"""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:
       _UpperCamelCase       :						List[str]     =   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 					UpperCAmelCase_     (   unittest.TestCase):


      def 		_UpperCAmelCase	(       self							)	-> List[Any]:
             lowercase__      :		Optional[int]          =     tempfile.mkdtemp()

             lowercase__      :		Optional[int]          =     PixaStructImageProcessor()
             lowercase__      :		Optional[Any]          =     TaTokenizer.from_pretrained('t5-small'							)

             lowercase__      :		Any          =     PixaStructProcessor(a		,      a							)

             processor.save_pretrained(self.tmpdirname							)


      def 		_UpperCAmelCase	(       self		,      **a							)	-> Union[str, Any]:
             return AutoProcessor.from_pretrained(self.tmpdirname		,      **a							).tokenizer


      def 		_UpperCAmelCase	(       self		,      **a							)	-> Tuple:
             return AutoProcessor.from_pretrained(self.tmpdirname		,      **a							).image_processor


      def 		_UpperCAmelCase	(       self							)	-> List[Any]:
             shutil.rmtree(self.tmpdirname							)


      def 		_UpperCAmelCase	(       self							)	-> List[Any]:
             lowercase__      :		Optional[int]          =     [np.random.randint(2_5_5		,      size=(3, 3_0, 4_0_0)		,      dtype=np.uinta							)]

             lowercase__      :		Optional[Any]          =     [Image.fromarray(np.moveaxis(a		,      0		,      -1							)							) for x in image_inputs]

             return image_inputs


      def 		_UpperCAmelCase	(       self							)	-> Union[str, Any]:
             lowercase__      :		Tuple          =     PixaStructProcessor(tokenizer=self.get_tokenizer()		,      image_processor=self.get_image_processor()							)
             processor.save_pretrained(self.tmpdirname							)

             lowercase__      :		List[Any]          =     self.get_tokenizer(bos_token='(BOS)'		,      eos_token='(EOS)'							)
             lowercase__      :		Union[str, Any]          =     self.get_image_processor(do_normalize=a		,      padding_value=1.0							)

             lowercase__      :		Optional[int]          =     PixaStructProcessor.from_pretrained(
                 self.tmpdirname		,      bos_token='(BOS)'		,      eos_token='(EOS)'		,      do_normalize=a		,      padding_value=1.0							)

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

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


      def 		_UpperCAmelCase	(       self							)	-> Union[str, Any]:
             lowercase__      :		Optional[int]          =     self.get_image_processor()
             lowercase__      :		Union[str, Any]          =     self.get_tokenizer()

             lowercase__      :		List[str]          =     PixaStructProcessor(tokenizer=a		,      image_processor=a							)

             lowercase__      :		Optional[int]          =     self.prepare_image_inputs()

             lowercase__      :		Optional[Any]          =     image_processor(a		,      return_tensors='np'							)
             lowercase__      :		Union[str, Any]          =     processor(images=a		,      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							)	-> str:
             lowercase__      :		int          =     self.get_image_processor()
             lowercase__      :		str          =     self.get_tokenizer()

             lowercase__      :		List[str]          =     PixaStructProcessor(tokenizer=a		,      image_processor=a							)

             lowercase__      :		int          =     'lower newer'

             lowercase__      :		Tuple          =     processor(text=a							)

             lowercase__      :		Union[str, Any]          =     tokenizer(a		,      return_token_type_ids=a		,      add_special_tokens=a							)

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


      def 		_UpperCAmelCase	(       self							)	-> int:
             lowercase__      :		str          =     self.get_image_processor()
             lowercase__      :		Optional[int]          =     self.get_tokenizer()

             lowercase__      :		List[Any]          =     PixaStructProcessor(tokenizer=a		,      image_processor=a							)

             lowercase__      :		Dict          =     'lower newer'
             lowercase__      :		Optional[Any]          =     self.prepare_image_inputs()

             lowercase__      :		int          =     processor(text=a		,      images=a							)

             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(a							):
                    processor()


      def 		_UpperCAmelCase	(       self							)	-> Tuple:
             lowercase__      :		List[Any]          =     self.get_image_processor()
             lowercase__      :		Union[str, Any]          =     self.get_tokenizer()

             lowercase__      :		Union[str, Any]          =     PixaStructProcessor(tokenizer=a		,      image_processor=a							)

             lowercase__      :		Optional[int]          =     'lower newer'
             lowercase__      :		Optional[Any]          =     self.prepare_image_inputs()

             lowercase__      :		List[Any]          =     processor(text=a		,      images=a							)

             lowercase__      :		Dict          =     [5_1_2, 1_0_2_4, 2_0_4_8, 4_0_9_6]
             lowercase__      :		Tuple          =     [7_7_0, 7_7_0, 7_7_0, 7_7_0]
             # with text
             for i, max_patch in enumerate(a							):
                    lowercase__      :		int          =     processor(text=a		,      images=a		,      max_patches=a							)
                    self.assertEqual(inputs['flattened_patches'][0].shape[0]		,      a							)
                    self.assertEqual(inputs['flattened_patches'][0].shape[1]		,      expected_hidden_size[i]							)

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


      def 		_UpperCAmelCase	(       self							)	-> Optional[Any]:
             lowercase__      :		int          =     self.get_image_processor()
             lowercase__      :		Any          =     self.get_tokenizer()

             lowercase__      :		int          =     PixaStructProcessor(tokenizer=a		,      image_processor=a							)

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

             lowercase__      :		Optional[Any]          =     processor.batch_decode(a							)
             lowercase__      :		Optional[Any]          =     tokenizer.batch_decode(a							)

             self.assertListEqual(a		,      a							)


      def 		_UpperCAmelCase	(       self							)	-> List[str]:
             lowercase__      :		List[Any]          =     self.get_image_processor()
             lowercase__      :		Optional[Any]          =     self.get_tokenizer()

             lowercase__      :		Union[str, Any]          =     PixaStructProcessor(tokenizer=a		,      image_processor=a							)

             lowercase__      :		Dict          =     'lower newer'
             lowercase__      :		int          =     self.prepare_image_inputs()

             lowercase__      :		Tuple          =     processor(text=a		,      images=a							)

             # 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__      :		List[Any]          =     processor(text=a							)

             # 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"""





import torch

from transformers import PreTrainedModel

from .custom_configuration import CustomConfig, NoSuperInitConfig


class 					UpperCAmelCase_     (   _a):
      lowerCamelCase__		:   List[str]			  =  CustomConfig


      def __init__(       self		,      a							)	-> Optional[int]:
             super().__init__(a							)
             lowercase__      :		Any          =     torch.nn.Linear(config.hidden_size		,      config.hidden_size							)


      def 		_UpperCAmelCase	(       self		,      a							)	-> Union[str, Any]:
             return self.linear(a							)


      def 		_UpperCAmelCase	(       self		,      a							)	-> Tuple:
             pass





class 					UpperCAmelCase_     (   _a):
      lowerCamelCase__		:   Optional[int]			  =  NoSuperInitConfig


      def __init__(       self		,      a							)	-> int:
             super().__init__(a							)
             lowercase__      :		List[Any]          =     torch.nn.Linear(config.attribute		,      config.attribute							)


      def 		_UpperCAmelCase	(       self		,      a							)	-> Optional[int]:
             return self.linear(a							)


      def 		_UpperCAmelCase	(       self		,      a							)	-> Optional[Any]:
             pass

