


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


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


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

lowerCAmelCase__      		= 5_00_03
lowerCAmelCase__      		= 5_00_02







@require_sentencepiece
@require_tokenizers
class      lowerCAmelCase__       (				a     ,    unittest.TestCase):




     '''simple docstring'''
     __SCREAMING_SNAKE_CASE  			= PLBartTokenizer
     __SCREAMING_SNAKE_CASE  			= None
     __SCREAMING_SNAKE_CASE  			= False


     def  _lowerCamelCase (						self)			->				Optional[int]:
            super().setUp()

            # We have a SentencePiece fixture for testing
            _A      :       Union[str, Any]				    = PLBartTokenizer(__lowerCamelCase ,						language_codes="base" ,						keep_accents=__lowerCamelCase)
            tokenizer.save_pretrained(self.tmpdirname)


     def  _lowerCamelCase (						self)			->				List[Any]:
            _A      :       List[Any]				    = PLBartTokenizer(__lowerCamelCase ,						language_codes="base" ,						keep_accents=__lowerCamelCase)

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

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

            _A      :       Dict				    = tokenizer.tokenize("I was born in 92000, and this is falsé.")
            self.assertListEqual(
                __lowerCamelCase ,						[
                    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",
                    ".",
                ] ,						)
            _A      :       Optional[int]				    = tokenizer.convert_tokens_to_ids(__lowerCamelCase)
            self.assertListEqual(
                __lowerCamelCase ,						[
                    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]
                ] ,						)

            _A      :       Union[str, Any]				    = tokenizer.convert_ids_to_tokens(__lowerCamelCase)
            self.assertListEqual(
                __lowerCamelCase ,						[
                    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>",
                    ".",
                ] ,						)

            _A      :       Tuple				    = tokenizer.vocab_size
            _A      :       int				    = [tokenizer.convert_ids_to_tokens(__lowerCamelCase) for x in range(end - 4 ,						__lowerCamelCase)]

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

            _A      :       Tuple				    = "java.lang.Exception, python.lang.Exception, javascript, php, ruby, go"
            _A      :       Dict				    = tokenizer(__lowerCamelCase).input_ids
            self.assertEqual(
                tokenizer.decode(__lowerCamelCase ,						skip_special_tokens=__lowerCamelCase ,						clean_up_tokenization_spaces=__lowerCamelCase) ,						__lowerCamelCase ,						)


     def  _lowerCamelCase (						self)			->				Union[str, Any]:
            _A      :       str				    = PLBartTokenizer(__lowerCamelCase ,						language_codes="multi" ,						keep_accents=__lowerCamelCase)

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

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

            _A      :       List[str]				    = tokenizer.tokenize("I was born in 92000, and this is falsé.")
            self.assertListEqual(
                __lowerCamelCase ,						[
                    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",
                    ".",
                ] ,						)
            _A      :       List[Any]				    = tokenizer.convert_tokens_to_ids(__lowerCamelCase)
            self.assertListEqual(
                __lowerCamelCase ,						[
                    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]
                ] ,						)

            _A      :       str				    = tokenizer.convert_ids_to_tokens(__lowerCamelCase)
            self.assertListEqual(
                __lowerCamelCase ,						[
                    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>",
                    ".",
                ] ,						)
            _A      :       Dict				    = tokenizer.vocab_size
            _A      :       List[Any]				    = [tokenizer.convert_ids_to_tokens(__lowerCamelCase) for x in range(end - 7 ,						__lowerCamelCase)]

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







@require_torch
@require_sentencepiece
@require_tokenizers
class      lowerCAmelCase__       (				unittest.TestCase):




     '''simple docstring'''
     __SCREAMING_SNAKE_CASE  			= "uclanlp/plbart-python-en_XX"
     __SCREAMING_SNAKE_CASE  			= [
         "def maximum(a,b,c):NEW_LINE_INDENTreturn max([a,b,c])",
         "def sum(a,b,c):NEW_LINE_INDENTreturn sum([a,b,c])",
     ]
     __SCREAMING_SNAKE_CASE  			= [
         "Returns the maximum value of a b c.",
         "Sums the values of a b c.",
     ]
     __SCREAMING_SNAKE_CASE  			= [
         134,
         5452,
         3_3460,
         3_3441,
         3_3463,
         3_3465,
         3_3463,
         3_3449,
         988,
         20,
         3_3456,
         19,
         3_3456,
         771,
         39,
         4258,
         889,
         3318,
         3_3441,
         3_3463,
         3_3465,
         3_3463,
         3_3449,
         2471,
         2,
         PYTHON_CODE,
     ]


     @classmethod
     def  _lowerCamelCase (						cls)			->				str:
            _A      :       PLBartTokenizer				    = PLBartTokenizer.from_pretrained(
                cls.checkpoint_name ,						language_codes="base" ,						src_lang="python" ,						tgt_lang="en_XX")
            _A      :       str				    = 1
            return cls


     def  _lowerCamelCase (						self)			->				Union[str, 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  _lowerCamelCase (						self)			->				Any:
            _A      :       Optional[Any]				    = self.tokenizer.batch_encode_plus(self.src_text).input_ids[0]
            self.assertListEqual(self.expected_src_tokens ,						__lowerCamelCase)


     def  _lowerCamelCase (						self)			->				Optional[int]:
            self.assertIn(__lowerCamelCase ,						self.tokenizer.all_special_ids)
            _A      :       int				    = [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]
            _A      :       Dict				    = self.tokenizer.decode(__lowerCamelCase ,						skip_special_tokens=__lowerCamelCase)
            _A      :       Optional[Any]				    = self.tokenizer.decode(generated_ids[1:] ,						skip_special_tokens=__lowerCamelCase)
            self.assertEqual(__lowerCamelCase ,						__lowerCamelCase)
            self.assertNotIn(self.tokenizer.eos_token ,						__lowerCamelCase)


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


     def  _lowerCamelCase (						self)			->				Tuple:
            self.assertListEqual(self.tokenizer.convert_tokens_to_ids(["<mask>", "__java__"]) ,						[5_0_0_0_4, 5_0_0_0_1])


     def  _lowerCamelCase (						self)			->				str:
            _A      :       Any				    = tempfile.mkdtemp()
            _A      :       Dict				    = self.tokenizer.fairseq_tokens_to_ids
            self.tokenizer.save_pretrained(__lowerCamelCase)
            _A      :       int				    = PLBartTokenizer.from_pretrained(__lowerCamelCase)
            self.assertDictEqual(new_tok.fairseq_tokens_to_ids ,						__lowerCamelCase)


     @require_torch
     def  _lowerCamelCase (						self)			->				Optional[Any]:
            _A      :       Optional[int]				    = self.tokenizer(self.src_text ,						text_target=self.tgt_text ,						padding=__lowerCamelCase ,						return_tensors="pt")
            _A      :       Union[str, Any]				    = 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] ,						__lowerCamelCase)
            self.assertEqual(batch.decoder_input_ids[1][-1] ,						2)
            self.assertEqual(batch.labels[1][-2:].tolist() ,						[2, EN_CODE])


     @require_torch
     def  _lowerCamelCase (						self)			->				Optional[Any]:
            _A      :       List[Any]				    = self.tokenizer(
                self.src_text ,						text_target=self.tgt_text ,						padding=__lowerCamelCase ,						truncation=__lowerCamelCase ,						max_length=len(self.expected_src_tokens) ,						return_tensors="pt" ,						)
            _A      :       Tuple				    = shift_tokens_right(batch["labels"] ,						self.tokenizer.pad_token_id)

            self.assertIsInstance(__lowerCamelCase ,						__lowerCamelCase)

            self.assertEqual((2, 2_6) ,						batch.input_ids.shape)
            self.assertEqual((2, 2_6) ,						batch.attention_mask.shape)
            _A      :       Tuple				    = batch.input_ids.tolist()[0]
            self.assertListEqual(self.expected_src_tokens ,						__lowerCamelCase)
            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  _lowerCamelCase (						self)			->				List[Any]:
            _A      :       List[Any]				    = self.tokenizer(self.src_text ,						padding=__lowerCamelCase ,						truncation=__lowerCamelCase ,						max_length=3 ,						return_tensors="pt")
            _A      :       Dict				    = self.tokenizer(
                text_target=self.tgt_text ,						padding=__lowerCamelCase ,						truncation=__lowerCamelCase ,						max_length=1_0 ,						return_tensors="pt")
            _A      :       Any				    = targets["input_ids"]
            _A      :       Union[str, Any]				    = shift_tokens_right(__lowerCamelCase ,						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  _lowerCamelCase (						self)			->				Union[str, Any]:
            _A      :       str				    = self.tokenizer._build_translation_inputs(
                "A test" ,						return_tensors="pt" ,						src_lang="en_XX" ,						tgt_lang="java")

            self.assertEqual(
                nested_simplify(__lowerCamelCase) ,						{
                    # 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,
                } ,						)




import warnings
from typing import List

import numpy as np

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import is_flax_available, is_tf_available, is_torch_available







class      lowerCAmelCase__       (				a):




     '''simple docstring'''
     __SCREAMING_SNAKE_CASE  			= ["image_processor", "tokenizer"]
     __SCREAMING_SNAKE_CASE  			= "OwlViTImageProcessor"
     __SCREAMING_SNAKE_CASE  			= ("CLIPTokenizer", "CLIPTokenizerFast")


     def __init__(						self ,						__lowerCamelCase=None ,						__lowerCamelCase=None ,						**__lowerCamelCase)			->				Union[str, Any]:
            _A      :       int				    = None
            if "feature_extractor" in kwargs:
                   warnings.warn(
                       "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
                       " instead." ,						__lowerCamelCase ,						)
                   _A      :       List[Any]				    = kwargs.pop("feature_extractor")

            _A      :       Dict				    = 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__(__lowerCamelCase ,						__lowerCamelCase)


     def __call__(						self ,						__lowerCamelCase=None ,						__lowerCamelCase=None ,						__lowerCamelCase=None ,						__lowerCamelCase="max_length" ,						__lowerCamelCase="np" ,						**__lowerCamelCase)			->				Any:

            if text is None and query_images is None and images is None:
                   raise ValueError(
                       "You have to specify at least one text or query image or image. All three cannot be none.")

            if text is not None:
                   if isinstance(__lowerCamelCase ,						__lowerCamelCase) or (isinstance(__lowerCamelCase ,						__lowerCamelCase) and not isinstance(text[0] ,						__lowerCamelCase)):
                          _A      :       Union[str, Any]				    = [self.tokenizer(__lowerCamelCase ,						padding=__lowerCamelCase ,						return_tensors=__lowerCamelCase ,						**__lowerCamelCase)]

                   elif isinstance(__lowerCamelCase ,						__lowerCamelCase) and isinstance(text[0] ,						__lowerCamelCase):
                          _A      :       Optional[Any]				    = []

                          # Maximum number of queries across batch
                          _A      :       str				    = max([len(__lowerCamelCase) for t in text])

                          # Pad all batch samples to max number of text queries
                          for t in text:
                                 if len(__lowerCamelCase) != max_num_queries:
                                        _A      :       Optional[int]				    = t + [" "] * (max_num_queries - len(__lowerCamelCase))

                                 _A      :       List[Any]				    = self.tokenizer(__lowerCamelCase ,						padding=__lowerCamelCase ,						return_tensors=__lowerCamelCase ,						**__lowerCamelCase)
                                 encodings.append(__lowerCamelCase)
                   else:
                          raise TypeError("Input text should be a string, a list of strings or a nested list of strings")

                   if return_tensors == "np":
                          _A      :       Tuple				    = np.concatenate([encoding["input_ids"] for encoding in encodings] ,						axis=0)
                          _A      :       Optional[Any]				    = np.concatenate([encoding["attention_mask"] for encoding in encodings] ,						axis=0)

                   elif return_tensors == "jax" and is_flax_available():
                          import jax.numpy as jnp

                          _A      :       Optional[int]				    = jnp.concatenate([encoding["input_ids"] for encoding in encodings] ,						axis=0)
                          _A      :       Optional[int]				    = jnp.concatenate([encoding["attention_mask"] for encoding in encodings] ,						axis=0)

                   elif return_tensors == "pt" and is_torch_available():
                          import torch

                          _A      :       Optional[Any]				    = torch.cat([encoding["input_ids"] for encoding in encodings] ,						dim=0)
                          _A      :       Union[str, Any]				    = torch.cat([encoding["attention_mask"] for encoding in encodings] ,						dim=0)

                   elif return_tensors == "tf" and is_tf_available():
                          import tensorflow as tf

                          _A      :       Any				    = tf.stack([encoding["input_ids"] for encoding in encodings] ,						axis=0)
                          _A      :       Tuple				    = tf.stack([encoding["attention_mask"] for encoding in encodings] ,						axis=0)

                   else:
                          raise ValueError("Target return tensor type could not be returned")

                   _A      :       Optional[Any]				    = BatchEncoding()
                   _A      :       Tuple				    = input_ids
                   _A      :       Dict				    = attention_mask

            if query_images is not None:
                   _A      :       Optional[Any]				    = BatchEncoding()
                   _A      :       List[str]				    = self.image_processor(
                       __lowerCamelCase ,						return_tensors=__lowerCamelCase ,						**__lowerCamelCase).pixel_values
                   _A      :       Union[str, Any]				    = query_pixel_values

            if images is not None:
                   _A      :       int				    = self.image_processor(__lowerCamelCase ,						return_tensors=__lowerCamelCase ,						**__lowerCamelCase)

            if text is not None and images is not None:
                   _A      :       Tuple				    = image_features.pixel_values
                   return encoding
            elif query_images is not None and images is not None:
                   _A      :       int				    = image_features.pixel_values
                   return encoding
            elif text is not None or query_images is not None:
                   return encoding
            else:
                   return BatchEncoding(data=dict(**__lowerCamelCase) ,						tensor_type=__lowerCamelCase)


     def  _lowerCamelCase (						self ,						*__lowerCamelCase ,						**__lowerCamelCase)			->				str:
            return self.image_processor.post_process(*__lowerCamelCase ,						**__lowerCamelCase)


     def  _lowerCamelCase (						self ,						*__lowerCamelCase ,						**__lowerCamelCase)			->				List[str]:
            return self.image_processor.post_process_object_detection(*__lowerCamelCase ,						**__lowerCamelCase)


     def  _lowerCamelCase (						self ,						*__lowerCamelCase ,						**__lowerCamelCase)			->				Optional[int]:
            return self.image_processor.post_process_image_guided_detection(*__lowerCamelCase ,						**__lowerCamelCase)


     def  _lowerCamelCase (						self ,						*__lowerCamelCase ,						**__lowerCamelCase)			->				int:
            return self.tokenizer.batch_decode(*__lowerCamelCase ,						**__lowerCamelCase)


     def  _lowerCamelCase (						self ,						*__lowerCamelCase ,						**__lowerCamelCase)			->				Optional[int]:
            return self.tokenizer.decode(*__lowerCamelCase ,						**__lowerCamelCase)


     @property
     def  _lowerCamelCase (						self)			->				int:
            warnings.warn(
                "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead." ,						__lowerCamelCase ,						)
            return self.image_processor_class


     @property
     def  _lowerCamelCase (						self)			->				List[str]:
            warnings.warn(
                "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead." ,						__lowerCamelCase ,						)
            return self.image_processor
