import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available


if is_torch_available():
						from transformers.pytorch_utils import is_torch_greater_or_equal_than_1_11
else:
						lowerCAmelCase__  	=      False

if is_vision_available():
						from PIL import Image

						from transformers import (
						    AutoProcessor,
						    PixaStructImageProcessor,
						    PixaStructProcessor,
						    PreTrainedTokenizerFast,
						    TaTokenizer,
						)
@unittest.skipIf(
    not is_torch_greater_or_equal_than_1_11							,		reason='`Pix2StructImageProcessor` requires `torch>=1.11.0`.'							,		)
@require_vision
@require_torch
class 	_a  (							unittest.TestCase					):



	"""simple docstring"""



	def __lowerCAmelCase      (   self       ):
		_lowercase   =tempfile.mkdtemp()

		_lowercase   =PixaStructImageProcessor()
		_lowercase   =TaTokenizer.from_pretrained("t5-small"       )

		_lowercase   =PixaStructProcessor(lowerCAmelCase_       ,  lowerCAmelCase_       )

		processor.save_pretrained(self.tmpdirname       )


	def __lowerCAmelCase      (   self       ,  **lowerCAmelCase_       ):
		return AutoProcessor.from_pretrained(self.tmpdirname       ,  **lowerCAmelCase_       ).tokenizer


	def __lowerCAmelCase      (   self       ,  **lowerCAmelCase_       ):
		return AutoProcessor.from_pretrained(self.tmpdirname       ,  **lowerCAmelCase_       ).image_processor


	def __lowerCAmelCase      (   self       ):
		shutil.rmtree(self.tmpdirname       )


	def __lowerCAmelCase      (   self       ):
		_lowercase   =[np.random.randint(255       ,  size=(3, 30, 400)       ,  dtype=np.uinta       )]

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

		return image_inputs


	def __lowerCAmelCase      (   self       ):
		_lowercase   =PixaStructProcessor(tokenizer=self.get_tokenizer()       ,  image_processor=self.get_image_processor()       )
		processor.save_pretrained(self.tmpdirname       )

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

		_lowercase   =PixaStructProcessor.from_pretrained(
		    self.tmpdirname       ,  bos_token="(BOS)"       ,  eos_token="(EOS)"       ,  do_normalize=lowerCAmelCase_       ,  padding_value=1.0       )

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

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


	def __lowerCAmelCase      (   self       ):
		_lowercase   =self.get_image_processor()
		_lowercase   =self.get_tokenizer()

		_lowercase   =PixaStructProcessor(tokenizer=lowerCAmelCase_       ,  image_processor=lowerCAmelCase_       )

		_lowercase   =self.prepare_image_inputs()

		_lowercase   =image_processor(lowerCAmelCase_       ,  return_tensors="np"       )
		_lowercase   =processor(images=lowerCAmelCase_       ,  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 __lowerCAmelCase      (   self       ):
		_lowercase   =self.get_image_processor()
		_lowercase   =self.get_tokenizer()

		_lowercase   =PixaStructProcessor(tokenizer=lowerCAmelCase_       ,  image_processor=lowerCAmelCase_       )

		_lowercase   ="lower newer"

		_lowercase   =processor(text=lowerCAmelCase_       )

		_lowercase   =tokenizer(lowerCAmelCase_       ,  return_token_type_ids=lowerCAmelCase_       ,  add_special_tokens=lowerCAmelCase_       )

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


	def __lowerCAmelCase      (   self       ):
		_lowercase   =self.get_image_processor()
		_lowercase   =self.get_tokenizer()

		_lowercase   =PixaStructProcessor(tokenizer=lowerCAmelCase_       ,  image_processor=lowerCAmelCase_       )

		_lowercase   ="lower newer"
		_lowercase   =self.prepare_image_inputs()

		_lowercase   =processor(text=lowerCAmelCase_       ,  images=lowerCAmelCase_       )

		self.assertListEqual(
		    list(inputs.keys()       )       ,  ["flattened_patches", "attention_mask", "decoder_attention_mask", "decoder_input_ids"]       )

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


	def __lowerCAmelCase      (   self       ):
		_lowercase   =self.get_image_processor()
		_lowercase   =self.get_tokenizer()

		_lowercase   =PixaStructProcessor(tokenizer=lowerCAmelCase_       ,  image_processor=lowerCAmelCase_       )

		_lowercase   ="lower newer"
		_lowercase   =self.prepare_image_inputs()

		_lowercase   =processor(text=lowerCAmelCase_       ,  images=lowerCAmelCase_       )

		_lowercase   =[512, 1024, 2048, 4096]
		_lowercase   =[770, 770, 770, 770]
		# with text
		for i, max_patch in enumerate(lowerCAmelCase_       ):
			_lowercase   =processor(text=lowerCAmelCase_       ,  images=lowerCAmelCase_       ,  max_patches=lowerCAmelCase_       )
			self.assertEqual(inputs["flattened_patches"][0].shape[0]       ,  lowerCAmelCase_       )
			self.assertEqual(inputs["flattened_patches"][0].shape[1]       ,  expected_hidden_size[i]       )

		# without text input
		for i, max_patch in enumerate(lowerCAmelCase_       ):
			_lowercase   =processor(images=lowerCAmelCase_       ,  max_patches=lowerCAmelCase_       )
			self.assertEqual(inputs["flattened_patches"][0].shape[0]       ,  lowerCAmelCase_       )
			self.assertEqual(inputs["flattened_patches"][0].shape[1]       ,  expected_hidden_size[i]       )


	def __lowerCAmelCase      (   self       ):
		_lowercase   =self.get_image_processor()
		_lowercase   =self.get_tokenizer()

		_lowercase   =PixaStructProcessor(tokenizer=lowerCAmelCase_       ,  image_processor=lowerCAmelCase_       )

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

		_lowercase   =processor.batch_decode(lowerCAmelCase_       )
		_lowercase   =tokenizer.batch_decode(lowerCAmelCase_       )

		self.assertListEqual(lowerCAmelCase_       ,  lowerCAmelCase_       )


	def __lowerCAmelCase      (   self       ):
		_lowercase   =self.get_image_processor()
		_lowercase   =self.get_tokenizer()

		_lowercase   =PixaStructProcessor(tokenizer=lowerCAmelCase_       ,  image_processor=lowerCAmelCase_       )

		_lowercase   ="lower newer"
		_lowercase   =self.prepare_image_inputs()

		_lowercase   =processor(text=lowerCAmelCase_       ,  images=lowerCAmelCase_       )

		# For now the processor supports only ["flattened_patches", "input_ids", "attention_mask", "decoder_attention_mask"]
		self.assertListEqual(
		    list(inputs.keys()       )       ,  ["flattened_patches", "attention_mask", "decoder_attention_mask", "decoder_input_ids"]       )

		_lowercase   =processor(text=lowerCAmelCase_       )

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






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

from tokenizers import pre_tokenizers

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_deberta import DebertaTokenizer


if TYPE_CHECKING:
   from transformers.pipelines.conversational import Conversation


UpperCAmelCase__						:	str	     = logging.get_logger(__name__)

UpperCAmelCase__						:	int	     = {'vocab_file': 'vocab.json', 'merges_file': 'merges.txt', 'tokenizer_file': 'tokenizer.json'}

UpperCAmelCase__						:	int	     = {
    'vocab_file': {
        'microsoft/deberta-base': 'https://huggingface.co/microsoft/deberta-base/resolve/main/vocab.json',
        'microsoft/deberta-large': 'https://huggingface.co/microsoft/deberta-large/resolve/main/vocab.json',
        'microsoft/deberta-xlarge': 'https://huggingface.co/microsoft/deberta-xlarge/resolve/main/vocab.json',
        'microsoft/deberta-base-mnli': 'https://huggingface.co/microsoft/deberta-base-mnli/resolve/main/vocab.json',
        'microsoft/deberta-large-mnli': 'https://huggingface.co/microsoft/deberta-large-mnli/resolve/main/vocab.json',
        'microsoft/deberta-xlarge-mnli': (
            'https://huggingface.co/microsoft/deberta-xlarge-mnli/resolve/main/vocab.json'
        ),
    },
    'merges_file': {
        'microsoft/deberta-base': 'https://huggingface.co/microsoft/deberta-base/resolve/main/merges.txt',
        'microsoft/deberta-large': 'https://huggingface.co/microsoft/deberta-large/resolve/main/merges.txt',
        'microsoft/deberta-xlarge': 'https://huggingface.co/microsoft/deberta-xlarge/resolve/main/merges.txt',
        'microsoft/deberta-base-mnli': 'https://huggingface.co/microsoft/deberta-base-mnli/resolve/main/merges.txt',
        'microsoft/deberta-large-mnli': 'https://huggingface.co/microsoft/deberta-large-mnli/resolve/main/merges.txt',
        'microsoft/deberta-xlarge-mnli': (
            'https://huggingface.co/microsoft/deberta-xlarge-mnli/resolve/main/merges.txt'
        ),
    },
}

UpperCAmelCase__						:	List[str]	     = {
    'microsoft/deberta-base': 512,
    'microsoft/deberta-large': 512,
    'microsoft/deberta-xlarge': 512,
    'microsoft/deberta-base-mnli': 512,
    'microsoft/deberta-large-mnli': 512,
    'microsoft/deberta-xlarge-mnli': 512,
}

UpperCAmelCase__						:	Optional[Any]	     = {
    'microsoft/deberta-base': {'do_lower_case': False},
    'microsoft/deberta-large': {'do_lower_case': False},
}






class UpperCAmelCase  (		SCREAMING_SNAKE_CASE__  ):





   '''simple docstring'''
   __UpperCamelCase						:       Union[str, Any]												=       VOCAB_FILES_NAMES
   __UpperCamelCase						:       Union[str, Any]												=       PRETRAINED_VOCAB_FILES_MAP
   __UpperCamelCase						:       Union[str, Any]												=       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   __UpperCamelCase						:       str												=       ['''input_ids''', '''attention_mask''', '''token_type_ids''']
   __UpperCamelCase						:       Any												=       DebertaTokenizer


   def __init__(   self	:				Union[str, Any]     ,     lowerCAmelCase_	:				Optional[Any]=None     ,     lowerCAmelCase_	:				int=None     ,     lowerCAmelCase_	:				Dict=None     ,     lowerCAmelCase_	:				int="replace"     ,     lowerCAmelCase_	:				List[str]="[CLS]"     ,     lowerCAmelCase_	:				str="[SEP]"     ,     lowerCAmelCase_	:				int="[SEP]"     ,     lowerCAmelCase_	:				Any="[CLS]"     ,     lowerCAmelCase_	:				Dict="[UNK]"     ,     lowerCAmelCase_	:				Optional[int]="[PAD]"     ,     lowerCAmelCase_	:				List[str]="[MASK]"     ,     lowerCAmelCase_	:				Optional[Any]=False     ,     **lowerCAmelCase_	:				List[Any]     ,     ):

      """simple docstring"""
      super().__init__(
          lowerCAmelCase_     ,     lowerCAmelCase_     ,     tokenizer_file=lowerCAmelCase_     ,     errors=lowerCAmelCase_     ,     bos_token=lowerCAmelCase_     ,     eos_token=lowerCAmelCase_     ,     unk_token=lowerCAmelCase_     ,     sep_token=lowerCAmelCase_     ,     cls_token=lowerCAmelCase_     ,     pad_token=lowerCAmelCase_     ,     mask_token=lowerCAmelCase_     ,     add_prefix_space=lowerCAmelCase_     ,     **lowerCAmelCase_     ,     )
      _A:    List[str]						     =			kwargs.pop('''add_bos_token'''     ,     lowerCAmelCase_	)

      _A:    Optional[int]						     =			json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()	)
      if pre_tok_state.get('''add_prefix_space'''     ,     lowerCAmelCase_	) != add_prefix_space:
         _A:    Dict						     =			getattr(lowerCAmelCase_     ,     pre_tok_state.pop('''type'''	)	)
         _A:    List[Any]						     =			add_prefix_space
         _A:    Optional[int]						     =			pre_tok_class(**lowerCAmelCase_	)

      _A:    Optional[Any]						     =			add_prefix_space


   @property
   def 					__magic_name__							(   self	:				Optional[Any]	):

      """simple docstring"""
      if self._mask_token is None:
         if self.verbose:
            logger.error('''Using mask_token, but it is not set yet.'''	)
         return None
      return str(self._mask_token	)


   @mask_token.setter
   def 					__magic_name__							(   self	:				str     ,     lowerCAmelCase_	:				str	):

      """simple docstring"""
      _A:    Dict						     =			AddedToken(lowerCAmelCase_     ,     lstrip=lowerCAmelCase_     ,     rstrip=lowerCAmelCase_	) if isinstance(lowerCAmelCase_     ,     lowerCAmelCase_	) else value
      _A:    str						     =			value


   def 					__magic_name__							(   self	:				Optional[Any]     ,     lowerCAmelCase_	:				List[int]     ,     lowerCAmelCase_	:				Optional[List[int]] = None	):

      """simple docstring"""
      if token_ids_a is None:
         return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
      _A:    str						     =			[self.cls_token_id]
      _A:    Any						     =			[self.sep_token_id]
      return cls + token_ids_a + sep + token_ids_a + sep


   def 					__magic_name__							(   self	:				Optional[int]     ,     lowerCAmelCase_	:				List[int]     ,     lowerCAmelCase_	:				Optional[List[int]] = None	):

      """simple docstring"""
      _A:    Dict						     =			[self.sep_token_id]
      _A:    Any						     =			[self.cls_token_id]

      if token_ids_a is None:
         return len(cls + token_ids_a + sep	) * [0]
      return len(cls + token_ids_a + sep	) * [0] + len(token_ids_a + sep	) * [1]


   def 					__magic_name__							(   self	:				Tuple     ,     *lowerCAmelCase_	:				Dict     ,     **lowerCAmelCase_	:				Tuple	):

      """simple docstring"""
      _A:    Any						     =			kwargs.get('''is_split_into_words'''     ,     lowerCAmelCase_	)
      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(*lowerCAmelCase_     ,     **lowerCAmelCase_	)


   def 					__magic_name__							(   self	:				List[Any]     ,     *lowerCAmelCase_	:				Optional[Any]     ,     **lowerCAmelCase_	:				Any	):

      """simple docstring"""
      _A:    Optional[Any]						     =			kwargs.get('''is_split_into_words'''     ,     lowerCAmelCase_	)

      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(*lowerCAmelCase_     ,     **lowerCAmelCase_	)


   def 					__magic_name__							(   self	:				Optional[int]     ,     lowerCAmelCase_	:				str     ,     lowerCAmelCase_	:				Optional[str] = None	):

      """simple docstring"""
      _A:    Union[str, Any]						     =			self._tokenizer.model.save(lowerCAmelCase_     ,     name=lowerCAmelCase_	)
      return tuple(lowerCAmelCase_	)


   def 					__magic_name__							(   self	:				Union[str, Any]     ,     lowerCAmelCase_	:				"Conversation"	):

      """simple docstring"""
      _A:    List[Any]						     =			[]
      for is_user, text in conversation.iter_texts():
         input_ids.extend(self.encode(lowerCAmelCase_     ,     add_special_tokens=lowerCAmelCase_	) + [self.eos_token_id]	)

      if len(lowerCAmelCase_	) > self.model_max_length:
         _A:    Union[str, Any]						     =			input_ids[-self.model_max_length :]
      return input_ids



