






import unittest

import torch

from diffusers import DDIMScheduler, DDPMScheduler, UNetaDModel
from diffusers.training_utils import set_seed
from diffusers.utils.testing_utils import slow


snake_case     					=		False


class 			SCREAMING_SNAKE_CASE      (      unittest.TestCase       ):
     '''simple docstring'''


     def 					_A							(					self							:						Optional[int]       ,			UpperCAmelCase_							:						Any=32   ):
       set_seed(0   )
       SCREAMING_SNAKE_CASE							:   Optional[int]						    =     UNetaDModel(sample_size=UpperCAmelCase_       ,			in_channels=3       ,			out_channels=3   )
       SCREAMING_SNAKE_CASE							:   Tuple						    =     torch.optim.SGD(model.parameters()       ,			lr=0.0_001   )
       return model, optimizer






     @slow
     def 					_A							(					self							:						Tuple   ):
       SCREAMING_SNAKE_CASE							:   str						    =     "cpu"  # ensure full determinism without setting the CUBLAS_WORKSPACE_CONFIG env variable
       SCREAMING_SNAKE_CASE							:   Union[str, Any]						    =     DDPMScheduler(
           num_train_timesteps=1000       ,			beta_start=0.0_001       ,			beta_end=0.02       ,			beta_schedule="linear"       ,			clip_sample=UpperCAmelCase_       ,			)
       SCREAMING_SNAKE_CASE							:   List[str]						    =     DDIMScheduler(
           num_train_timesteps=1000       ,			beta_start=0.0_001       ,			beta_end=0.02       ,			beta_schedule="linear"       ,			clip_sample=UpperCAmelCase_       ,			)

       assert ddpm_scheduler.config.num_train_timesteps == ddim_scheduler.config.num_train_timesteps

       # shared batches for DDPM and DDIM
       set_seed(0   )
       SCREAMING_SNAKE_CASE							:   List[Any]						    =     [torch.randn((4, 3, 32, 32)   ).clip(-1       ,			1   ).to(UpperCAmelCase_   ) for _ in range(4   )]
       SCREAMING_SNAKE_CASE							:   Union[str, Any]						    =     [torch.randn((4, 3, 32, 32)   ).to(UpperCAmelCase_   ) for _ in range(4   )]
       SCREAMING_SNAKE_CASE							:   Any						    =     [torch.randint(0       ,			1000       ,			(4,)   ).long().to(UpperCAmelCase_   ) for _ in range(4   )]

       # train with a DDPM scheduler
       SCREAMING_SNAKE_CASE    ,	SCREAMING_SNAKE_CASE							:   Dict						    =     self.get_model_optimizer(resolution=32   )
       model.train().to(UpperCAmelCase_   )
       for i in range(4   ):
         optimizer.zero_grad()
         SCREAMING_SNAKE_CASE							:   Union[str, Any]						    =     ddpm_scheduler.add_noise(clean_images[i]       ,			noise[i]       ,			timesteps[i]   )
         SCREAMING_SNAKE_CASE							:   str						    =     model(UpperCAmelCase_       ,			timesteps[i]   ).sample
         SCREAMING_SNAKE_CASE							:   str						    =     torch.nn.functional.mse_loss(UpperCAmelCase_       ,			noise[i]   )
         loss.backward()
         optimizer.step()
       del model, optimizer

       # recreate the model and optimizer, and retry with DDIM
       SCREAMING_SNAKE_CASE    ,	SCREAMING_SNAKE_CASE							:   str						    =     self.get_model_optimizer(resolution=32   )
       model.train().to(UpperCAmelCase_   )
       for i in range(4   ):
         optimizer.zero_grad()
         SCREAMING_SNAKE_CASE							:   Optional[int]						    =     ddim_scheduler.add_noise(clean_images[i]       ,			noise[i]       ,			timesteps[i]   )
         SCREAMING_SNAKE_CASE							:   List[Any]						    =     model(UpperCAmelCase_       ,			timesteps[i]   ).sample
         SCREAMING_SNAKE_CASE							:   int						    =     torch.nn.functional.mse_loss(UpperCAmelCase_       ,			noise[i]   )
         loss.backward()
         optimizer.step()
       del model, optimizer

       self.assertTrue(torch.allclose(UpperCAmelCase_       ,			UpperCAmelCase_       ,			atol=1E-5   )   )
       self.assertTrue(torch.allclose(UpperCAmelCase_       ,			UpperCAmelCase_       ,			atol=1E-5   )   )










'''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'''] )



