
"""simple docstring"""

import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available


if is_vision_available():
      from PIL import Image

      from transformers import AutoProcessor, BertTokenizer, BlipImageProcessor, BlipProcessor, PreTrainedTokenizerFast

@require_vision
class __lowerCAmelCase		(				unittest.TestCase      ):





     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      __UpperCamelCase      =	tempfile.mkdtemp()

      __UpperCamelCase      =	BlipImageProcessor()
      __UpperCamelCase      =	BertTokenizer.from_pretrained('hf-internal-testing/tiny-random-BertModel'				)

      __UpperCamelCase      =	BlipProcessor(__UpperCAmelCase     ,					__UpperCAmelCase				)

      processor.save_pretrained(self.tmpdirname				)





     def 						UpperCAmelCase (     self     ,					**__UpperCAmelCase				):

      '''simple docstring'''




      return AutoProcessor.from_pretrained(self.tmpdirname     ,					**__UpperCAmelCase				).tokenizer





     def 						UpperCAmelCase (     self     ,					**__UpperCAmelCase				):

      '''simple docstring'''




      return AutoProcessor.from_pretrained(self.tmpdirname     ,					**__UpperCAmelCase				).image_processor





     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      shutil.rmtree(self.tmpdirname				)





     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      __UpperCamelCase      =	[np.random.randint(255     ,					size=(3, 30, 400)     ,					dtype=np.uinta				)]

      __UpperCamelCase      =	[Image.fromarray(np.moveaxis(__UpperCAmelCase     ,					0     ,					-1				)				) for x in image_inputs]

      return image_inputs





     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      __UpperCamelCase      =	BlipProcessor(tokenizer=self.get_tokenizer()     ,					image_processor=self.get_image_processor()				)
      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      =	BlipProcessor.from_pretrained(
          self.tmpdirname     ,					bos_token='(BOS)'     ,					eos_token='(EOS)'     ,					do_normalize=__UpperCAmelCase     ,					padding_value=1.0				)

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

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





     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      __UpperCamelCase      =	self.get_image_processor()
      __UpperCamelCase      =	self.get_tokenizer()

      __UpperCamelCase      =	BlipProcessor(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_feat_extract.keys():
       self.assertAlmostEqual(input_feat_extract[key].sum()     ,					input_processor[key].sum()     ,					delta=1E-2				)





     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      __UpperCamelCase      =	self.get_image_processor()
      __UpperCamelCase      =	self.get_tokenizer()

      __UpperCamelCase      =	BlipProcessor(tokenizer=__UpperCAmelCase     ,					image_processor=__UpperCAmelCase				)

      __UpperCamelCase      =	'lower newer'

      __UpperCamelCase      =	processor(text=__UpperCAmelCase				)

      __UpperCamelCase      =	tokenizer(__UpperCAmelCase     ,					return_token_type_ids=__UpperCAmelCase				)

      for key in encoded_tok.keys():
       self.assertListEqual(encoded_tok[key]     ,					encoded_processor[key]				)





     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      __UpperCamelCase      =	self.get_image_processor()
      __UpperCamelCase      =	self.get_tokenizer()

      __UpperCamelCase      =	BlipProcessor(tokenizer=__UpperCAmelCase     ,					image_processor=__UpperCAmelCase				)

      __UpperCamelCase      =	'lower newer'
      __UpperCamelCase      =	self.prepare_image_inputs()

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

      self.assertListEqual(list(inputs.keys()				)     ,					['pixel_values', 'input_ids', 'attention_mask']				)

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





     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      __UpperCamelCase      =	self.get_image_processor()
      __UpperCamelCase      =	self.get_tokenizer()

      __UpperCamelCase      =	BlipProcessor(tokenizer=__UpperCAmelCase     ,					image_processor=__UpperCAmelCase				)

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

      __UpperCamelCase      =	processor.batch_decode(__UpperCAmelCase				)
      __UpperCamelCase      =	tokenizer.batch_decode(__UpperCAmelCase				)

      self.assertListEqual(__UpperCAmelCase     ,					__UpperCAmelCase				)







     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      __UpperCamelCase      =	self.get_image_processor()
      __UpperCamelCase      =	self.get_tokenizer()

      __UpperCamelCase      =	BlipProcessor(tokenizer=__UpperCAmelCase     ,					image_processor=__UpperCAmelCase				)

      __UpperCamelCase      =	'lower newer'
      __UpperCamelCase      =	self.prepare_image_inputs()

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

      # For now the processor supports only ['pixel_values', 'input_ids', 'attention_mask']
      self.assertListEqual(list(inputs.keys()				)     ,					['pixel_values', 'input_ids', 'attention_mask']				)





"""simple docstring"""

import json
import re
from typing import TYPE_CHECKING, List, Optional, Tuple, Union

import numpy as np

from ...utils import is_tf_available, is_torch_available, logging


if TYPE_CHECKING:
      if is_torch_available():
            import torch
      if is_tf_available():
            import tensorflow as tf

from tokenizers import pre_tokenizers

from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from .tokenization_codegen import CodeGenTokenizer


UpperCamelCase     :    List[Any]       =			logging.get_logger(__name__)

UpperCamelCase     :    List[str]       =			{"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}

UpperCamelCase     :    List[Any]       =			{
    "vocab_file": {
        "Salesforce/codegen-350M-mono": "https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/vocab.json",
    },
    "merges_file": {
        "Salesforce/codegen-350M-mono": "https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/merges.txt",
    },
    "tokenizer_file": {
        "Salesforce/codegen-350M-mono": (
            "https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/tokenizer.json"
        ),
    },
}

UpperCamelCase     :    List[Any]       =			{
    "Salesforce/codegen-350M-mono": 2_0_4_8,
}

class __lowerCAmelCase		(				__SCREAMING_SNAKE_CASE      ):
     lowercase						       =						VOCAB_FILES_NAMES
     lowercase						       =						PRETRAINED_VOCAB_FILES_MAP
     lowercase						       =						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     lowercase						       =						["input_ids", "attention_mask"]
     lowercase						       =						CodeGenTokenizer





     def __init__(     self     ,					__UpperCAmelCase=None     ,					__UpperCAmelCase=None     ,					__UpperCAmelCase=None     ,					__UpperCAmelCase="<|endoftext|>"     ,					__UpperCAmelCase="<|endoftext|>"     ,					__UpperCAmelCase="<|endoftext|>"     ,					__UpperCAmelCase=False     ,					**__UpperCAmelCase     ,					):

      '''simple docstring'''




      super().__init__(
          __UpperCAmelCase     ,					__UpperCAmelCase     ,					tokenizer_file=__UpperCAmelCase     ,					unk_token=__UpperCAmelCase     ,					bos_token=__UpperCAmelCase     ,					eos_token=__UpperCAmelCase     ,					add_prefix_space=__UpperCAmelCase     ,					**__UpperCAmelCase     ,					)

      if kwargs.pop('add_bos_token'     ,					__UpperCAmelCase				):
       __UpperCamelCase      =	kwargs.pop('name_or_path'     ,					''				)
       raise ValueError(
           'Currenty GPT2\'s fast tokenizer does NOT support adding a BOS token.'
           'Instead you should use GPT2\'s slow tokenizer class `CodeGenTokenizer` as follows: \n'
           F'`CodeGenTokenizer.from_pretrained(\'{model_id}\')`\nor\n'
           F'`AutoTokenizer.from_pretrained(\'{model_id}\', use_fast=False)`\n'
           'This issue will be fixed soon, see: https://github.com/huggingface/tokenizers/pull/1005.'
           ' so that the fast tokenizer works correctly.'				)

      __UpperCamelCase      =	json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()				)
      if pre_tok_state.get('add_prefix_space'     ,					__UpperCAmelCase				) != add_prefix_space:
       __UpperCamelCase      =	getattr(__UpperCAmelCase     ,					pre_tok_state.pop('type'				)				)
       __UpperCamelCase      =	add_prefix_space
       __UpperCamelCase      =	pre_tok_class(**__UpperCAmelCase				)

      __UpperCamelCase      =	add_prefix_space





     def 						UpperCAmelCase (     self     ,					*__UpperCAmelCase     ,					**__UpperCAmelCase				):

      '''simple docstring'''




      __UpperCamelCase      =	kwargs.get('is_split_into_words'     ,					__UpperCAmelCase				)
      assert self.add_prefix_space or not is_split_into_words, (
          F'You need to instantiate {self.__class__.__name__} with add_prefix_space=True '
          "to use it with pretokenized inputs."
      )

      return super()._batch_encode_plus(*__UpperCAmelCase     ,					**__UpperCAmelCase				)





     def 						UpperCAmelCase (     self     ,					*__UpperCAmelCase     ,					**__UpperCAmelCase				):

      '''simple docstring'''




      __UpperCamelCase      =	kwargs.get('is_split_into_words'     ,					__UpperCAmelCase				)

      assert self.add_prefix_space or not is_split_into_words, (
          F'You need to instantiate {self.__class__.__name__} with add_prefix_space=True '
          "to use it with pretokenized inputs."
      )

      return super()._encode_plus(*__UpperCAmelCase     ,					**__UpperCAmelCase				)





     def 						UpperCAmelCase (     self     ,					__UpperCAmelCase     ,					__UpperCAmelCase = None				):

      '''simple docstring'''




      __UpperCamelCase      =	self._tokenizer.model.save(__UpperCAmelCase     ,					name=__UpperCAmelCase				)
      return tuple(__UpperCAmelCase				)





     def 						UpperCAmelCase (     self     ,					__UpperCAmelCase     ,					__UpperCAmelCase = False     ,					__UpperCAmelCase = None     ,					__UpperCAmelCase = None     ,					**__UpperCAmelCase     ,					):

      '''simple docstring'''




      __UpperCamelCase      =	super().decode(
          token_ids=__UpperCAmelCase     ,					skip_special_tokens=__UpperCAmelCase     ,					clean_up_tokenization_spaces=__UpperCAmelCase     ,					**__UpperCAmelCase     ,					)

      if truncate_before_pattern is not None and len(__UpperCAmelCase				) > 0:
       __UpperCamelCase      =	self.truncate(__UpperCAmelCase     ,					__UpperCAmelCase				)

      return decoded_text







     def 						UpperCAmelCase (     self     ,					__UpperCAmelCase     ,					__UpperCAmelCase				):

      '''simple docstring'''




      def find_re(__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase				):
       __UpperCamelCase      =	pattern.search(__UpperCAmelCase     ,					__UpperCAmelCase				)
       return m.start() if m else -1

      __UpperCamelCase      =	[re.compile(__UpperCAmelCase     ,					re.MULTILINE				) for pattern in truncate_before_pattern]

      __UpperCamelCase      =	list(re.finditer('^print'     ,					__UpperCAmelCase     ,					re.MULTILINE				)				)

      if len(__UpperCAmelCase				) > 1:
       __UpperCamelCase      =	completion[: prints[1].start()]

      __UpperCamelCase      =	list(re.finditer('^def'     ,					__UpperCAmelCase     ,					re.MULTILINE				)				)

      if len(__UpperCAmelCase				) > 1:
       __UpperCamelCase      =	completion[: defs[1].start()]

      __UpperCamelCase      =	0

      __UpperCamelCase      =	[
          pos for pos in [find_re(__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase				) for terminal in terminals] if pos != -1
      ]

      if len(__UpperCAmelCase				) > 0:
       return completion[: min(__UpperCAmelCase				)]
      else:
       return completion



