


'''simple docstring'''





import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, DDIMScheduler, DDPMScheduler, StableDiffusionUpscalePipeline, UNetaDConditionModel
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu


enable_full_determinism()




class 				UpperCAmelCase			(			unittest.TestCase						):


 def        __SCREAMING_SNAKE_CASE					(      self							:	str ):
    # clean up the VRAM after each test
    super().tearDown()
    gc.collect()
    torch.cuda.empty_cache()


 @property
 def        __SCREAMING_SNAKE_CASE					(      self							:	str ):
    UpperCAmelCase__	:List[Any]      =      1
    UpperCAmelCase__	:Dict      =      3
    UpperCAmelCase__	:int      =      (3_2, 3_2)

    UpperCAmelCase__	:Optional[Any]      =      floats_tensor((batch_size, num_channels) + sizes			,       rng=random.Random(0 ) ).to(__lowerCamelCase )
    return image


 @property
 def        __SCREAMING_SNAKE_CASE					(      self							:	Optional[Any] ):
    torch.manual_seed(0 )
    UpperCAmelCase__	:List[str]      =      UNetaDConditionModel(
        block_out_channels=(3_2, 3_2, 6_4)			,       layers_per_block=2			,       sample_size=3_2			,       in_channels=7			,       out_channels=4			,       down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''', '''CrossAttnDownBlock2D''')			,       up_block_types=('''CrossAttnUpBlock2D''', '''CrossAttnUpBlock2D''', '''UpBlock2D''')			,       cross_attention_dim=3_2			,       attention_head_dim=8			,       use_linear_projection=__lowerCamelCase			,       only_cross_attention=(True, True, False)			,       num_class_embeds=1_0_0			,       )
    return model


 @property
 def        __SCREAMING_SNAKE_CASE					(      self							:	int ):
    torch.manual_seed(0 )
    UpperCAmelCase__	:Any      =      AutoencoderKL(
        block_out_channels=[3_2, 3_2, 6_4]			,       in_channels=3			,       out_channels=3			,       down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''', '''DownEncoderBlock2D''']			,       up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''', '''UpDecoderBlock2D''']			,       latent_channels=4			,       )
    return model


 @property
 def        __SCREAMING_SNAKE_CASE					(      self							:	Any ):
    torch.manual_seed(0 )
    UpperCAmelCase__	:Tuple      =      CLIPTextConfig(
        bos_token_id=0			,       eos_token_id=2			,       hidden_size=3_2			,       intermediate_size=3_7			,       layer_norm_eps=1e-05			,       num_attention_heads=4			,       num_hidden_layers=5			,       pad_token_id=1			,       vocab_size=1_0_0_0			,       hidden_act='''gelu'''			,       projection_dim=5_1_2			,       )
    return CLIPTextModel(__lowerCamelCase )


 def        __SCREAMING_SNAKE_CASE					(      self							:	str ):
    UpperCAmelCase__	:Optional[Any]      =      '''cpu'''  # ensure determinism for the device-dependent torch.Generator
    UpperCAmelCase__	:Optional[Any]      =      self.dummy_cond_unet_upscale
    UpperCAmelCase__	:List[str]      =      DDPMScheduler()
    UpperCAmelCase__	:List[str]      =      DDIMScheduler(prediction_type='''v_prediction''' )
    UpperCAmelCase__	:List[str]      =      self.dummy_vae
    UpperCAmelCase__	:Dict      =      self.dummy_text_encoder
    UpperCAmelCase__	:Union[str, Any]      =      CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip''' )

    UpperCAmelCase__	:List[Any]      =      self.dummy_image.cpu().permute(0			,       2			,       3			,       1 )[0]
    UpperCAmelCase__	:Union[str, Any]      =      Image.fromarray(np.uinta(__lowerCamelCase ) ).convert('''RGB''' ).resize((6_4, 6_4) )

    # make sure here that pndm scheduler skips prk
    UpperCAmelCase__	:Tuple      =      StableDiffusionUpscalePipeline(
        unet=__lowerCamelCase			,       low_res_scheduler=__lowerCamelCase			,       scheduler=__lowerCamelCase			,       vae=__lowerCamelCase			,       text_encoder=__lowerCamelCase			,       tokenizer=__lowerCamelCase			,       max_noise_level=3_5_0			,       )
    UpperCAmelCase__	:int      =      sd_pipe.to(__lowerCamelCase )
    sd_pipe.set_progress_bar_config(disable=__lowerCamelCase )

    UpperCAmelCase__	:Union[str, Any]      =      '''A painting of a squirrel eating a burger'''
    UpperCAmelCase__	:Tuple      =      torch.Generator(device=__lowerCamelCase ).manual_seed(0 )
    UpperCAmelCase__	:Dict      =      sd_pipe(
        [prompt]			,       image=__lowerCamelCase			,       generator=__lowerCamelCase			,       guidance_scale=6.0			,       noise_level=2_0			,       num_inference_steps=2			,       output_type='''np'''			,       )

    UpperCAmelCase__	:List[Any]      =      output.images

    UpperCAmelCase__	:Dict      =      torch.Generator(device=__lowerCamelCase ).manual_seed(0 )
    UpperCAmelCase__	:Union[str, Any]      =      sd_pipe(
        [prompt]			,       image=__lowerCamelCase			,       generator=__lowerCamelCase			,       guidance_scale=6.0			,       noise_level=2_0			,       num_inference_steps=2			,       output_type='''np'''			,       return_dict=__lowerCamelCase			,       )[0]

    UpperCAmelCase__	:Union[str, Any]      =      image[0, -3:, -3:, -1]
    UpperCAmelCase__	:Union[str, Any]      =      image_from_tuple[0, -3:, -3:, -1]

    UpperCAmelCase__	:Dict      =      low_res_image.size[0] * 4
    assert image.shape == (1, expected_height_width, expected_height_width, 3)
    UpperCAmelCase__	:int      =      np.array([0.31_13, 0.39_10, 0.42_72, 0.48_59, 0.50_61, 0.46_52, 0.53_62, 0.57_15, 0.56_61] )

    assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
    assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1e-2


 def        __SCREAMING_SNAKE_CASE					(      self							:	List[str] ):
    UpperCAmelCase__	:List[Any]      =      '''cpu'''  # ensure determinism for the device-dependent torch.Generator
    UpperCAmelCase__	:Any      =      self.dummy_cond_unet_upscale
    UpperCAmelCase__	:Union[str, Any]      =      DDPMScheduler()
    UpperCAmelCase__	:Dict      =      DDIMScheduler(prediction_type='''v_prediction''' )
    UpperCAmelCase__	:Optional[int]      =      self.dummy_vae
    UpperCAmelCase__	:Dict      =      self.dummy_text_encoder
    UpperCAmelCase__	:Union[str, Any]      =      CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip''' )

    UpperCAmelCase__	:List[str]      =      self.dummy_image.cpu().permute(0			,       2			,       3			,       1 )[0]
    UpperCAmelCase__	:Optional[Any]      =      Image.fromarray(np.uinta(__lowerCamelCase ) ).convert('''RGB''' ).resize((6_4, 6_4) )

    # make sure here that pndm scheduler skips prk
    UpperCAmelCase__	:List[str]      =      StableDiffusionUpscalePipeline(
        unet=__lowerCamelCase			,       low_res_scheduler=__lowerCamelCase			,       scheduler=__lowerCamelCase			,       vae=__lowerCamelCase			,       text_encoder=__lowerCamelCase			,       tokenizer=__lowerCamelCase			,       max_noise_level=3_5_0			,       )
    UpperCAmelCase__	:Dict      =      sd_pipe.to(__lowerCamelCase )
    sd_pipe.set_progress_bar_config(disable=__lowerCamelCase )

    UpperCAmelCase__	:int      =      '''A painting of a squirrel eating a burger'''
    UpperCAmelCase__	:Any      =      sd_pipe(
        2 * [prompt]			,       image=2 * [low_res_image]			,       guidance_scale=6.0			,       noise_level=2_0			,       num_inference_steps=2			,       output_type='''np'''			,       )
    UpperCAmelCase__	:Optional[Any]      =      output.images
    assert image.shape[0] == 2

    UpperCAmelCase__	:Dict      =      torch.Generator(device=__lowerCamelCase ).manual_seed(0 )
    UpperCAmelCase__	:Dict      =      sd_pipe(
        [prompt]			,       image=__lowerCamelCase			,       generator=__lowerCamelCase			,       num_images_per_prompt=2			,       guidance_scale=6.0			,       noise_level=2_0			,       num_inference_steps=2			,       output_type='''np'''			,       )
    UpperCAmelCase__	:Optional[Any]      =      output.images
    assert image.shape[0] == 2





 @unittest.skipIf(torch_device != '''cuda'''			,       '''This test requires a GPU''' )
 def        __SCREAMING_SNAKE_CASE					(      self							:	Tuple ):
    UpperCAmelCase__	:Dict      =      self.dummy_cond_unet_upscale
    UpperCAmelCase__	:Dict      =      DDPMScheduler()
    UpperCAmelCase__	:int      =      DDIMScheduler(prediction_type='''v_prediction''' )
    UpperCAmelCase__	:Any      =      self.dummy_vae
    UpperCAmelCase__	:Optional[int]      =      self.dummy_text_encoder
    UpperCAmelCase__	:List[Any]      =      CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip''' )

    UpperCAmelCase__	:Tuple      =      self.dummy_image.cpu().permute(0			,       2			,       3			,       1 )[0]
    UpperCAmelCase__	:Tuple      =      Image.fromarray(np.uinta(__lowerCamelCase ) ).convert('''RGB''' ).resize((6_4, 6_4) )

    # put models in fp16, except vae as it overflows in fp16
    UpperCAmelCase__	:str      =      unet.half()
    UpperCAmelCase__	:List[str]      =      text_encoder.half()

    # make sure here that pndm scheduler skips prk
    UpperCAmelCase__	:Tuple      =      StableDiffusionUpscalePipeline(
        unet=__lowerCamelCase			,       low_res_scheduler=__lowerCamelCase			,       scheduler=__lowerCamelCase			,       vae=__lowerCamelCase			,       text_encoder=__lowerCamelCase			,       tokenizer=__lowerCamelCase			,       max_noise_level=3_5_0			,       )
    UpperCAmelCase__	:Dict      =      sd_pipe.to(__lowerCamelCase )
    sd_pipe.set_progress_bar_config(disable=__lowerCamelCase )

    UpperCAmelCase__	:Optional[int]      =      '''A painting of a squirrel eating a burger'''
    UpperCAmelCase__	:Optional[int]      =      torch.manual_seed(0 )
    UpperCAmelCase__	:List[Any]      =      sd_pipe(
        [prompt]			,       image=__lowerCamelCase			,       generator=__lowerCamelCase			,       num_inference_steps=2			,       output_type='''np'''			,       ).images

    UpperCAmelCase__	:List[Any]      =      low_res_image.size[0] * 4
    assert image.shape == (1, expected_height_width, expected_height_width, 3)




@slow
@require_torch_gpu
class 				UpperCAmelCase			(			unittest.TestCase						):


 def        __SCREAMING_SNAKE_CASE					(      self							:	Optional[Any] ):
    # clean up the VRAM after each test
    super().tearDown()
    gc.collect()
    torch.cuda.empty_cache()


 def        __SCREAMING_SNAKE_CASE					(      self							:	Optional[int] ):
    UpperCAmelCase__	:Optional[Any]      =      load_image(
        '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
        '''/sd2-upscale/low_res_cat.png''' )
    UpperCAmelCase__	:Union[str, Any]      =      load_numpy(
        '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-upscale'''
        '''/upsampled_cat.npy''' )

    UpperCAmelCase__	:str      =      '''stabilityai/stable-diffusion-x4-upscaler'''
    UpperCAmelCase__	:Union[str, Any]      =      StableDiffusionUpscalePipeline.from_pretrained(__lowerCamelCase )
    pipe.to(__lowerCamelCase )
    pipe.set_progress_bar_config(disable=__lowerCamelCase )
    pipe.enable_attention_slicing()

    UpperCAmelCase__	:Optional[Any]      =      '''a cat sitting on a park bench'''

    UpperCAmelCase__	:Dict      =      torch.manual_seed(0 )
    UpperCAmelCase__	:Any      =      pipe(
        prompt=__lowerCamelCase			,       image=__lowerCamelCase			,       generator=__lowerCamelCase			,       output_type='''np'''			,       )
    UpperCAmelCase__	:List[str]      =      output.images[0]

    assert image.shape == (5_1_2, 5_1_2, 3)
    assert np.abs(expected_image - image ).max() < 1e-3


 def        __SCREAMING_SNAKE_CASE					(      self							:	str ):
    UpperCAmelCase__	:Optional[Any]      =      load_image(
        '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
        '''/sd2-upscale/low_res_cat.png''' )
    UpperCAmelCase__	:str      =      load_numpy(
        '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-upscale'''
        '''/upsampled_cat_fp16.npy''' )

    UpperCAmelCase__	:List[Any]      =      '''stabilityai/stable-diffusion-x4-upscaler'''
    UpperCAmelCase__	:Dict      =      StableDiffusionUpscalePipeline.from_pretrained(
        __lowerCamelCase			,       torch_dtype=torch.floataa			,       )
    pipe.to(__lowerCamelCase )
    pipe.set_progress_bar_config(disable=__lowerCamelCase )
    pipe.enable_attention_slicing()

    UpperCAmelCase__	:str      =      '''a cat sitting on a park bench'''

    UpperCAmelCase__	:int      =      torch.manual_seed(0 )
    UpperCAmelCase__	:Union[str, Any]      =      pipe(
        prompt=__lowerCamelCase			,       image=__lowerCamelCase			,       generator=__lowerCamelCase			,       output_type='''np'''			,       )
    UpperCAmelCase__	:Any      =      output.images[0]

    assert image.shape == (5_1_2, 5_1_2, 3)
    assert np.abs(expected_image - image ).max() < 5e-1





 def        __SCREAMING_SNAKE_CASE					(      self							:	Optional[Any] ):
    torch.cuda.empty_cache()
    torch.cuda.reset_max_memory_allocated()
    torch.cuda.reset_peak_memory_stats()

    UpperCAmelCase__	:List[Any]      =      load_image(
        '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
        '''/sd2-upscale/low_res_cat.png''' )

    UpperCAmelCase__	:Any      =      '''stabilityai/stable-diffusion-x4-upscaler'''
    UpperCAmelCase__	:List[str]      =      StableDiffusionUpscalePipeline.from_pretrained(
        __lowerCamelCase			,       torch_dtype=torch.floataa			,       )
    pipe.to(__lowerCamelCase )
    pipe.set_progress_bar_config(disable=__lowerCamelCase )
    pipe.enable_attention_slicing(1 )
    pipe.enable_sequential_cpu_offload()

    UpperCAmelCase__	:Any      =      '''a cat sitting on a park bench'''

    UpperCAmelCase__	:List[Any]      =      torch.manual_seed(0 )
    UpperCAmelCase__	:int      =      pipe(
        prompt=__lowerCamelCase			,       image=__lowerCamelCase			,       generator=__lowerCamelCase			,       num_inference_steps=5			,       output_type='''np'''			,       )

    UpperCAmelCase__	:Any      =      torch.cuda.max_memory_allocated()
    # make sure that less than 2.9 GB is allocated
    assert mem_bytes < 2.9 * 1_0**9







'''simple docstring'''





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 				UpperCAmelCase			(			unittest.TestCase						):
 UpperCAmelCase				=					ViTImageProcessor if is_vision_available() else None


 @property
 def        __SCREAMING_SNAKE_CASE					(      self							:	List[Any] ):
    return self.image_processor_tester.prepare_image_processor_dict()


 def        __SCREAMING_SNAKE_CASE					(      self							:	List[str] ):
    UpperCAmelCase__	:Any      =      (3, 3_2, 1_2_8)
    UpperCAmelCase__	:Optional[int]      =      tempfile.mkdtemp()

    # fmt: off
    UpperCAmelCase__	:Union[str, Any]      =      ['''[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__	:str      =      dict(zip(__lowerCamelCase			,       range(len(__lowerCamelCase ) ) ) )

    UpperCAmelCase__	:str      =      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(__lowerCamelCase ) + '''\n''' )

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


 def        __SCREAMING_SNAKE_CASE					(      self							:	Tuple			,       **__lowerCamelCase							:	Any ):
    return MgpstrTokenizer.from_pretrained(self.tmpdirname			,       **__lowerCamelCase )


 def        __SCREAMING_SNAKE_CASE					(      self							:	int			,       **__lowerCamelCase							:	str ):
    return ViTImageProcessor.from_pretrained(self.tmpdirname			,       **__lowerCamelCase )


 def        __SCREAMING_SNAKE_CASE					(      self							:	int ):
    shutil.rmtree(self.tmpdirname )


 def        __SCREAMING_SNAKE_CASE					(      self							:	Union[str, Any] ):
    UpperCAmelCase__	:Optional[int]      =      np.random.randint(2_5_5			,       size=(3, 3_0, 4_0_0)			,       dtype=np.uinta )

    UpperCAmelCase__	:Union[str, Any]      =      Image.fromarray(np.moveaxis(__lowerCamelCase			,       0			,       -1 ) )

    return image_input


 def        __SCREAMING_SNAKE_CASE					(      self							:	int ):
    UpperCAmelCase__	:Optional[Any]      =      self.get_tokenizer()
    UpperCAmelCase__	:str      =      self.get_image_processor()

    UpperCAmelCase__	:List[str]      =      MgpstrProcessor(tokenizer=__lowerCamelCase			,       image_processor=__lowerCamelCase )
    processor.save_pretrained(self.tmpdirname )
    UpperCAmelCase__	:Optional[int]      =      MgpstrProcessor.from_pretrained(self.tmpdirname			,       use_fast=__lowerCamelCase )

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

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


 def        __SCREAMING_SNAKE_CASE					(      self							:	Union[str, Any] ):
    UpperCAmelCase__	:Any      =      self.get_tokenizer()
    UpperCAmelCase__	:int      =      self.get_image_processor()

    UpperCAmelCase__	:Union[str, Any]      =      MgpstrProcessor(tokenizer=__lowerCamelCase			,       image_processor=__lowerCamelCase )
    processor.save_pretrained(self.tmpdirname )

    UpperCAmelCase__	:List[Any]      =      self.get_tokenizer(bos_token='''(BOS)'''			,       eos_token='''(EOS)''' )
    UpperCAmelCase__	:List[Any]      =      self.get_image_processor(do_normalize=__lowerCamelCase			,       padding_value=1.0 )

    UpperCAmelCase__	:Any      =      MgpstrProcessor.from_pretrained(
        self.tmpdirname			,       bos_token='''(BOS)'''			,       eos_token='''(EOS)'''			,       do_normalize=__lowerCamelCase			,       padding_value=1.0 )

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

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


 def        __SCREAMING_SNAKE_CASE					(      self							:	List[Any] ):
    UpperCAmelCase__	:Tuple      =      self.get_image_processor()
    UpperCAmelCase__	:Any      =      self.get_tokenizer()

    UpperCAmelCase__	:Any      =      MgpstrProcessor(tokenizer=__lowerCamelCase			,       image_processor=__lowerCamelCase )

    UpperCAmelCase__	:Optional[Any]      =      self.prepare_image_inputs()

    UpperCAmelCase__	:str      =      image_processor(__lowerCamelCase			,       return_tensors='''np''' )
    UpperCAmelCase__	:Tuple      =      processor(images=__lowerCamelCase			,       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        __SCREAMING_SNAKE_CASE					(      self							:	Optional[int] ):
    UpperCAmelCase__	:Optional[Any]      =      self.get_image_processor()
    UpperCAmelCase__	:List[Any]      =      self.get_tokenizer()

    UpperCAmelCase__	:List[str]      =      MgpstrProcessor(tokenizer=__lowerCamelCase			,       image_processor=__lowerCamelCase )

    UpperCAmelCase__	:Union[str, Any]      =      '''test'''

    UpperCAmelCase__	:Tuple      =      processor(text=__lowerCamelCase )

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


 def        __SCREAMING_SNAKE_CASE					(      self							:	str ):
    UpperCAmelCase__	:int      =      self.get_image_processor()
    UpperCAmelCase__	:Dict      =      self.get_tokenizer()

    UpperCAmelCase__	:int      =      MgpstrProcessor(tokenizer=__lowerCamelCase			,       image_processor=__lowerCamelCase )

    UpperCAmelCase__	:List[Any]      =      '''test'''
    UpperCAmelCase__	:List[Any]      =      self.prepare_image_inputs()

    UpperCAmelCase__	:Any      =      processor(text=__lowerCamelCase			,       images=__lowerCamelCase )

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

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


 def        __SCREAMING_SNAKE_CASE					(      self							:	List[Any] ):
    UpperCAmelCase__	:int      =      self.get_image_processor()
    UpperCAmelCase__	:Optional[int]      =      self.get_tokenizer()

    UpperCAmelCase__	:Union[str, Any]      =      MgpstrProcessor(tokenizer=__lowerCamelCase			,       image_processor=__lowerCamelCase )

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

    UpperCAmelCase__	:Optional[Any]      =      processor.char_decode(__lowerCamelCase )
    UpperCAmelCase__	:int      =      tokenizer.batch_decode(__lowerCamelCase )
    UpperCAmelCase__	:int      =      [seq.replace(''' '''			,       '''''' ) for seq in decoded_tok]

    self.assertListEqual(__lowerCamelCase			,       __lowerCamelCase )


 def        __SCREAMING_SNAKE_CASE					(      self							:	Union[str, Any] ):
    UpperCAmelCase__	:int      =      self.get_image_processor()
    UpperCAmelCase__	:Optional[int]      =      self.get_tokenizer()

    UpperCAmelCase__	:Dict      =      MgpstrProcessor(tokenizer=__lowerCamelCase			,       image_processor=__lowerCamelCase )

    UpperCAmelCase__	:List[str]      =      None
    UpperCAmelCase__	:Optional[int]      =      self.prepare_image_inputs()

    UpperCAmelCase__	:Tuple      =      processor(text=__lowerCamelCase			,       images=__lowerCamelCase )

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





 def        __SCREAMING_SNAKE_CASE					(      self							:	Tuple ):
    UpperCAmelCase__	:Dict      =      self.get_image_processor()
    UpperCAmelCase__	:int      =      self.get_tokenizer()

    UpperCAmelCase__	:Any      =      MgpstrProcessor(tokenizer=__lowerCamelCase			,       image_processor=__lowerCamelCase )

    UpperCAmelCase__	:Union[str, Any]      =      torch.randn(1			,       2_7			,       3_8 )
    UpperCAmelCase__	:Any      =      torch.randn(1			,       2_7			,       5_0_2_5_7 )
    UpperCAmelCase__	:Union[str, Any]      =      torch.randn(1			,       2_7			,       3_0_5_2_2 )

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

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



