
import os
import sys
import unittest


_lowercase											=    os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, """utils"""))

import check_dummies  # noqa: E402
from check_dummies import create_dummy_files, create_dummy_object, find_backend, read_init  # noqa: E402


# Align TRANSFORMERS_PATH in check_dummies with the current path
_lowercase											=    os.path.join(git_repo_path, """src""", """transformers""")

_lowercase											=    """
{0} = None
"""

_lowercase											=    """
class {0}(metaclass=DummyObject):
    _backends = {1}

    def __init__(self, *args, **kwargs):
        requires_backends(self, {1})
"""


_lowercase											=    """
def {0}(*args, **kwargs):
    requires_backends({0}, {1})
"""


class 			lowercase_		(      unittest.TestCase       ):


       def 	_snake_case				(    self				)						->    List[str]:
              SCREAMING_SNAKE_CASE_							:				Dict		=find_backend('''    _import_structure["models.albert"].append("AlbertTokenizerFast")'''				)
              self.assertIsNone(__A				)

              SCREAMING_SNAKE_CASE_							:				str		=find_backend('''    if not is_tokenizers_available():'''				)
              self.assertEqual(__A						,       '''tokenizers'''				)

              SCREAMING_SNAKE_CASE_							:				str		=find_backend('''    if not is_tensorflow_text_available():'''				)
              self.assertEqual(__A						,       '''tensorflow_text'''				)

              SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=find_backend('''    if not (is_sentencepiece_available() and is_tokenizers_available()):'''				)
              self.assertEqual(__A						,       '''sentencepiece_and_tokenizers'''				)

              SCREAMING_SNAKE_CASE_							:				Optional[Any]		=find_backend(
                  '''    if not (is_sentencepiece_available() and is_tensorflow_text_available()):'''				)
              self.assertEqual(__A						,       '''sentencepiece_and_tensorflow_text'''				)

              SCREAMING_SNAKE_CASE_							:				Optional[Any]		=find_backend(
                  '''    if not (is_sentencepiece_available() and is_tokenizers_available() and is_vision_available()):'''				)
              self.assertEqual(__A						,       '''sentencepiece_and_tokenizers_and_vision'''				)


       def 	_snake_case				(    self				)						->    str:
              SCREAMING_SNAKE_CASE_							:				Tuple		=read_init()
              # We don't assert on the exact list of keys to allow for smooth grow of backend-specific objects
              self.assertIn('''torch'''						,       __A				)
              self.assertIn('''tensorflow_text'''						,       __A				)
              self.assertIn('''sentencepiece_and_tokenizers'''						,       __A				)

              # Likewise, we can't assert on the exact content of a key
              self.assertIn('''BertModel'''						,       objects['''torch''']				)
              self.assertIn('''TFBertModel'''						,       objects['''tf''']				)
              self.assertIn('''FlaxBertModel'''						,       objects['''flax''']				)
              self.assertIn('''BertModel'''						,       objects['''torch''']				)
              self.assertIn('''TFBertTokenizer'''						,       objects['''tensorflow_text''']				)
              self.assertIn('''convert_slow_tokenizer'''						,       objects['''sentencepiece_and_tokenizers''']				)


       def 	_snake_case				(    self				)						->    List[Any]:
              SCREAMING_SNAKE_CASE_							:				List[Any]		=create_dummy_object('''CONSTANT'''						,       '''\'torch\''''				)
              self.assertEqual(__A						,       '''\nCONSTANT = None\n'''				)

              SCREAMING_SNAKE_CASE_							:				List[Any]		=create_dummy_object('''function'''						,       '''\'torch\''''				)
              self.assertEqual(
                  __A						,       '''\ndef function(*args, **kwargs):\n    requires_backends(function, \'torch\')\n'''				)

              SCREAMING_SNAKE_CASE_							:				Optional[Any]		='''
class FakeClass(metaclass=DummyObject):
    _backends = \'torch\'

    def __init__(self, *args, **kwargs):
        requires_backends(self, \'torch\')
'''
              SCREAMING_SNAKE_CASE_							:				int		=create_dummy_object('''FakeClass'''						,       '''\'torch\''''				)
              self.assertEqual(__A						,       __A				)





       def 	_snake_case				(    self				)						->    int:
              SCREAMING_SNAKE_CASE_							:				Optional[Any]		='''# This file is autogenerated by the command `make fix-copies`, do not edit.
from ..utils import DummyObject, requires_backends


CONSTANT = None


def function(*args, **kwargs):
    requires_backends(function, ["torch"])


class FakeClass(metaclass=DummyObject):
    _backends = ["torch"]

    def __init__(self, *args, **kwargs):
        requires_backends(self, ["torch"])
'''
              SCREAMING_SNAKE_CASE_							:				List[Any]		=create_dummy_files({'''torch''': ['''CONSTANT''', '''function''', '''FakeClass''']}				)
              self.assertEqual(dummy_files['''torch''']						,       __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				)
