


import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_torch_available, is_vision_available


if is_torch_available():
      import torch


if is_vision_available():
      from PIL import Image

      from transformers import MgpstrProcessor, ViTImageProcessor





@require_torch
@require_vision
class 					A							(    unittest.TestCase					):
   __UpperCAmelCase			:			List[str]              =      ViTImageProcessor if is_vision_available() else None


   @property
   def       lowercase_				(self					:						Dict		)						->    Optional[Any]:
         """simple docstring"""



         return self.image_processor_tester.prepare_image_processor_dict()


   def       lowercase_				(self					:						Union[str, Any]		)						->    Dict:
         """simple docstring"""



         UpperCAmelCase__								=  (3, 3_2, 1_2_8)
         UpperCAmelCase__								=  tempfile.mkdtemp()

         # fmt: off
         UpperCAmelCase__								=  ["""[GO]""", """[s]""", """0""", """1""", """2""", """3""", """4""", """5""", """6""", """7""", """8""", """9""", """a""", """b""", """c""", """d""", """e""", """f""", """g""", """h""", """i""", """j""", """k""", """l""", """m""", """n""", """o""", """p""", """q""", """r""", """s""", """t""", """u""", """v""", """w""", """x""", """y""", """z"""]
         # fmt: on
         UpperCAmelCase__								=  dict(zip(__UpperCAmelCase	,  range(len(__UpperCAmelCase		)		)		)		)

         UpperCAmelCase__								=  os.path.join(self.tmpdirname	,  VOCAB_FILES_NAMES["vocab_file"]		)
         with open(self.vocab_file	,  "w"	,  encoding="utf-8"		) as fp:
               fp.write(json.dumps(__UpperCAmelCase		) + "\n"		)

         UpperCAmelCase__								=  {
             """do_normalize""": False,
             """do_resize""": True,
             """image_processor_type""": """ViTImageProcessor""",
             """resample""": 3,
             """size""": {"""height""": 3_2, """width""": 1_2_8},
         }
         UpperCAmelCase__								=  os.path.join(self.tmpdirname	,  __UpperCAmelCase		)
         with open(self.image_processor_file	,  "w"	,  encoding="utf-8"		) as fp:
               json.dump(__UpperCAmelCase	,  __UpperCAmelCase		)


   def       lowercase_				(self					:						int	,  **__UpperCAmelCase					:						str		)						->    Tuple:
         """simple docstring"""



         return MgpstrTokenizer.from_pretrained(self.tmpdirname	,  **__UpperCAmelCase		)


   def       lowercase_				(self					:						int	,  **__UpperCAmelCase					:						Tuple		)						->    List[str]:
         """simple docstring"""



         return ViTImageProcessor.from_pretrained(self.tmpdirname	,  **__UpperCAmelCase		)


   def       lowercase_				(self					:						List[str]		)						->    str:
         """simple docstring"""



         shutil.rmtree(self.tmpdirname		)


   def       lowercase_				(self					:						Tuple		)						->    Tuple:
         """simple docstring"""



         UpperCAmelCase__								=  np.random.randint(2_5_5	,  size=(3, 3_0, 4_0_0)	,  dtype=np.uinta		)

         UpperCAmelCase__								=  Image.fromarray(np.moveaxis(__UpperCAmelCase	,  0	,  -1		)		)

         return image_input


   def       lowercase_				(self					:						str		)						->    str:
         """simple docstring"""



         UpperCAmelCase__								=  self.get_tokenizer()
         UpperCAmelCase__								=  self.get_image_processor()

         UpperCAmelCase__								=  MgpstrProcessor(tokenizer=__UpperCAmelCase	,  image_processor=__UpperCAmelCase		)
         processor.save_pretrained(self.tmpdirname		)
         UpperCAmelCase__								=  MgpstrProcessor.from_pretrained(self.tmpdirname	,  use_fast=__UpperCAmelCase		)

         self.assertEqual(processor.char_tokenizer.get_vocab()	,  tokenizer.get_vocab()		)
         self.assertIsInstance(processor.char_tokenizer	,  __UpperCAmelCase		)

         self.assertEqual(processor.image_processor.to_json_string()	,  image_processor.to_json_string()		)
         self.assertIsInstance(processor.image_processor	,  __UpperCAmelCase		)


   def       lowercase_				(self					:						int		)						->    Optional[int]:
         """simple docstring"""



         UpperCAmelCase__								=  self.get_tokenizer()
         UpperCAmelCase__								=  self.get_image_processor()

         UpperCAmelCase__								=  MgpstrProcessor(tokenizer=__UpperCAmelCase	,  image_processor=__UpperCAmelCase		)
         processor.save_pretrained(self.tmpdirname		)

         UpperCAmelCase__								=  self.get_tokenizer(bos_token="(BOS)"	,  eos_token="(EOS)"		)
         UpperCAmelCase__								=  self.get_image_processor(do_normalize=__UpperCAmelCase	,  padding_value=1.0		)

         UpperCAmelCase__								=  MgpstrProcessor.from_pretrained(
             self.tmpdirname	,  bos_token="(BOS)"	,  eos_token="(EOS)"	,  do_normalize=__UpperCAmelCase	,  padding_value=1.0		)

         self.assertEqual(processor.char_tokenizer.get_vocab()	,  tokenizer_add_kwargs.get_vocab()		)
         self.assertIsInstance(processor.char_tokenizer	,  __UpperCAmelCase		)

         self.assertEqual(processor.image_processor.to_json_string()	,  image_processor_add_kwargs.to_json_string()		)
         self.assertIsInstance(processor.image_processor	,  __UpperCAmelCase		)


   def       lowercase_				(self					:						Any		)						->    List[Any]:
         """simple docstring"""



         UpperCAmelCase__								=  self.get_image_processor()
         UpperCAmelCase__								=  self.get_tokenizer()

         UpperCAmelCase__								=  MgpstrProcessor(tokenizer=__UpperCAmelCase	,  image_processor=__UpperCAmelCase		)

         UpperCAmelCase__								=  self.prepare_image_inputs()

         UpperCAmelCase__								=  image_processor(__UpperCAmelCase	,  return_tensors="np"		)
         UpperCAmelCase__								=  processor(images=__UpperCAmelCase	,  return_tensors="np"		)

         for key in input_image_proc.keys():
               self.assertAlmostEqual(input_image_proc[key].sum()	,  input_processor[key].sum()	,  delta=1E-2		)


   def       lowercase_				(self					:						Optional[int]		)						->    Tuple:
         """simple docstring"""



         UpperCAmelCase__								=  self.get_image_processor()
         UpperCAmelCase__								=  self.get_tokenizer()

         UpperCAmelCase__								=  MgpstrProcessor(tokenizer=__UpperCAmelCase	,  image_processor=__UpperCAmelCase		)

         UpperCAmelCase__								=  """test"""

         UpperCAmelCase__								=  processor(text=__UpperCAmelCase		)

         UpperCAmelCase__								=  tokenizer(__UpperCAmelCase		)
         for key in encoded_tok.keys():
               self.assertListEqual(encoded_tok[key]	,  encoded_processor[key]		)


   def       lowercase_				(self					:						Dict		)						->    Union[str, Any]:
         """simple docstring"""



         UpperCAmelCase__								=  self.get_image_processor()
         UpperCAmelCase__								=  self.get_tokenizer()

         UpperCAmelCase__								=  MgpstrProcessor(tokenizer=__UpperCAmelCase	,  image_processor=__UpperCAmelCase		)

         UpperCAmelCase__								=  """test"""
         UpperCAmelCase__								=  self.prepare_image_inputs()

         UpperCAmelCase__								=  processor(text=__UpperCAmelCase	,  images=__UpperCAmelCase		)

         self.assertListEqual(list(inputs.keys()		)	,  ["pixel_values", "labels"]		)

         # test if it raises when no input is passed
         with pytest.raises(__UpperCAmelCase		):
               processor()


   def       lowercase_				(self					:						Optional[int]		)						->    Union[str, Any]:
         """simple docstring"""



         UpperCAmelCase__								=  self.get_image_processor()
         UpperCAmelCase__								=  self.get_tokenizer()

         UpperCAmelCase__								=  MgpstrProcessor(tokenizer=__UpperCAmelCase	,  image_processor=__UpperCAmelCase		)

         UpperCAmelCase__								=  [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9], [3, 4, 3, 1, 1, 8, 9]]

         UpperCAmelCase__								=  processor.char_decode(__UpperCAmelCase		)
         UpperCAmelCase__								=  tokenizer.batch_decode(__UpperCAmelCase		)
         UpperCAmelCase__								=  [seq.replace(" "	,  ""		) for seq in decoded_tok]

         self.assertListEqual(__UpperCAmelCase	,  __UpperCAmelCase		)


   def       lowercase_				(self					:						List[Any]		)						->    Optional[int]:
         """simple docstring"""



         UpperCAmelCase__								=  self.get_image_processor()
         UpperCAmelCase__								=  self.get_tokenizer()

         UpperCAmelCase__								=  MgpstrProcessor(tokenizer=__UpperCAmelCase	,  image_processor=__UpperCAmelCase		)

         UpperCAmelCase__								=  None
         UpperCAmelCase__								=  self.prepare_image_inputs()

         UpperCAmelCase__								=  processor(text=__UpperCAmelCase	,  images=__UpperCAmelCase		)

         self.assertListEqual(list(inputs.keys()		)	,  processor.model_input_names		)





   def       lowercase_				(self					:						Dict		)						->    int:
         """simple docstring"""



         UpperCAmelCase__								=  self.get_image_processor()
         UpperCAmelCase__								=  self.get_tokenizer()

         UpperCAmelCase__								=  MgpstrProcessor(tokenizer=__UpperCAmelCase	,  image_processor=__UpperCAmelCase		)

         UpperCAmelCase__								=  torch.randn(1	,  2_7	,  3_8		)
         UpperCAmelCase__								=  torch.randn(1	,  2_7	,  5_0_2_5_7		)
         UpperCAmelCase__								=  torch.randn(1	,  2_7	,  3_0_5_2_2		)

         UpperCAmelCase__								=  processor.batch_decode([char_input, bpe_input, wp_input]		)

         self.assertListEqual(list(results.keys()		)	,  ["generated_text", "scores", "char_preds", "bpe_preds", "wp_preds"]		)




import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
      from .tokenization_pegasus import PegasusTokenizer
else:
      UpperCamelCase__    							= None


UpperCamelCase__    							= logging.get_logger(__name__)


UpperCamelCase__    							= '▁'

UpperCamelCase__    							= {'vocab_file': 'spiece.model', 'tokenizer_file': 'tokenizer.json'}

UpperCamelCase__    							= {
    'vocab_file': {'google/pegasus-xsum': 'https://huggingface.co/google/pegasus-xsum/resolve/main/spiece.model'},
    'tokenizer_file': {
        'google/pegasus-xsum': 'https://huggingface.co/google/pegasus-xsum/resolve/main/tokenizer.json'
    },
}

UpperCamelCase__    							= {
    'google/pegasus-xsum': 5_1_2,
}





class 					A							(    UpperCAmelCase_					):
   __UpperCAmelCase			:			str              =      VOCAB_FILES_NAMES
   __UpperCAmelCase			:			Any              =      PRETRAINED_VOCAB_FILES_MAP
   __UpperCAmelCase			:			List[Any]              =      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   __UpperCAmelCase			:			Union[str, Any]              =      PegasusTokenizer
   __UpperCAmelCase			:			Any              =      ['input_ids', 'attention_mask']


   def __init__(self					:						Optional[int]	,  __UpperCAmelCase					:						List[str]=None	,  __UpperCAmelCase					:						Any=None	,  __UpperCAmelCase					:						Union[str, Any]="<pad>"	,  __UpperCAmelCase					:						List[str]="</s>"	,  __UpperCAmelCase					:						Union[str, Any]="<unk>"	,  __UpperCAmelCase					:						int="<mask_2>"	,  __UpperCAmelCase					:						Optional[Any]="<mask_1>"	,  __UpperCAmelCase					:						Union[str, Any]=None	,  __UpperCAmelCase					:						str=1_0_3	,  **__UpperCAmelCase					:						str	,  )						->    Optional[Any]:
         """simple docstring"""



         UpperCAmelCase__								=  offset

         if additional_special_tokens is not None:
               if not isinstance(__UpperCAmelCase	,  __UpperCAmelCase		):
                     raise TypeError(
                         f"""additional_special_tokens should be of type {type(__UpperCAmelCase		)}, but is"""
                         f""" {type(__UpperCAmelCase		)}"""		)

               UpperCAmelCase__								=  (
                   ([mask_token_sent] + additional_special_tokens)
                   if mask_token_sent not in additional_special_tokens and mask_token_sent is not None
                   else additional_special_tokens
               )
               # fill additional tokens with ..., <unk_token_102> in case not all additional tokens are already taken
               additional_special_tokens_extended += [
                   f"""<unk_{i}>""" for i in range(len(__UpperCAmelCase		)	,  self.offset - 1		)
               ]

               if len(set(__UpperCAmelCase		)		) != len(__UpperCAmelCase		):
                     raise ValueError(
                         "Please make sure that the provided additional_special_tokens do not contain an incorrectly"
                         f""" shifted list of <unk_x> tokens. Found {additional_special_tokens_extended}."""		)
               UpperCAmelCase__								=  additional_special_tokens_extended
         else:
               UpperCAmelCase__								=  [mask_token_sent] if mask_token_sent is not None else []
               additional_special_tokens += [f"""<unk_{i}>""" for i in range(2	,  self.offset		)]

         super().__init__(
             __UpperCAmelCase	,  tokenizer_file=__UpperCAmelCase	,  pad_token=__UpperCAmelCase	,  eos_token=__UpperCAmelCase	,  unk_token=__UpperCAmelCase	,  mask_token=__UpperCAmelCase	,  mask_token_sent=__UpperCAmelCase	,  offset=__UpperCAmelCase	,  additional_special_tokens=__UpperCAmelCase	,  **__UpperCAmelCase	,  )
         UpperCAmelCase__								=  vocab_file
         UpperCAmelCase__								=  False if not self.vocab_file else True


   def       lowercase_				(self					:						List[Any]	,  __UpperCAmelCase					:						Tuple		)						->    int:
         """simple docstring"""



         UpperCAmelCase__								=  set(self.all_special_ids		)  # call it once instead of inside list comp
         all_special_ids.remove(self.unk_token_id		)  # <unk> is only sometimes special

         if all_special_ids != set(range(len(self.additional_special_tokens		) + 3		)		):
               raise ValueError(
                   "There should be 3 special tokens: mask_token, pad_token, and eos_token +"
                   f""" {len(self.additional_special_tokens		)} additional_special_tokens, but got {all_special_ids}"""		)

         return [1 if x in all_special_ids else 0 for x in seq]


   def       lowercase_				(self					:						Union[str, Any]	,  __UpperCAmelCase					:						List	,  __UpperCAmelCase					:						Optional[List] = None	,  __UpperCAmelCase					:						bool = False		)						->    List[int]:
         """simple docstring"""



         if already_has_special_tokens:
               return self._special_token_mask(__UpperCAmelCase		)
         elif token_ids_a is None:
               return self._special_token_mask(__UpperCAmelCase		) + [1]
         else:
               return self._special_token_mask(token_ids_a + token_ids_a		) + [1]


   def       lowercase_				(self					:						str	,  __UpperCAmelCase					:						List[str]	,  __UpperCAmelCase					:						Optional[Any]=None		)						->    List[int]:
         """simple docstring"""



         if token_ids_a is None:
               return token_ids_a + [self.eos_token_id]
         # We don't expect to process pairs, but leave the pair logic for API consistency
         return token_ids_a + token_ids_a + [self.eos_token_id]





   def       lowercase_				(self					:						List[str]	,  __UpperCAmelCase					:						str	,  __UpperCAmelCase					:						Optional[str] = None		)						->    Tuple[str]:
         """simple docstring"""



         if not self.can_save_slow_tokenizer:
               raise ValueError(
                   "Your fast tokenizer does not have the necessary information to save the vocabulary for a slow "
                   "tokenizer."		)

         if not os.path.isdir(__UpperCAmelCase		):
               logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""		)
               return
         UpperCAmelCase__								=  os.path.join(
             __UpperCAmelCase	,  (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]		)

         if os.path.abspath(self.vocab_file		) != os.path.abspath(__UpperCAmelCase		):
               copyfile(self.vocab_file	,  __UpperCAmelCase		)

         return (out_vocab_file,)



