
import json
import os
import unittest

from transformers.models.biogpt.tokenization_biogpt import VOCAB_FILES_NAMES, BioGptTokenizer
from transformers.testing_utils import slow

from ...test_tokenization_common import TokenizerTesterMixin


class 			lowercase_		(      A  ,      unittest.TestCase       ):
       __lowerCamelCase				       =						BioGptTokenizer
       __lowerCamelCase				       =						False


       def 	_snake_case				(    self				)						->    Any:
              super().setUp()

              # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
              SCREAMING_SNAKE_CASE_							:				int		=[
                  '''l''',
                  '''o''',
                  '''w''',
                  '''e''',
                  '''r''',
                  '''s''',
                  '''t''',
                  '''i''',
                  '''d''',
                  '''n''',
                  '''w</w>''',
                  '''r</w>''',
                  '''t</w>''',
                  '''lo''',
                  '''low''',
                  '''er</w>''',
                  '''low</w>''',
                  '''lowest</w>''',
                  '''newer</w>''',
                  '''wider</w>''',
                  '''<unk>''',
              ]
              SCREAMING_SNAKE_CASE_							:				List[str]		=dict(zip(__A						,       range(len(__A				)				)				)				)
              SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=['''l o 123''', '''lo w 1456''', '''e r</w> 1789''', '''''']

              SCREAMING_SNAKE_CASE_							:				Any		=os.path.join(self.tmpdirname						,       VOCAB_FILES_NAMES['''vocab_file''']				)
              SCREAMING_SNAKE_CASE_							:				List[str]		=os.path.join(self.tmpdirname						,       VOCAB_FILES_NAMES['''merges_file''']				)
              with open(self.vocab_file						,       '''w'''				) as fp:
                     fp.write(json.dumps(__A				)				)
              with open(self.merges_file						,       '''w'''				) as fp:
                     fp.write('''\n'''.join(__A				)				)


       def 	_snake_case				(    self						,       __A				)						->    Optional[int]:
              SCREAMING_SNAKE_CASE_							:				str		='''lower newer'''
              SCREAMING_SNAKE_CASE_							:				List[Any]		='''lower newer'''
              return input_text, output_text


       def 	_snake_case				(    self				)						->    List[str]:
              SCREAMING_SNAKE_CASE_							:				int		=BioGptTokenizer(self.vocab_file						,       self.merges_file				)

              SCREAMING_SNAKE_CASE_							:				List[str]		='''lower'''
              SCREAMING_SNAKE_CASE_							:				List[str]		=['''low''', '''er</w>''']
              SCREAMING_SNAKE_CASE_							:				Dict		=tokenizer.tokenize(__A				)
              self.assertListEqual(__A						,       __A				)

              SCREAMING_SNAKE_CASE_							:				int		=tokens + ['''<unk>''']
              SCREAMING_SNAKE_CASE_							:				int		=[14, 15, 20]
              self.assertListEqual(tokenizer.convert_tokens_to_ids(__A				)						,       __A				)





       @slow
       def 	_snake_case				(    self				)						->    int:
              SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=BioGptTokenizer.from_pretrained('''microsoft/biogpt'''				)

              SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=tokenizer.encode('''sequence builders'''						,       add_special_tokens=__A				)
              SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=tokenizer.encode('''multi-sequence build'''						,       add_special_tokens=__A				)

              SCREAMING_SNAKE_CASE_							:				Dict		=tokenizer.build_inputs_with_special_tokens(__A				)
              SCREAMING_SNAKE_CASE_							:				Optional[int]		=tokenizer.build_inputs_with_special_tokens(__A						,       __A				)

              self.assertTrue(encoded_sentence == [2] + text				)
              self.assertTrue(encoded_pair == [2] + text + [2] + text_a				)


from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import KarrasVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


class 			lowercase_		(      A       ):
       __lowerCamelCase				       =						42
       __lowerCamelCase				       =						42


       def __init__(    self						,       __A						,       __A				)						->    List[str]:
              super().__init__()
              self.register_modules(unet=__A						,       scheduler=__A				)





       @torch.no_grad()
       def __call__(    self						,       __A = 1						,       __A = 50						,       __A = None						,       __A = "pil"						,       __A = True						,       **__A						,       )						->    Union[Tuple, ImagePipelineOutput]:
              SCREAMING_SNAKE_CASE_							:				Dict		=self.unet.config.sample_size
              SCREAMING_SNAKE_CASE_							:				Tuple		=(batch_size, 3, img_size, img_size)

              SCREAMING_SNAKE_CASE_							:				int		=self.unet

              # sample x_0 ~ N(0, sigma_0^2 * I)
              SCREAMING_SNAKE_CASE_							:				Tuple		=randn_tensor(__A						,       generator=__A						,       device=self.device				) * self.scheduler.init_noise_sigma

              self.scheduler.set_timesteps(__A				)

              for t in self.progress_bar(self.scheduler.timesteps				):
                     # here sigma_t == t_i from the paper
                     SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=self.scheduler.schedule[t]
                     SCREAMING_SNAKE_CASE_							:				List[Any]		=self.scheduler.schedule[t - 1] if t > 0 else 0

                     # 1. Select temporarily increased noise level sigma_hat
                     # 2. Add new noise to move from sample_i to sample_hat
                     SCREAMING_SNAKE_CASE_	,     SCREAMING_SNAKE_CASE_							:				Dict		=self.scheduler.add_noise_to_input(__A						,       __A						,       generator=__A				)

                     # 3. Predict the noise residual given the noise magnitude `sigma_hat`
                     # The model inputs and output are adjusted by following eq. (213) in [1].
                     SCREAMING_SNAKE_CASE_							:				Tuple		=(sigma_hat / 2) * model((sample_hat + 1) / 2						,       sigma_hat / 2				).sample

                     # 4. Evaluate dx/dt at sigma_hat
                     # 5. Take Euler step from sigma to sigma_prev
                     SCREAMING_SNAKE_CASE_							:				List[Any]		=self.scheduler.step(__A						,       __A						,       __A						,       __A				)

                     if sigma_prev != 0:
                            # 6. Apply 2nd order correction
                            # The model inputs and output are adjusted by following eq. (213) in [1].
                            SCREAMING_SNAKE_CASE_							:				int		=(sigma_prev / 2) * model((step_output.prev_sample + 1) / 2						,       sigma_prev / 2				).sample
                            SCREAMING_SNAKE_CASE_							:				Dict		=self.scheduler.step_correct(
                                __A						,       __A						,       __A						,       __A						,       step_output.prev_sample						,       step_output['''derivative''']						,       )
                     SCREAMING_SNAKE_CASE_							:				Any		=step_output.prev_sample

              SCREAMING_SNAKE_CASE_							:				List[Any]		=(sample / 2 + 0.5).clamp(0						,       1				)
              SCREAMING_SNAKE_CASE_							:				Any		=sample.cpu().permute(0						,       2						,       3						,       1				).numpy()
              if output_type == "pil":
                     SCREAMING_SNAKE_CASE_							:				Optional[Any]		=self.numpy_to_pil(__A				)

              if not return_dict:
                     return (image,)

              return ImagePipelineOutput(images=__A				)
