




"""simple docstring"""

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					:     Optional[Any]				       =    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     lowercase (      unittest.TestCase):







    def    a_					(							self				:   Union[str, Any]    ):



     """simple docstring"""

     A_					:			Optional[Any]						=						tempfile.mkdtemp()

     A_					:			Any						=						PixaStructImageProcessor()
     A_					:			List[str]						=						TaTokenizer.from_pretrained('''t5-small'''    )

     A_					:			Dict						=						PixaStructProcessor(__lowercase ,       __lowercase    )

     processor.save_pretrained(self.tmpdirname    )







    def    a_					(							self				:   List[Any] ,       **_lowerCamelCase				:   int    ):



     """simple docstring"""

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







    def    a_					(							self				:   List[Any] ,       **_lowerCamelCase				:   Union[str, Any]    ):



     """simple docstring"""

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







    def    a_					(							self				:   Dict    ):



     """simple docstring"""

     shutil.rmtree(self.tmpdirname    )







    def    a_					(							self				:   int    ):



     """simple docstring"""

     A_					:			Tuple						=						[np.random.randint(2_55 ,       size=(3, 30, 4_00) ,       dtype=np.uinta    )]

     A_					:			List[str]						=						[Image.fromarray(np.moveaxis(__lowercase ,       0 ,       -1    )    ) for x in image_inputs]

     return image_inputs







    def    a_					(							self				:   Any    ):



     """simple docstring"""

     A_					:			List[Any]						=						PixaStructProcessor(tokenizer=self.get_tokenizer() ,       image_processor=self.get_image_processor()    )
     processor.save_pretrained(self.tmpdirname    )

     A_					:			Optional[Any]						=						self.get_tokenizer(bos_token='''(BOS)''' ,       eos_token='''(EOS)'''    )
     A_					:			Optional[int]						=						self.get_image_processor(do_normalize=__lowercase ,       padding_value=1.0    )

     A_					:			List[Any]						=						PixaStructProcessor.from_pretrained(
         self.tmpdirname ,       bos_token='''(BOS)''' ,       eos_token='''(EOS)''' ,       do_normalize=__lowercase ,       padding_value=1.0    )

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

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







    def    a_					(							self				:   int    ):



     """simple docstring"""

     A_					:			Optional[Any]						=						self.get_image_processor()
     A_					:			List[Any]						=						self.get_tokenizer()

     A_					:			Optional[Any]						=						PixaStructProcessor(tokenizer=__lowercase ,       image_processor=__lowercase    )

     A_					:			Dict						=						self.prepare_image_inputs()

     A_					:			Any						=						image_processor(__lowercase ,       return_tensors='''np'''    )
     A_					:			Dict						=						processor(images=__lowercase ,       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    a_					(							self				:   str    ):



     """simple docstring"""

     A_					:			Any						=						self.get_image_processor()
     A_					:			Any						=						self.get_tokenizer()

     A_					:			Dict						=						PixaStructProcessor(tokenizer=__lowercase ,       image_processor=__lowercase    )

     A_					:			Optional[Any]						=						'''lower newer'''

     A_					:			Optional[int]						=						processor(text=__lowercase    )

     A_					:			int						=						tokenizer(__lowercase ,       return_token_type_ids=__lowercase ,       add_special_tokens=__lowercase    )

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







    def    a_					(							self				:   Any    ):



     """simple docstring"""

     A_					:			Optional[Any]						=						self.get_image_processor()
     A_					:			Union[str, Any]						=						self.get_tokenizer()

     A_					:			Union[str, Any]						=						PixaStructProcessor(tokenizer=__lowercase ,       image_processor=__lowercase    )

     A_					:			List[Any]						=						'''lower newer'''
     A_					:			int						=						self.prepare_image_inputs()

     A_					:			List[str]						=						processor(text=__lowercase ,       images=__lowercase    )

     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(__lowercase    ):
      processor()







    def    a_					(							self				:   Dict    ):



     """simple docstring"""

     A_					:			Optional[int]						=						self.get_image_processor()
     A_					:			Tuple						=						self.get_tokenizer()

     A_					:			Any						=						PixaStructProcessor(tokenizer=__lowercase ,       image_processor=__lowercase    )

     A_					:			int						=						'''lower newer'''
     A_					:			Union[str, Any]						=						self.prepare_image_inputs()

     A_					:			Union[str, Any]						=						processor(text=__lowercase ,       images=__lowercase    )

     A_					:			str						=						[5_12, 10_24, 20_48, 40_96]
     A_					:			Optional[Any]						=						[7_70, 7_70, 7_70, 7_70]
     # with text
     for i, max_patch in enumerate(__lowercase    ):
      A_					:			Tuple						=						processor(text=__lowercase ,       images=__lowercase ,       max_patches=__lowercase    )
      self.assertEqual(inputs['''flattened_patches'''][0].shape[0] ,       __lowercase    )
      self.assertEqual(inputs['''flattened_patches'''][0].shape[1] ,       expected_hidden_size[i]    )

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







    def    a_					(							self				:   Tuple    ):



     """simple docstring"""

     A_					:			List[Any]						=						self.get_image_processor()
     A_					:			str						=						self.get_tokenizer()

     A_					:			Union[str, Any]						=						PixaStructProcessor(tokenizer=__lowercase ,       image_processor=__lowercase    )

     A_					:			Any						=						[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

     A_					:			List[str]						=						processor.batch_decode(__lowercase    )
     A_					:			Tuple						=						tokenizer.batch_decode(__lowercase    )

     self.assertListEqual(__lowercase ,       __lowercase    )







    def    a_					(							self				:   Any    ):



     """simple docstring"""

     A_					:			Optional[Any]						=						self.get_image_processor()
     A_					:			List[Any]						=						self.get_tokenizer()

     A_					:			Optional[int]						=						PixaStructProcessor(tokenizer=__lowercase ,       image_processor=__lowercase    )

     A_					:			List[str]						=						'''lower newer'''
     A_					:			List[str]						=						self.prepare_image_inputs()

     A_					:			Union[str, Any]						=						processor(text=__lowercase ,       images=__lowercase    )

     # 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''']    )

     A_					:			List[Any]						=						processor(text=__lowercase    )

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











"""simple docstring"""

from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast
from ...utils import logging


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

_lowerCamelCase					:     Dict				       =    {
    'uclanlp/plbart-base': 'https://huggingface.co/uclanlp/plbart-base/resolve/main/config.json',
    # See all PLBART models at https://huggingface.co/models?filter=plbart
}

class     lowercase (      __UpperCAmelCase):
				__lowerCAmelCase      :      List[str]					    =				"""plbart"""
				__lowerCAmelCase      :      int					    =				["""past_key_values"""]
				__lowerCAmelCase      :      Union[str, Any]					    =				{"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}







				def __init__(							self				:   Any ,       _lowerCamelCase				:   Any=5_00_05 ,       _lowerCamelCase				:   List[str]=10_24 ,       _lowerCamelCase				:   Union[str, Any]=6 ,       _lowerCamelCase				:   Any=30_72 ,       _lowerCamelCase				:   List[str]=12 ,       _lowerCamelCase				:   Tuple=6 ,       _lowerCamelCase				:   Dict=30_72 ,       _lowerCamelCase				:   Optional[int]=12 ,       _lowerCamelCase				:   Optional[int]=0.0 ,       _lowerCamelCase				:   Optional[int]=0.0 ,       _lowerCamelCase				:   Tuple=True ,       _lowerCamelCase				:   Optional[int]=True ,       _lowerCamelCase				:   str="gelu" ,       _lowerCamelCase				:   int=7_68 ,       _lowerCamelCase				:   Union[str, Any]=0.1 ,       _lowerCamelCase				:   Optional[Any]=0.1 ,       _lowerCamelCase				:   Tuple=0.0 ,       _lowerCamelCase				:   Optional[int]=0.02 ,       _lowerCamelCase				:   Dict=0.0 ,       _lowerCamelCase				:   Dict=True ,       _lowerCamelCase				:   List[str]=1 ,       _lowerCamelCase				:   str=0 ,       _lowerCamelCase				:   int=2 ,       _lowerCamelCase				:   str=2 ,       **_lowerCamelCase				:   Dict ,       ):



					"""simple docstring"""

					A_					:			int						=						vocab_size
					A_					:			Tuple						=						max_position_embeddings
					A_					:			Optional[Any]						=						d_model
					A_					:			Optional[int]						=						encoder_ffn_dim
					A_					:			Optional[int]						=						encoder_layers
					A_					:			List[str]						=						encoder_attention_heads
					A_					:			Dict						=						decoder_ffn_dim
					A_					:			List[str]						=						decoder_layers
					A_					:			str						=						decoder_attention_heads
					A_					:			List[Any]						=						dropout
					A_					:			int						=						attention_dropout
					A_					:			Dict						=						activation_dropout
					A_					:			int						=						activation_function
					A_					:			List[str]						=						init_std
					A_					:			int						=						encoder_layerdrop
					A_					:			int						=						decoder_layerdrop
					A_					:			Optional[int]						=						classifier_dropout
					A_					:			Optional[int]						=						use_cache
					A_					:			Dict						=						encoder_layers
					A_					:			int						=						scale_embedding  # scale factor will be sqrt(d_model) if True
					super().__init__(
					    pad_token_id=_lowerCamelCase ,       bos_token_id=_lowerCamelCase ,       eos_token_id=_lowerCamelCase ,       is_encoder_decoder=_lowerCamelCase ,       forced_eos_token_id=_lowerCamelCase ,       **_lowerCamelCase ,       )

class     lowercase (      __UpperCAmelCase):







				@property
				def    a_					(							self				:   Any    ):



					"""simple docstring"""

					return OrderedDict(
					    [
					        ('''input_ids''', {0: '''batch''', 1: '''sequence'''}),
					        ('''attention_mask''', {0: '''batch''', 1: '''sequence'''}),
					    ]    )







				@property
				def    a_					(							self				:   Dict    ):



					"""simple docstring"""

					if self.use_past:
						return OrderedDict(
						    [
						        ('''last_hidden_state''', {0: '''batch''', 1: '''sequence'''}),
						        ('''past_keys''', {0: '''batch''', 2: '''sequence'''}),
						        ('''encoder_last_hidden_state''', {0: '''batch''', 1: '''sequence'''}),
						    ]    )
					else:
						return OrderedDict(
						    [
						        ('''last_hidden_state''', {0: '''batch''', 1: '''sequence'''}),
						        ('''encoder_last_hidden_state''', {0: '''batch''', 1: '''sequence'''}),
						    ]    )





