

"""simple docstring"""




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_							:			List[str]											= get_tests_dir("fixtures/test_sentencepiece.model")


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

A_							:			str											= 50_003
A_							:			Tuple											= 50_002





@require_sentencepiece
@require_tokenizers
class    lowerCamelCase						(A__			,unittest.TestCase			):
			lowerCamelCase__   :		Optional[Any]							=							PLBartTokenizer
			lowerCamelCase__   :		int							=							None
			lowerCamelCase__   :		Optional[Any]							=							False

			def 					SCREAMING_SNAKE_CASE				(					self				:						str       )   ->    List[Any]:
						super().setUp()

						# We have a SentencePiece fixture for testing
						SCREAMING_SNAKE_CASE__								=      PLBartTokenizer(_UpperCAmelCase    ,				language_codes="""base"""    ,				keep_accents=_UpperCAmelCase       )
						tokenizer.save_pretrained(self.tmpdirname       )

			def 					SCREAMING_SNAKE_CASE				(					self				:						Optional[int]       )   ->    Optional[Any]:
						SCREAMING_SNAKE_CASE__								=      PLBartTokenizer(_UpperCAmelCase    ,				language_codes="""base"""    ,				keep_accents=_UpperCAmelCase       )

						SCREAMING_SNAKE_CASE__								=      tokenizer.tokenize("""This is a test"""       )
						self.assertListEqual(_UpperCAmelCase    ,				["""▁This""", """▁is""", """▁a""", """▁t""", """est"""]       )

						self.assertListEqual(
						    tokenizer.convert_tokens_to_ids(_UpperCAmelCase       )    ,				[value + tokenizer.fairseq_offset for value in [2_8_5, 4_6, 1_0, 1_7_0, 3_8_2]]    ,				)

						SCREAMING_SNAKE_CASE__								=      tokenizer.tokenize("""I was born in 92000, and this is falsé."""       )
						self.assertListEqual(
						    _UpperCAmelCase    ,				[
						        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__								=      tokenizer.convert_tokens_to_ids(_UpperCAmelCase       )
						self.assertListEqual(
						    _UpperCAmelCase    ,				[
						        value + tokenizer.fairseq_offset
						        for value in [8, 2_1, 8_4, 5_5, 2_4, 1_9, 7, 2, 6_0_2, 3_4_7, 3_4_7, 3_4_7, 3, 1_2, 6_6, 4_6, 7_2, 8_0, 6, 2, 4]
						    ]    ,				)

						SCREAMING_SNAKE_CASE__								=      tokenizer.convert_ids_to_tokens(_UpperCAmelCase       )
						self.assertListEqual(
						    _UpperCAmelCase    ,				[
						        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__								=      tokenizer.vocab_size
						SCREAMING_SNAKE_CASE__								=      [tokenizer.convert_ids_to_tokens(_UpperCAmelCase       ) for x in range(end - 4    ,				_UpperCAmelCase       )]

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

						SCREAMING_SNAKE_CASE__								=      """java.lang.Exception, python.lang.Exception, javascript, php, ruby, go"""
						SCREAMING_SNAKE_CASE__								=      tokenizer(_UpperCAmelCase       ).input_ids
						self.assertEqual(
						    tokenizer.decode(_UpperCAmelCase    ,				skip_special_tokens=_UpperCAmelCase    ,				clean_up_tokenization_spaces=_UpperCAmelCase       )    ,				_UpperCAmelCase    ,				)





			def 					SCREAMING_SNAKE_CASE				(					self				:						Optional[Any]       )   ->    int:
						SCREAMING_SNAKE_CASE__								=      PLBartTokenizer(_UpperCAmelCase    ,				language_codes="""multi"""    ,				keep_accents=_UpperCAmelCase       )

						SCREAMING_SNAKE_CASE__								=      tokenizer.tokenize("""This is a test"""       )
						self.assertListEqual(_UpperCAmelCase    ,				["""▁This""", """▁is""", """▁a""", """▁t""", """est"""]       )

						self.assertListEqual(
						    tokenizer.convert_tokens_to_ids(_UpperCAmelCase       )    ,				[value + tokenizer.fairseq_offset for value in [2_8_5, 4_6, 1_0, 1_7_0, 3_8_2]]    ,				)

						SCREAMING_SNAKE_CASE__								=      tokenizer.tokenize("""I was born in 92000, and this is falsé."""       )
						self.assertListEqual(
						    _UpperCAmelCase    ,				[
						        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__								=      tokenizer.convert_tokens_to_ids(_UpperCAmelCase       )
						self.assertListEqual(
						    _UpperCAmelCase    ,				[
						        value + tokenizer.fairseq_offset
						        for value in [8, 2_1, 8_4, 5_5, 2_4, 1_9, 7, 2, 6_0_2, 3_4_7, 3_4_7, 3_4_7, 3, 1_2, 6_6, 4_6, 7_2, 8_0, 6, 2, 4]
						    ]    ,				)

						SCREAMING_SNAKE_CASE__								=      tokenizer.convert_ids_to_tokens(_UpperCAmelCase       )
						self.assertListEqual(
						    _UpperCAmelCase    ,				[
						        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__								=      tokenizer.vocab_size
						SCREAMING_SNAKE_CASE__								=      [tokenizer.convert_ids_to_tokens(_UpperCAmelCase       ) for x in range(end - 7    ,				_UpperCAmelCase       )]

						self.assertListEqual(
						    _UpperCAmelCase    ,				["""__java__""", """__python__""", """__en_XX__""", """__javascript__""", """__php__""", """__ruby__""", """__go__"""]       )
						SCREAMING_SNAKE_CASE__								=      """java.lang.Exception, python.lang.Exception, javascript, php, ruby, go"""
						SCREAMING_SNAKE_CASE__								=      tokenizer(_UpperCAmelCase       ).input_ids
						self.assertEqual(
						    tokenizer.decode(_UpperCAmelCase    ,				skip_special_tokens=_UpperCAmelCase    ,				clean_up_tokenization_spaces=_UpperCAmelCase       )    ,				_UpperCAmelCase    ,				)





@require_torch
@require_sentencepiece
@require_tokenizers
class    lowerCamelCase						(unittest.TestCase			):
			lowerCamelCase__   :		str							=							'uclanlp/plbart-python-en_XX'
			lowerCamelCase__   :		List[Any]							=							[
			    '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__   :		int							=							[
			    'Returns the maximum value of a b c.',
			    'Sums the values of a b c.',
			]
			lowerCamelCase__   :		Optional[int]							=							[
			    1_3_4,
			    5_4_5_2,
			    3_3_4_6_0,
			    3_3_4_4_1,
			    3_3_4_6_3,
			    3_3_4_6_5,
			    3_3_4_6_3,
			    3_3_4_4_9,
			    9_8_8,
			    2_0,
			    3_3_4_5_6,
			    1_9,
			    3_3_4_5_6,
			    7_7_1,
			    3_9,
			    4_2_5_8,
			    8_8_9,
			    3_3_1_8,
			    3_3_4_4_1,
			    3_3_4_6_3,
			    3_3_4_6_5,
			    3_3_4_6_3,
			    3_3_4_4_9,
			    2_4_7_1,
			    2,
			    PYTHON_CODE,
			]

			@classmethod
			def 					SCREAMING_SNAKE_CASE				(					cls				:						int       )   ->    Dict:
						SCREAMING_SNAKE_CASE__								=      PLBartTokenizer.from_pretrained(
						    cls.checkpoint_name    ,				language_codes="""base"""    ,				src_lang="""python"""    ,				tgt_lang="""en_XX"""       )
						SCREAMING_SNAKE_CASE__								=      1
						return cls

			def 					SCREAMING_SNAKE_CASE				(					self				:						List[Any]       )   ->    Optional[Any]:
						self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["""__java__"""]    ,				5_0_0_0_1       )
						self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["""__python__"""]    ,				5_0_0_0_2       )
						self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["""__en_XX__"""]    ,				5_0_0_0_3       )

			def 					SCREAMING_SNAKE_CASE				(					self				:						int       )   ->    Union[str, Any]:
						SCREAMING_SNAKE_CASE__								=      self.tokenizer.batch_encode_plus(self.src_text       ).input_ids[0]
						self.assertListEqual(self.expected_src_tokens    ,				_UpperCAmelCase       )

			def 					SCREAMING_SNAKE_CASE				(					self				:						List[str]       )   ->    Union[str, Any]:
						self.assertIn(_UpperCAmelCase    ,				self.tokenizer.all_special_ids       )
						SCREAMING_SNAKE_CASE__								=      [EN_CODE, 9_0_3_7, 3_3_4_4_2, 5_7, 7_5_2, 1_5_3, 1_4, 5_6, 1_8, 9, 2]
						SCREAMING_SNAKE_CASE__								=      self.tokenizer.decode(_UpperCAmelCase    ,				skip_special_tokens=_UpperCAmelCase       )
						SCREAMING_SNAKE_CASE__								=      self.tokenizer.decode(generated_ids[1:]    ,				skip_special_tokens=_UpperCAmelCase       )
						self.assertEqual(_UpperCAmelCase    ,				_UpperCAmelCase       )
						self.assertNotIn(self.tokenizer.eos_token    ,				_UpperCAmelCase       )

			def 					SCREAMING_SNAKE_CASE				(					self				:						int       )   ->    int:
						SCREAMING_SNAKE_CASE__								=      ["""def sum(a,b,c):NEW_LINE_INDENTreturn sum([a,b,c])""" * 2_0]
						self.assertIsInstance(src_text[0]    ,				_UpperCAmelCase       )
						SCREAMING_SNAKE_CASE__								=      1_0
						SCREAMING_SNAKE_CASE__								=      self.tokenizer(_UpperCAmelCase    ,				max_length=_UpperCAmelCase    ,				truncation=_UpperCAmelCase       ).input_ids[0]
						self.assertEqual(ids[-2]    ,				2       )
						self.assertEqual(ids[-1]    ,				_UpperCAmelCase       )
						self.assertEqual(len(_UpperCAmelCase       )    ,				_UpperCAmelCase       )

			def 					SCREAMING_SNAKE_CASE				(					self				:						Optional[Any]       )   ->    Optional[int]:
						self.assertListEqual(self.tokenizer.convert_tokens_to_ids(["""<mask>""", """__java__"""]       )    ,				[5_0_0_0_4, 5_0_0_0_1]       )

			def 					SCREAMING_SNAKE_CASE				(					self				:						Optional[Any]       )   ->    List[str]:
						SCREAMING_SNAKE_CASE__								=      tempfile.mkdtemp()
						SCREAMING_SNAKE_CASE__								=      self.tokenizer.fairseq_tokens_to_ids
						self.tokenizer.save_pretrained(_UpperCAmelCase       )
						SCREAMING_SNAKE_CASE__								=      PLBartTokenizer.from_pretrained(_UpperCAmelCase       )
						self.assertDictEqual(new_tok.fairseq_tokens_to_ids    ,				_UpperCAmelCase       )

			@require_torch
			def 					SCREAMING_SNAKE_CASE				(					self				:						Optional[int]       )   ->    List[Any]:
						SCREAMING_SNAKE_CASE__								=      self.tokenizer(self.src_text    ,				text_target=self.tgt_text    ,				padding=_UpperCAmelCase    ,				return_tensors="""pt"""       )
						SCREAMING_SNAKE_CASE__								=      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]    ,				_UpperCAmelCase       )
						self.assertEqual(batch.decoder_input_ids[1][-1]    ,				2       )
						self.assertEqual(batch.labels[1][-2:].tolist()    ,				[2, EN_CODE]       )

			@require_torch
			def 					SCREAMING_SNAKE_CASE				(					self				:						int       )   ->    str:
						SCREAMING_SNAKE_CASE__								=      self.tokenizer(
						    self.src_text    ,				text_target=self.tgt_text    ,				padding=_UpperCAmelCase    ,				truncation=_UpperCAmelCase    ,				max_length=len(self.expected_src_tokens       )    ,				return_tensors="""pt"""    ,				)
						SCREAMING_SNAKE_CASE__								=      shift_tokens_right(batch["""labels"""]    ,				self.tokenizer.pad_token_id       )

						self.assertIsInstance(_UpperCAmelCase    ,				_UpperCAmelCase       )

						self.assertEqual((2, 2_6)    ,				batch.input_ids.shape       )
						self.assertEqual((2, 2_6)    ,				batch.attention_mask.shape       )
						SCREAMING_SNAKE_CASE__								=      batch.input_ids.tolist()[0]
						self.assertListEqual(self.expected_src_tokens    ,				_UpperCAmelCase       )
						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 					SCREAMING_SNAKE_CASE				(					self				:						List[Any]       )   ->    List[Any]:
						SCREAMING_SNAKE_CASE__								=      self.tokenizer(self.src_text    ,				padding=_UpperCAmelCase    ,				truncation=_UpperCAmelCase    ,				max_length=3    ,				return_tensors="""pt"""       )
						SCREAMING_SNAKE_CASE__								=      self.tokenizer(
						    text_target=self.tgt_text    ,				padding=_UpperCAmelCase    ,				truncation=_UpperCAmelCase    ,				max_length=1_0    ,				return_tensors="""pt"""       )
						SCREAMING_SNAKE_CASE__								=      targets["""input_ids"""]
						SCREAMING_SNAKE_CASE__								=      shift_tokens_right(_UpperCAmelCase    ,				self.tokenizer.pad_token_id       )

						self.assertEqual(batch.input_ids.shape[1]    ,				3       )
						self.assertEqual(batch.decoder_input_ids.shape[1]    ,				1_0       )





			@require_torch
			def 					SCREAMING_SNAKE_CASE				(					self				:						Tuple       )   ->    List[str]:
						SCREAMING_SNAKE_CASE__								=      self.tokenizer._build_translation_inputs(
						    """A test"""    ,				return_tensors="""pt"""    ,				src_lang="""en_XX"""    ,				tgt_lang="""java"""       )

						self.assertEqual(
						    nested_simplify(_UpperCAmelCase       )    ,				{
						        # A, test, EOS, en_XX
						        """input_ids""": [[1_5_0, 2_4_2, 2, 5_0_0_0_3]],
						        """attention_mask""": [[1, 1, 1, 1]],
						        # java
						        """forced_bos_token_id""": 5_0_0_0_1,
						    }    ,				)








"""simple docstring"""




# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


A_							:			Dict											= {
    "configuration_efficientnet": [
        "EFFICIENTNET_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "EfficientNetConfig",
        "EfficientNetOnnxConfig",
    ]
}

try:
						if not is_vision_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						A_							:			List[Any]											= ["EfficientNetImageProcessor"]

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						A_							:			Dict											= [
						    "EFFICIENTNET_PRETRAINED_MODEL_ARCHIVE_LIST",
						    "EfficientNetForImageClassification",
						    "EfficientNetModel",
						    "EfficientNetPreTrainedModel",
						]

if TYPE_CHECKING:
						from .configuration_efficientnet import (
						    EFFICIENTNET_PRETRAINED_CONFIG_ARCHIVE_MAP,
						    EfficientNetConfig,
						    EfficientNetOnnxConfig,
						)

						try:
												if not is_vision_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .image_processing_efficientnet import EfficientNetImageProcessor

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_efficientnet import (
												    EFFICIENTNET_PRETRAINED_MODEL_ARCHIVE_LIST,
												    EfficientNetForImageClassification,
												    EfficientNetModel,
												    EfficientNetPreTrainedModel,
												)

else:
						import sys

						A_							:			List[Any]											= _LazyModule(__name__, globals()["__file__"], _import_structure)


