

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     lowercase						(       unittest.TestCase					):



 '''simple docstring'''


 __SCREAMING_SNAKE_CASE												=				"""hf-internal-testing/tiny-stable-diffusion-pipe"""
 __SCREAMING_SNAKE_CASE												=				1
 __SCREAMING_SNAKE_CASE												=				77
 __SCREAMING_SNAKE_CASE												=				32
 __SCREAMING_SNAKE_CASE												=				4
 __SCREAMING_SNAKE_CASE												=				__SCREAMING_SNAKE_CASE												=				64
 __SCREAMING_SNAKE_CASE												=				torch.manual_seed(0					)




 def   UpperCamelCase__							(self       ,       __a=0.99_99 ) ->					List[Any]:






  """simple docstring"""





  UpperCAmelCase__							=				UNetaDConditionModel.from_pretrained(self.model_id       ,       subfolder='unet' )
  UpperCAmelCase__							=				unet.to(_UpperCAmelCase )
  UpperCAmelCase__							=				EMAModel(unet.parameters()       ,       decay=_UpperCAmelCase       ,       model_cls=_UpperCAmelCase       ,       model_config=unet.config )
  return unet, ema_unet




 def   UpperCamelCase__							(self ) ->					Optional[int]:






  """simple docstring"""





  UpperCAmelCase__							=				torch.randn(
      self.batch_size       ,       self.num_in_channels       ,       self.latent_height       ,       self.latent_width       ,       generator=self.generator ).to(_UpperCAmelCase )
  UpperCAmelCase__							=				torch.randint(0       ,       1000       ,       size=(self.batch_size,)       ,       generator=self.generator ).to(_UpperCAmelCase )
  UpperCAmelCase__							=				torch.randn(
      self.batch_size       ,       self.prompt_length       ,       self.text_encoder_hidden_dim       ,       generator=self.generator ).to(_UpperCAmelCase )
  return noisy_latents, timesteps, encoder_hidden_states




 def   UpperCamelCase__							(self       ,       __a ) ->					Dict:






  """simple docstring"""





  UpperCAmelCase__							=				{}
  for k, param in unet.state_dict().items():
   UpperCAmelCase__							=				torch.randn_like(_UpperCAmelCase ) + (param * torch.randn_like(_UpperCAmelCase ))
   updated_state_dict.update({k: updated_param} )
  unet.load_state_dict(_UpperCAmelCase )
  return unet




 def   UpperCamelCase__							(self ) ->					Tuple:






  """simple docstring"""





  UpperCAmelCase__							=				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   UpperCamelCase__							(self ) ->					str:






  """simple docstring"""





  UpperCAmelCase__							=				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(unet.parameters() )
  for s_param, param in zip(ema_unet.shadow_params       ,       _UpperCAmelCase ):
   assert torch.allclose(_UpperCAmelCase       ,       _UpperCAmelCase )

  # 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       ,       _UpperCAmelCase ):
   assert torch.allclose(_UpperCAmelCase       ,       _UpperCAmelCase )




 def   UpperCamelCase__							(self ) ->					Union[str, Any]:






  """simple docstring"""





  UpperCAmelCase__							=				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__							=				self.simulate_backprop(_UpperCAmelCase )

  # 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__							=				list(unet_pseudo_updated_step_one.parameters() )
  for s_param, param in zip(ema_unet.shadow_params       ,       _UpperCAmelCase ):
   assert ~torch.allclose(_UpperCAmelCase       ,       _UpperCAmelCase )

  # 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       ,       _UpperCAmelCase ):
   assert ~torch.allclose(_UpperCAmelCase       ,       _UpperCAmelCase )




 def   UpperCamelCase__							(self ) ->					Tuple:






  """simple docstring"""





  UpperCAmelCase__							=				self.get_models()

  # First backprop + EMA
  UpperCAmelCase__							=				self.simulate_backprop(_UpperCAmelCase )
  ema_unet.step(unet_step_one.parameters() )
  UpperCAmelCase__							=				ema_unet.shadow_params

  # Second backprop + EMA
  UpperCAmelCase__							=				self.simulate_backprop(_UpperCAmelCase )
  ema_unet.step(unet_step_two.parameters() )
  UpperCAmelCase__							=				ema_unet.shadow_params

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




 def   UpperCamelCase__							(self ) ->					str:






  """simple docstring"""





  UpperCAmelCase__							=				self.get_models(decay=0.0 )
  UpperCAmelCase__							=				self.simulate_backprop(_UpperCAmelCase )
  ema_unet.step(unet_step_one.parameters() )
  UpperCAmelCase__							=				ema_unet.shadow_params

  UpperCAmelCase__							=				self.simulate_backprop(_UpperCAmelCase )
  ema_unet.step(unet_step_two.parameters() )
  UpperCAmelCase__							=				ema_unet.shadow_params

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




 @skip_mps
 def   UpperCamelCase__							(self ) ->					str:






  """simple docstring"""





  UpperCAmelCase__							=				self.get_models()
  UpperCAmelCase__							=				self.get_dummy_inputs()

  with tempfile.TemporaryDirectory() as tmpdir:
   ema_unet.save_pretrained(_UpperCAmelCase )
   UpperCAmelCase__							=				UNetaDConditionModel.from_pretrained(_UpperCAmelCase       ,       model_cls=_UpperCAmelCase )
   UpperCAmelCase__							=				loaded_unet.to(unet.device )

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

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









from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


_lowerCamelCase					:     Optional[int]					  =  logging.get_logger(__name__)

_lowerCamelCase					:     Optional[int]					  =  {
    "squeezebert/squeezebert-uncased": (
        "https://huggingface.co/squeezebert/squeezebert-uncased/resolve/main/config.json"
    ),
    "squeezebert/squeezebert-mnli": "https://huggingface.co/squeezebert/squeezebert-mnli/resolve/main/config.json",
    "squeezebert/squeezebert-mnli-headless": (
        "https://huggingface.co/squeezebert/squeezebert-mnli-headless/resolve/main/config.json"
    ),
}
class 				__snake_case     (_a						):
				lowerCAmelCase__			=   SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP
				lowerCAmelCase__			=   "squeezebert"



				def __init__(					self					:						Optional[int]						,		_UpperCAmelCase					:						Tuple=3_0522						,		_UpperCAmelCase					:						str=768						,		_UpperCAmelCase					:						int=12						,		_UpperCAmelCase					:						Tuple=12						,		_UpperCAmelCase					:						Tuple=3072						,		_UpperCAmelCase					:						Union[str, Any]="gelu"						,		_UpperCAmelCase					:						Union[str, Any]=0.1						,		_UpperCAmelCase					:						List[Any]=0.1						,		_UpperCAmelCase					:						List[Any]=512						,		_UpperCAmelCase					:						Tuple=2						,		_UpperCAmelCase					:						Optional[Any]=0.02						,		_UpperCAmelCase					:						Any=1E-12						,		_UpperCAmelCase					:						Optional[Any]=0						,		_UpperCAmelCase					:						Union[str, Any]=768						,		_UpperCAmelCase					:						Any=4						,		_UpperCAmelCase					:						Any=4						,		_UpperCAmelCase					:						Optional[int]=4						,		_UpperCAmelCase					:						str=1						,		_UpperCAmelCase					:						Union[str, Any]=4						,		_UpperCAmelCase					:						List[str]=4						,		**_UpperCAmelCase					:						int						,		)     ->			Any:





								'''simple docstring'''




								super().__init__(pad_token_id=_UpperCAmelCase						,		**_UpperCAmelCase					)

								_lowerCAmelCase			:			Optional[Any]        =					vocab_size
								_lowerCAmelCase			:			Optional[Any]        =					hidden_size
								_lowerCAmelCase			:			Tuple        =					num_hidden_layers
								_lowerCAmelCase			:			int        =					num_attention_heads
								_lowerCAmelCase			:			List[Any]        =					hidden_act
								_lowerCAmelCase			:			List[Any]        =					intermediate_size
								_lowerCAmelCase			:			List[str]        =					hidden_dropout_prob
								_lowerCAmelCase			:			Any        =					attention_probs_dropout_prob
								_lowerCAmelCase			:			str        =					max_position_embeddings
								_lowerCAmelCase			:			int        =					type_vocab_size
								_lowerCAmelCase			:			str        =					initializer_range
								_lowerCAmelCase			:			int        =					layer_norm_eps
								_lowerCAmelCase			:			int        =					embedding_size
								_lowerCAmelCase			:			Optional[int]        =					q_groups
								_lowerCAmelCase			:			List[Any]        =					k_groups
								_lowerCAmelCase			:			List[Any]        =					v_groups
								_lowerCAmelCase			:			Dict        =					post_attention_groups
								_lowerCAmelCase			:			Optional[Any]        =					intermediate_groups
								_lowerCAmelCase			:			str        =					output_groups






class 				__snake_case     (_a						):



				@property
				def 					SCREAMING_SNAKE_CASE				(					self					:						Union[str, Any]					)     ->			Mapping[str, Mapping[int, str]]:





								'''simple docstring'''




								if self.task == "multiple-choice":
												_lowerCAmelCase			:			Optional[int]        =					{0: """batch""", 1: """choice""", 2: """sequence"""}
								else:
												_lowerCAmelCase			:			int        =					{0: """batch""", 1: """sequence"""}
								return OrderedDict(
								    [
								        ("""input_ids""", dynamic_axis),
								        ("""attention_mask""", dynamic_axis),
								        ("""token_type_ids""", dynamic_axis),
								    ]					)

