



'''simple docstring'''





import unittest

import numpy as np
import torch

from diffusers import PNDMPipeline, PNDMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()


class 					snake_case			(unittest.TestCase ):



       @property
       def 				_a   (					self   )	->				Any:
           torch.manual_seed(0   )
           lowercase__									=    UNetaDModel(
               block_out_channels=(32, 64)  ,layers_per_block=2  ,sample_size=32  ,in_channels=3  ,out_channels=3  ,down_block_types=("DownBlock2D", "AttnDownBlock2D")  ,up_block_types=("AttnUpBlock2D", "UpBlock2D")  ,)
           return model







       def 				_a   (					self   )	->				Optional[Any]:
           lowercase__									=    self.dummy_uncond_unet
           lowercase__									=    PNDMScheduler()

           lowercase__									=    PNDMPipeline(unet=UpperCAmelCase_  ,scheduler=UpperCAmelCase_   )
           pndm.to(UpperCAmelCase_   )
           pndm.set_progress_bar_config(disable=UpperCAmelCase_   )

           lowercase__									=    torch.manual_seed(0   )
           lowercase__									=    pndm(generator=UpperCAmelCase_  ,num_inference_steps=20  ,output_type="numpy"   ).images

           lowercase__									=    torch.manual_seed(0   )
           lowercase__									=    pndm(generator=UpperCAmelCase_  ,num_inference_steps=20  ,output_type="numpy"  ,return_dict=UpperCAmelCase_   )[0]

           lowercase__									=    image[0, -3:, -3:, -1]
           lowercase__									=    image_from_tuple[0, -3:, -3:, -1]

           assert image.shape == (1, 32, 32, 3)
           lowercase__									=    np.array([1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0]   )

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





@slow
@require_torch
class 					snake_case			(unittest.TestCase ):



       def 				_a   (					self   )	->				Optional[int]:
           lowercase__									=    "google/ddpm-cifar10-32"

           lowercase__									=    UNetaDModel.from_pretrained(UpperCAmelCase_   )
           lowercase__									=    PNDMScheduler()

           lowercase__									=    PNDMPipeline(unet=UpperCAmelCase_  ,scheduler=UpperCAmelCase_   )
           pndm.to(UpperCAmelCase_   )
           pndm.set_progress_bar_config(disable=UpperCAmelCase_   )
           lowercase__									=    torch.manual_seed(0   )
           lowercase__									=    pndm(generator=UpperCAmelCase_  ,output_type="numpy"   ).images

           lowercase__									=    image[0, -3:, -3:, -1]

           assert image.shape == (1, 32, 32, 3)
           lowercase__									=    np.array([0.15_64, 0.1_46_45, 0.14_06, 0.1_47_15, 0.1_24_25, 0.1_40_45, 0.1_31_15, 0.1_21_75, 0.1_25]   )

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









'''simple docstring'''





import json
import os
import unittest

from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin


@require_tokenizers
class 					snake_case			(UpperCamelCase		,   unittest.TestCase ):
       lowerCAmelCase__	:str     =       MgpstrTokenizer
       lowerCAmelCase__	:Tuple     =       False
       lowerCAmelCase__	:Any     =       {}
       lowerCAmelCase__	:List[Any]     =       False



       def 				_a   (					self   )	->				Union[str, Any]:
           super().setUp()

           # fmt: off
           lowercase__									=    ["[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
           lowercase__									=    dict(zip(UpperCAmelCase_  ,range(len(UpperCAmelCase_   )   )   )   )

           lowercase__									=    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(UpperCAmelCase_   ) + "\n"   )



       def 				_a   (					self  ,**UpperCAmelCase_   )	->				Any:
           return MgpstrTokenizer.from_pretrained(self.tmpdirname  ,**UpperCAmelCase_   )



       def 				_a   (					self  ,UpperCAmelCase_   )	->				Dict:
           lowercase__									=    "tester"
           lowercase__									=    "tester"
           return input_text, output_text



       @unittest.skip("MGP-STR always lower cases letters."   )
       def 				_a   (					self   )	->				Dict:
           pass



       def 				_a   (					self   )	->				Optional[Any]:
           lowercase__									=    self.get_tokenizers(do_lower_case=UpperCAmelCase_   )
           for tokenizer in tokenizers:
               with self.subTest(F'''{tokenizer.__class__.__name__}'''   ):
                   lowercase__									=    "[SPECIAL_TOKEN]"

                   tokenizer.add_special_tokens({"cls_token": special_token}   )
                   lowercase__									=    tokenizer.encode([special_token]  ,add_special_tokens=UpperCAmelCase_   )
                   self.assertEqual(len(UpperCAmelCase_   )  ,1   )

                   lowercase__									=    tokenizer.decode(UpperCAmelCase_  ,skip_special_tokens=UpperCAmelCase_   )
                   self.assertTrue(special_token not in decoded   )



       def 				_a   (					self   )	->				str:
           lowercase__									=    self.get_tokenizers()
           for tokenizer in tokenizers:
               with self.subTest(F'''{tokenizer.__class__.__name__}'''   ):
                   lowercase__							,							lowercase__									=    self.get_input_output_texts(UpperCAmelCase_   )

                   lowercase__									=    tokenizer.tokenize(UpperCAmelCase_   )
                   lowercase__									=    tokenizer.convert_tokens_to_ids(UpperCAmelCase_   )
                   lowercase__									=    tokenizer.encode(UpperCAmelCase_  ,add_special_tokens=UpperCAmelCase_   )
                   self.assertListEqual(UpperCAmelCase_  ,UpperCAmelCase_   )

                   lowercase__									=    tokenizer.convert_ids_to_tokens(UpperCAmelCase_   )
                   self.assertNotEqual(len(UpperCAmelCase_   )  ,0   )
                   lowercase__									=    tokenizer.decode(UpperCAmelCase_   )
                   self.assertIsInstance(UpperCAmelCase_  ,UpperCAmelCase_   )

                   self.assertEqual(text_a.replace(" "  ,""   )  ,UpperCAmelCase_   )



       @unittest.skip("MGP-STR tokenizer only handles one sequence."   )
       def 				_a   (					self   )	->				Optional[int]:
           pass







       @unittest.skip("inputs cannot be pretokenized in MgpstrTokenizer"   )
       def 				_a   (					self   )	->				Any:
           pass




