMING_SNAKE_CASE   :      List[Any]				     =       1
				return cls
			def 							__UpperCamelCase (			self							: Optional[int]	)							->       Optional[Any]:

				"""simple docstring"""

				self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["__java__"]      ,      5_0001	)
				self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["__python__"]      ,      5_0002	)
				self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["__en_XX__"]      ,      5_0003	)
			def 							__UpperCamelCase (			self							: int	)							->       List[str]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       self.tokenizer.batch_encode_plus(self.src_text	).input_ids[0]
				self.assertListEqual(self.expected_src_tokens      ,      a	)
			def 							__UpperCamelCase (			self							: Union[str, Any]	)							->       Dict:

				"""simple docstring"""

				self.assertIn(a      ,      self.tokenizer.all_special_ids	)
				SCREAMING_SNAKE_CASE   :      Dict				     =       [EN_CODE, 9037, 3_3442, 57, 752, 153, 14, 56, 18, 9, 2]
				SCREAMING_SNAKE_CASE   :      Tuple				     =       self.tokenizer.decode(a      ,      skip_special_tokens=a	)
				SCREAMING_SNAKE_CASE   :      str				     =       self.tokenizer.decode(generated_ids[1:]      ,      skip_special_tokens=a	)
				self.assertEqual(a      ,      a	)
				self.assertNotIn(self.tokenizer.eos_token      ,      a	)
			def 							__UpperCamelCase (			self							: Optional[Any]	)							->       Dict:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       ["def sum(a,b,c):NEW_LINE_INDENTreturn sum([a,b,c])" * 20]
				self.assertIsInstance(src_text[0]      ,      a	)
				SCREAMING_SNAKE_CASE   :      Tuple				     =       10
				SCREAMING_SNAKE_CASE   :      int				     =       self.tokenizer(a      ,      max_length=a      ,      truncation=a	).input_ids[0]
				self.assertEqual(ids[-2]      ,      2	)
				self.assertEqual(ids[-1]      ,      a	)
				self.assertEqual(len(a	)      ,      a	)
			def 							__UpperCamelCase (			self							: str	)							->       Tuple:

				"""simple docstring"""

				self.assertListEqual(self.tokenizer.convert_tokens_to_ids(["<mask>", "__java__"]	)      ,      [5_0004, 5_0001]	)
			def 							__UpperCamelCase (			self							: Optional[int]	)							->       str:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Tuple				     =       tempfile.mkdtemp()
				SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       self.tokenizer.fairseq_tokens_to_ids
				self.tokenizer.save_pretrained(a	)
				SCREAMING_SNAKE_CASE   :      Dict				     =       PLBartTokenizer.from_pretrained(a	)
				self.assertDictEqual(new_tok.fairseq_tokens_to_ids      ,      a	)
			@require_torch
			def 							__UpperCamelCase (			self							: List[Any]	)							->       str:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      int				     =       self.tokenizer(self.src_text      ,      text_target=self.tgt_text      ,      padding=a      ,      return_tensors="pt"	)
				SCREAMING_SNAKE_CASE   :      Tuple				     =       shift_tokens_right(batch["labels"]      ,      self.tokenizer.pad_token_id	)

				# fairseq batch: https://gist.github.com/sshleifer/cba08bc2109361a74ac3760a7e30e4f4
				self.assertEqual(batch.input_ids[1][-2:].tolist()      ,      [2, PYTHON_CODE]	)
				self.assertEqual(batch.decoder_input_ids[1][0]      ,      a	)
				self.assertEqual(batch.decoder_input_ids[1][-1]      ,      2	)
				self.assertEqual(batch.labels[1][-2:].tolist()      ,      [2, EN_CODE]	)
			@require_torch
			def 							__UpperCamelCase (			self							: Any	)							->       int:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      int				     =       self.tokenizer(
				    self.src_text      ,      text_target=self.tgt_text      ,      padding=a      ,      truncation=a      ,      max_length=len(self.expected_src_tokens	)      ,      return_tensors="pt"      ,      )
				SCREAMING_SNAKE_CASE   :      List[Any]				     =       shift_tokens_right(batch["labels"]      ,      self.tokenizer.pad_token_id	)

				self.assertIsInstance(a      ,      a	)

				self.assertEqual((2, 26)      ,      batch.input_ids.shape	)
				self.assertEqual((2, 26)      ,      batch.attention_mask.shape	)
				SCREAMING_SNAKE_CASE   :      Optional[int]				     =       batch.input_ids.tolist()[0]
				self.assertListEqual(self.expected_src_tokens      ,      a	)
				self.assertEqual(2      ,      batch.decoder_input_ids[0, -1]	)  # EOS
				# Test that special tokens are reset
				self.assertEqual(self.tokenizer.prefix_tokens      ,      []	)
				self.assertEqual(self.tokenizer.suffix_tokens      ,      [self.tokenizer.eos_token_id, PYTHON_CODE]	)
			def 							__UpperCamelCase (			self							: Tuple	)							->       Union[str, Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       self.tokenizer(self.src_text      ,      padding=a      ,      truncation=a      ,      max_length=3      ,      return_tensors="pt"	)
				SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       self.tokenizer(
				    text_target=self.tgt_text      ,      padding=a      ,      truncation=a      ,      max_length=10      ,      return_tensors="pt"	)
				SCREAMING_SNAKE_CASE   :      str				     =       targets["input_ids"]
				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       shift_tokens_right(a      ,      self.tokenizer.pad_token_id	)

				self.assertEqual(batch.input_ids.shape[1]      ,      3	)
				self.assertEqual(batch.decoder_input_ids.shape[1]      ,      10	)






			@require_torch
			def 							__UpperCamelCase (			self							: Tuple	)							->       Dict:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Dict				     =       self.tokenizer._build_translation_inputs(
				    "A test"      ,      return_tensors="pt"      ,      src_lang="en_XX"      ,      tgt_lang="java"	)

				self.assertEqual(
				    nested_simplify(a	)      ,      {
				        # A, test, EOS, en_XX
				        "input_ids": [[150, 242, 2, 5_0003]],
				        "attention_mask": [[1, 1, 1, 1]],
				        # java
				        "forced_bos_token_id": 5_0001,
				    }      ,      )


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)