'''simple docstring'''




import tempfile
import unittest

import torch

from diffusers import UNetaDConditionModel
from diffusers.training_utils import EMAModel
from diffusers.utils.testing_utils import enable_full_determinism, skip_mps, torch_device


enable_full_determinism()

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

      __UpperCamelCase:  Optional[Any]      =						"hf-internal-testing/tiny-stable-diffusion-pipe"
      __UpperCamelCase:  Dict      =						1
      __UpperCamelCase:  Optional[Any]      =						7_7
      __UpperCamelCase:  int      =						3_2
      __UpperCamelCase:  List[str]      =						4
      __UpperCamelCase:  Any      =						6_4
      __UpperCamelCase:  List[str]      =						torch.manual_seed(0   )




      def 					_A     (	self						:    str						, A						:    Tuple=0.9_999	):
             _UpperCAmelCase					:  List[Any]											=   UNetaDConditionModel.from_pretrained(self.model_id						, subfolder="unet"	)
             _UpperCAmelCase					:  Optional[Any]											=   unet.to(A	)
             _UpperCAmelCase					:  Tuple											=   EMAModel(unet.parameters()						, decay=A						, model_cls=A						, model_config=unet.config	)
             return unet, ema_unet




      def 					_A     (	self						:    str	):
             _UpperCAmelCase					:  int											=   torch.randn(
                 self.batch_size						, self.num_in_channels						, self.latent_height						, self.latent_width						, generator=self.generator	).to(A	)
             _UpperCAmelCase					:  Optional[int]											=   torch.randint(0						, 1000						, size=(self.batch_size,)						, generator=self.generator	).to(A	)
             _UpperCAmelCase					:  List[str]											=   torch.randn(
                 self.batch_size						, self.prompt_length						, self.text_encoder_hidden_dim						, generator=self.generator	).to(A	)
             return noisy_latents, timesteps, encoder_hidden_states




      def 					_A     (	self						:    List[Any]						, A						:    Any	):
             _UpperCAmelCase					:  str											=   {}
             for k, param in unet.state_dict().items():
                    _UpperCAmelCase					:  List[str]											=   torch.randn_like(A	) + (param * torch.randn_like(A	))
                    updated_state_dict.update({k: updated_param}	)
             unet.load_state_dict(A	)
             return unet




      def 					_A     (	self						:    Any	):
             _UpperCAmelCase				,  _UpperCAmelCase					:  str											=   self.get_models()
             # Take the first (hypothetical) EMA step.
             ema_unet.step(unet.parameters()	)
             assert ema_unet.optimization_step == 1

             # Take two more.
             for _ in range(2	):
                    ema_unet.step(unet.parameters()	)
             assert ema_unet.optimization_step == 3




      def 					_A     (	self						:    Union[str, Any]	):
             _UpperCAmelCase				,  _UpperCAmelCase					:  Union[str, Any]											=   self.get_models()
             # Since the `unet` is not being updated (i.e., backprop'd)
             # there won't be any difference between the `params` of `unet`
             # and `ema_unet` even if we call `ema_unet.step(unet.parameters())`.
             ema_unet.step(unet.parameters()	)
             _UpperCAmelCase					:  List[str]											=   list(unet.parameters()	)
             for s_param, param in zip(ema_unet.shadow_params						, A	):
                    assert torch.allclose(A						, A	)

             # The above holds true even if we call `ema.step()` multiple times since
             # `unet` params are still not being updated.
             for _ in range(4	):
                    ema_unet.step(unet.parameters()	)
             for s_param, param in zip(ema_unet.shadow_params						, A	):
                    assert torch.allclose(A						, A	)




      def 					_A     (	self						:    List[Any]	):
             _UpperCAmelCase				,  _UpperCAmelCase					:  str											=   self.get_models()
             # Here we simulate the parameter updates for `unet`. Since there might
             # be some parameters which are initialized to zero we take extra care to
             # initialize their values to something non-zero before the multiplication.
             _UpperCAmelCase					:  Tuple											=   self.simulate_backprop(A	)

             # Take the EMA step.
             ema_unet.step(unet_pseudo_updated_step_one.parameters()	)

             # Now the EMA'd parameters won't be equal to the original model parameters.
             _UpperCAmelCase					:  Dict											=   list(unet_pseudo_updated_step_one.parameters()	)
             for s_param, param in zip(ema_unet.shadow_params						, A	):
                    assert ~torch.allclose(A						, A	)

             # Ensure this is the case when we take multiple EMA steps.
             for _ in range(4	):
                    ema_unet.step(unet.parameters()	)
             for s_param, param in zip(ema_unet.shadow_params						, A	):
                    assert ~torch.allclose(A						, A	)




      def 					_A     (	self						:    List[Any]	):
             # If we call EMA step after a backpropagation consecutively for two times,
             # the shadow params from those two steps should be different.
             _UpperCAmelCase				,  _UpperCAmelCase					:  Dict											=   self.get_models()

             # First backprop + EMA
             _UpperCAmelCase					:  List[Any]											=   self.simulate_backprop(A	)
             ema_unet.step(unet_step_one.parameters()	)
             _UpperCAmelCase					:  List[str]											=   ema_unet.shadow_params

             # Second backprop + EMA
             _UpperCAmelCase					:  Union[str, Any]											=   self.simulate_backprop(A	)
             ema_unet.step(unet_step_two.parameters()	)
             _UpperCAmelCase					:  Tuple											=   ema_unet.shadow_params

             for step_one, step_two in zip(A						, A	):
                    assert ~torch.allclose(A						, A	)




      def 					_A     (	self						:    Optional[Any]	):
             # If there's no decay even if there are backprops, EMA steps
             # won't take any effect i.e., the shadow params would remain the
             # same.
             _UpperCAmelCase				,  _UpperCAmelCase					:  Union[str, Any]											=   self.get_models(decay=0.0	)
             _UpperCAmelCase					:  str											=   self.simulate_backprop(A	)
             ema_unet.step(unet_step_one.parameters()	)
             _UpperCAmelCase					:  str											=   ema_unet.shadow_params

             _UpperCAmelCase					:  Any											=   self.simulate_backprop(A	)
             ema_unet.step(unet_step_two.parameters()	)
             _UpperCAmelCase					:  Optional[int]											=   ema_unet.shadow_params

             for step_one, step_two in zip(A						, A	):
                    assert torch.allclose(A						, A	)





      @skip_mps
      def 					_A     (	self						:    Optional[Any]	):
             _UpperCAmelCase				,  _UpperCAmelCase					:  Tuple											=   self.get_models()
             _UpperCAmelCase				,  _UpperCAmelCase				,  _UpperCAmelCase					:  Optional[Any]											=   self.get_dummy_inputs()

             with tempfile.TemporaryDirectory() as tmpdir:
                    ema_unet.save_pretrained(A	)
                    _UpperCAmelCase					:  Optional[int]											=   UNetaDConditionModel.from_pretrained(A						, model_cls=A	)
                    _UpperCAmelCase					:  Any											=   loaded_unet.to(unet.device	)

             # Since no EMA step has been performed the outputs should match.
             _UpperCAmelCase					:  Any											=   unet(A						, A						, A	).sample
             _UpperCAmelCase					:  Any											=   loaded_unet(A						, A						, A	).sample

             assert torch.allclose(A						, A						, atol=1E-4	)


'''simple docstring'''




import sys
from collections.abc import Mapping

import numpy as np
import pyarrow as pa

from .. import config
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter

class      lowerCamelCase_     (TensorFormatter[Mapping, np.ndarray, Mapping]   ):
      '''simple docstring'''

      def __init__(	self						:    str						, A						:    List[str]=None						, **A						:    Optional[Any]	):
             super().__init__(features=A	)
             _UpperCAmelCase					:  Tuple											=   np_array_kwargs




      def 					_A     (	self						:    str						, A						:    Tuple	):
             if isinstance(A						, A	):
                    if column and all(
                        isinstance(A						, np.ndarray	) and x.shape == column[0].shape and x.dtype == column[0].dtype for x in column	):
                           return np.stack(A	)
                    else:
                           # don't use np.array(column, dtype=object)
                           # since it fails in certain cases
                           # see https://stackoverflow.com/q/51005699
                           _UpperCAmelCase					:  List[str]											=   np.empty(len(A	)						, dtype=A	)
                           _UpperCAmelCase					:  int											=   column
                           return out
             return column




      def 					_A     (	self						:    Any						, A						:    Optional[Any]	):
             if isinstance(A						, (str, bytes, type(A	))	):
                    return value
             elif isinstance(A						, (np.character, np.ndarray)	) and np.issubdtype(value.dtype						, np.character	):
                    return value
             elif isinstance(A						, np.number	):
                    return value

             _UpperCAmelCase					:  List[str]											=   {}

             if isinstance(A						, np.ndarray	) and np.issubdtype(value.dtype						, np.integer	):
                    _UpperCAmelCase					:  str											=   {"dtype": np.intaa}
             elif isinstance(A						, np.ndarray	) and np.issubdtype(value.dtype						, np.floating	):
                    _UpperCAmelCase					:  int											=   {"dtype": np.floataa}
             elif config.PIL_AVAILABLE and "PIL" in sys.modules:
                    import PIL.Image

                    if isinstance(A						, PIL.Image.Image	):
                           return np.asarray(A						, **self.np_array_kwargs	)

             return np.asarray(A						, **{**default_dtype, **self.np_array_kwargs}	)




      def 					_A     (	self						:    Any						, A						:    Optional[Any]	):
             # support for torch, tf, jax etc.
             if config.TORCH_AVAILABLE and "torch" in sys.modules:
                    import torch

                    if isinstance(A						, torch.Tensor	):
                           return self._tensorize(data_struct.detach().cpu().numpy()[()]	)
             if hasattr(A						, "__array__"	) and not isinstance(A						, (np.ndarray, np.character, np.number)	):
                    _UpperCAmelCase					:  Dict											=   data_struct.__array__()
             # support for nested types like struct of list of struct
             if isinstance(A						, np.ndarray	):
                    if data_struct.dtype == object:
                           return self._consolidate([self.recursive_tensorize(A	) for substruct in data_struct]	)
             if isinstance(A						, (list, tuple)	):
                    return self._consolidate([self.recursive_tensorize(A	) for substruct in data_struct]	)
             return self._tensorize(A	)




      def 					_A     (	self						:    List[Any]						, A						:    dict	):
             return map_nested(self._recursive_tensorize						, A						, map_list=A	)




      def 					_A     (	self						:    List[str]						, A						:    pa.Table	):
             _UpperCAmelCase					:  str											=   self.numpy_arrow_extractor().extract_row(A	)
             _UpperCAmelCase					:  Dict											=   self.python_features_decoder.decode_row(A	)
             return self.recursive_tensorize(A	)




      def 					_A     (	self						:    str						, A						:    pa.Table	):
             _UpperCAmelCase					:  List[str]											=   self.numpy_arrow_extractor().extract_column(A	)
             _UpperCAmelCase					:  Optional[Any]											=   self.python_features_decoder.decode_column(A						, pa_table.column_names[0]	)
             _UpperCAmelCase					:  Tuple											=   self.recursive_tensorize(A	)
             _UpperCAmelCase					:  Tuple											=   self._consolidate(A	)
             return column





      def 					_A     (	self						:    Union[str, Any]						, A						:    pa.Table	):
             _UpperCAmelCase					:  Any											=   self.numpy_arrow_extractor().extract_batch(A	)
             _UpperCAmelCase					:  Any											=   self.python_features_decoder.decode_batch(A	)
             _UpperCAmelCase					:  List[str]											=   self.recursive_tensorize(A	)
             for column_name in batch:
                    _UpperCAmelCase					:  Optional[Any]											=   self._consolidate(batch[column_name]	)
             return batch

