


"""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 		_A    (    unittest.TestCase    ):







    """simple docstring"""




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






    def       __snake_case				(   self							:       Optional[int]							,					__UpperCAmelCase							:       Optional[int]=0.9_999):
      a		:						str 				=     UNetaDConditionModel.from_pretrained(self.model_id							,					subfolder="unet")
      a		:						int 				=     unet.to(__UpperCAmelCase)
      a		:						Union[str, Any] 				=     EMAModel(unet.parameters()							,					decay=__UpperCAmelCase							,					model_cls=__UpperCAmelCase							,					model_config=unet.config)
      return unet, ema_unet






    def       __snake_case				(   self							:       Union[str, Any]):
      a		:						Tuple 				=     torch.randn(
          self.batch_size							,					self.num_in_channels							,					self.latent_height							,					self.latent_width							,					generator=self.generator).to(__UpperCAmelCase)
      a		:						Optional[int] 				=     torch.randint(0							,					1000							,					size=(self.batch_size,)							,					generator=self.generator).to(__UpperCAmelCase)
      a		:						Tuple 				=     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       __snake_case				(   self							:       Any							,					__UpperCAmelCase							:       str):
      a		:						str 				=     {}
      for k, param in unet.state_dict().items():
        a		:						Optional[int] 				=     torch.randn_like(__UpperCAmelCase) + (param * torch.randn_like(__UpperCAmelCase))
        updated_state_dict.update({k: updated_param})
      unet.load_state_dict(__UpperCAmelCase)
      return unet






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

      # First backprop + EMA
      a		:						List[str] 				=     self.simulate_backprop(__UpperCAmelCase)
      ema_unet.step(unet_step_one.parameters())
      a		:						Dict 				=     ema_unet.shadow_params

      # Second backprop + EMA
      a		:						Tuple 				=     self.simulate_backprop(__UpperCAmelCase)
      ema_unet.step(unet_step_two.parameters())
      a		:						Optional[Any] 				=     ema_unet.shadow_params

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






    def       __snake_case				(   self							:       int):
      # 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.
      a				,						a		:						Dict 				=     self.get_models(decay=0.0)
      a		:						Dict 				=     self.simulate_backprop(__UpperCAmelCase)
      ema_unet.step(unet_step_one.parameters())
      a		:						str 				=     ema_unet.shadow_params

      a		:						Optional[Any] 				=     self.simulate_backprop(__UpperCAmelCase)
      ema_unet.step(unet_step_two.parameters())
      a		:						Dict 				=     ema_unet.shadow_params

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






    @skip_mps
    def       __snake_case				(   self							:       int):
      a				,						a		:						Dict 				=     self.get_models()
      a				,						a				,						a		:						Any 				=     self.get_dummy_inputs()

      with tempfile.TemporaryDirectory() as tmpdir:
        ema_unet.save_pretrained(__UpperCAmelCase)
        a		:						Union[str, Any] 				=     UNetaDConditionModel.from_pretrained(__UpperCAmelCase							,					model_cls=__UpperCAmelCase)
        a		:						Any 				=     loaded_unet.to(unet.device)

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

      assert torch.allclose(__UpperCAmelCase							,					__UpperCAmelCase							,					atol=1e-4)










"""simple docstring"""







from collections import OrderedDict
from typing import Mapping

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


__lowercase          = logging.get_logger(__name__)

__lowercase          = {
    """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 		_A    (    _a    ):







    """simple docstring"""




    UpperCAmelCase			:				Tuple      =			SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP
    UpperCAmelCase			:				Any      =			"""squeezebert"""






    def __init__(   self							:       List[str]							,					__UpperCAmelCase							:       Tuple=30522							,					__UpperCAmelCase							:       Tuple=768							,					__UpperCAmelCase							:       int=12							,					__UpperCAmelCase							:       str=12							,					__UpperCAmelCase							:       List[Any]=3072							,					__UpperCAmelCase							:       Dict="gelu"							,					__UpperCAmelCase							:       List[Any]=0.1							,					__UpperCAmelCase							:       Optional[int]=0.1							,					__UpperCAmelCase							:       Optional[Any]=512							,					__UpperCAmelCase							:       int=2							,					__UpperCAmelCase							:       Dict=0.02							,					__UpperCAmelCase							:       Optional[int]=1e-12							,					__UpperCAmelCase							:       Optional[Any]=0							,					__UpperCAmelCase							:       Optional[Any]=768							,					__UpperCAmelCase							:       Tuple=4							,					__UpperCAmelCase							:       Dict=4							,					__UpperCAmelCase							:       str=4							,					__UpperCAmelCase							:       List[Any]=1							,					__UpperCAmelCase							:       Optional[int]=4							,					__UpperCAmelCase							:       Optional[int]=4							,					**__UpperCAmelCase							:       Tuple							,					):
      super().__init__(pad_token_id=__UpperCAmelCase							,					**__UpperCAmelCase)

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






class 		_A    (    _a    ):







    """simple docstring"""




    @property
    def       __snake_case				(   self							:       Optional[Any]):
      if self.task == "multiple-choice":
        a		:						Optional[int] 				=     {0: "batch", 1: "choice", 2: "sequence"}
      else:
        a		:						Tuple 				=     {0: "batch", 1: "sequence"}
      return OrderedDict(
          [
              ("input_ids", dynamic_axis),
              ("attention_mask", dynamic_axis),
              ("token_type_ids", dynamic_axis),
          ])






