





'''simple docstring'''




import torch

from diffusers import KDPMaDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest







class   A_	(  lowerCAmelCase_			):
 _lowerCamelCase  : Dict           =							(KDPMaDiscreteScheduler,)
 _lowerCamelCase  : str           =							10




 def      lowercase	(					self       :    Tuple	,			**snake_case_       :    int    ):
      _UpperCAmelCase     							=   {
          "num_train_timesteps": 1_1_0_0,
          "beta_start": 0.0_0_0_1,
          "beta_end": 0.0_2,
          "beta_schedule": "linear",
      }

      config.update(**snake_case_    )
      return config




 def      lowercase	(					self       :    Any    ):
      for timesteps in [1_0, 5_0, 1_0_0, 1_0_0_0]:
           self.check_over_configs(num_train_timesteps=snake_case_    )




 def      lowercase	(					self       :    Optional[Any]    ):
      for beta_start, beta_end in zip([0.0_0_0_0_1, 0.0_0_0_1, 0.0_0_1]	,			[0.0_0_0_2, 0.0_0_2, 0.0_2]    ):
           self.check_over_configs(beta_start=snake_case_	,			beta_end=snake_case_    )




 def      lowercase	(					self       :    Union[str, Any]    ):
      for schedule in ["linear", "scaled_linear"]:
           self.check_over_configs(beta_schedule=snake_case_    )




 def      lowercase	(					self       :    Tuple    ):
      for prediction_type in ["epsilon", "v_prediction"]:
           self.check_over_configs(prediction_type=snake_case_    )




 def      lowercase	(					self       :    Optional[int]    ):
      _UpperCAmelCase     							=   self.scheduler_classes[0]
      _UpperCAmelCase     							=   self.get_scheduler_config(prediction_type="v_prediction"    )
      _UpperCAmelCase     							=   scheduler_class(**snake_case_    )

      scheduler.set_timesteps(self.num_inference_steps    )

      _UpperCAmelCase     							=   self.dummy_model()
      _UpperCAmelCase     							=   self.dummy_sample_deter * scheduler.init_noise_sigma
      _UpperCAmelCase     							=   sample.to(snake_case_    )

      for i, t in enumerate(scheduler.timesteps    ):
           _UpperCAmelCase     							=   scheduler.scale_model_input(snake_case_	,			snake_case_    )

           _UpperCAmelCase     							=   model(snake_case_	,			snake_case_    )

           _UpperCAmelCase     							=   scheduler.step(snake_case_	,			snake_case_	,			snake_case_    )
           _UpperCAmelCase     							=   output.prev_sample

      _UpperCAmelCase     							=   torch.sum(torch.abs(snake_case_    )    )
      _UpperCAmelCase     							=   torch.mean(torch.abs(snake_case_    )    )

      if torch_device in ["cpu", "mps"]:
           assert abs(result_sum.item() - 4.6_934e-07    ) < 1e-2
           assert abs(result_mean.item() - 6.1_112e-10    ) < 1e-3
      else:
           # CUDA
           assert abs(result_sum.item() - 4.693_428_650_170_972e-07    ) < 1e-2
           assert abs(result_mean.item() - 0.0_0_0_2    ) < 1e-3




 def      lowercase	(					self       :    str    ):
      if torch_device == "mps":
           return
      _UpperCAmelCase     							=   self.scheduler_classes[0]
      _UpperCAmelCase     							=   self.get_scheduler_config()
      _UpperCAmelCase     							=   scheduler_class(**snake_case_    )

      scheduler.set_timesteps(self.num_inference_steps    )

      _UpperCAmelCase     							=   self.dummy_model()
      _UpperCAmelCase     							=   self.dummy_sample_deter * scheduler.init_noise_sigma
      _UpperCAmelCase     							=   sample.to(snake_case_    )

      for i, t in enumerate(scheduler.timesteps    ):
           _UpperCAmelCase     							=   scheduler.scale_model_input(snake_case_	,			snake_case_    )

           _UpperCAmelCase     							=   model(snake_case_	,			snake_case_    )

           _UpperCAmelCase     							=   scheduler.step(snake_case_	,			snake_case_	,			snake_case_    )
           _UpperCAmelCase     							=   output.prev_sample

      _UpperCAmelCase     							=   torch.sum(torch.abs(snake_case_    )    )
      _UpperCAmelCase     							=   torch.mean(torch.abs(snake_case_    )    )

      if torch_device in ["cpu", "mps"]:
           assert abs(result_sum.item() - 2_0.4_1_2_5    ) < 1e-2
           assert abs(result_mean.item() - 0.0_2_6_6    ) < 1e-3
      else:
           # CUDA
           assert abs(result_sum.item() - 2_0.4_1_2_5    ) < 1e-2
           assert abs(result_mean.item() - 0.0_2_6_6    ) < 1e-3




 def      lowercase	(					self       :    Union[str, Any]    ):
      if torch_device == "mps":
           return
      _UpperCAmelCase     							=   self.scheduler_classes[0]
      _UpperCAmelCase     							=   self.get_scheduler_config()
      _UpperCAmelCase     							=   scheduler_class(**snake_case_    )

      scheduler.set_timesteps(self.num_inference_steps	,			device=snake_case_    )

      _UpperCAmelCase     							=   self.dummy_model()
      _UpperCAmelCase     							=   self.dummy_sample_deter.to(snake_case_    ) * scheduler.init_noise_sigma

      for t in scheduler.timesteps:
           _UpperCAmelCase     							=   scheduler.scale_model_input(snake_case_	,			snake_case_    )

           _UpperCAmelCase     							=   model(snake_case_	,			snake_case_    )

           _UpperCAmelCase     							=   scheduler.step(snake_case_	,			snake_case_	,			snake_case_    )
           _UpperCAmelCase     							=   output.prev_sample

      _UpperCAmelCase     							=   torch.sum(torch.abs(snake_case_    )    )
      _UpperCAmelCase     							=   torch.mean(torch.abs(snake_case_    )    )

      if str(snake_case_    ).startswith("cpu"    ):
           # The following sum varies between 148 and 156 on mps. Why?
           assert abs(result_sum.item() - 2_0.4_1_2_5    ) < 1e-2
           assert abs(result_mean.item() - 0.0_2_6_6    ) < 1e-3
      else:
           # CUDA
           assert abs(result_sum.item() - 2_0.4_1_2_5    ) < 1e-2
           assert abs(result_mean.item() - 0.0_2_6_6    ) < 1e-3











'''simple docstring'''




from collections import OrderedDict
from typing import Any, Mapping, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...feature_extraction_utils import FeatureExtractionMixin
from ...onnx import OnnxConfig
from ...onnx.utils import compute_effective_axis_dimension
from ...tokenization_utils_base import PreTrainedTokenizerBase
from ...utils import TensorType, logging


__SCREAMING_SNAKE_CASE     :Dict       =				logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE     :int       =				{
    '''deepmind/language-perceiver''': '''https://huggingface.co/deepmind/language-perceiver/resolve/main/config.json''',
    # See all Perceiver models at https://huggingface.co/models?filter=perceiver
}







class   A_	(  lowerCAmelCase_			):
 _lowerCamelCase  : int           =							"""perceiver"""




 def __init__(					self       :    Any	,			snake_case_       :    List[Any]=2_5_6	,			snake_case_       :    str=1_2_8_0	,			snake_case_       :    Optional[int]=7_6_8	,			snake_case_       :    int=1	,			snake_case_       :    List[Any]=2_6	,			snake_case_       :    Dict=8	,			snake_case_       :    List[Any]=8	,			snake_case_       :    Tuple=None	,			snake_case_       :    Tuple=None	,			snake_case_       :    Any="kv"	,			snake_case_       :    Any=1	,			snake_case_       :    List[str]=1	,			snake_case_       :    Optional[int]="gelu"	,			snake_case_       :    List[Any]=0.1	,			snake_case_       :    Dict=0.0_2	,			snake_case_       :    int=1e-12	,			snake_case_       :    List[str]=True	,			snake_case_       :    str=2_6_2	,			snake_case_       :    Optional[Any]=2_0_4_8	,			snake_case_       :    Union[str, Any]=5_6	,			snake_case_       :    Dict=[3_6_8, 4_9_6]	,			snake_case_       :    Tuple=1_6	,			snake_case_       :    Union[str, Any]=1_9_2_0	,			snake_case_       :    List[Any]=1_6	,			snake_case_       :    Tuple=[1, 1_6, 2_2_4, 2_2_4]	,			**snake_case_       :    List[Any]	,			):
      super().__init__(**snake_case_    )

      _UpperCAmelCase     							=   num_latents
      _UpperCAmelCase     							=   d_latents
      _UpperCAmelCase     							=   d_model
      _UpperCAmelCase     							=   num_blocks
      _UpperCAmelCase     							=   num_self_attends_per_block
      _UpperCAmelCase     							=   num_self_attention_heads
      _UpperCAmelCase     							=   num_cross_attention_heads
      _UpperCAmelCase     							=   qk_channels
      _UpperCAmelCase     							=   v_channels
      _UpperCAmelCase     							=   cross_attention_shape_for_attention
      _UpperCAmelCase     							=   self_attention_widening_factor
      _UpperCAmelCase     							=   cross_attention_widening_factor
      _UpperCAmelCase     							=   hidden_act
      _UpperCAmelCase     							=   attention_probs_dropout_prob
      _UpperCAmelCase     							=   initializer_range
      _UpperCAmelCase     							=   layer_norm_eps
      _UpperCAmelCase     							=   use_query_residual
      # masked language modeling attributes
      _UpperCAmelCase     							=   vocab_size
      _UpperCAmelCase     							=   max_position_embeddings
      # image classification attributes
      _UpperCAmelCase     							=   image_size
      # flow attributes
      _UpperCAmelCase     							=   train_size
      # multimodal autoencoding attributes
      _UpperCAmelCase     							=   num_frames
      _UpperCAmelCase     							=   audio_samples_per_frame
      _UpperCAmelCase     							=   samples_per_patch
      _UpperCAmelCase     							=   output_shape







class   A_	(  lowerCAmelCase_			):




 @property
 def      lowercase	(					self       :    int    ):
      if self.task == "multiple-choice":
           _UpperCAmelCase     							=   {0: "batch", 1: "choice", 2: "sequence"}
      else:
           _UpperCAmelCase     							=   {0: "batch", 1: "sequence"}
      return OrderedDict(
          [
              ("inputs", dynamic_axis),
              ("attention_mask", dynamic_axis),
          ]    )




 @property
 def      lowercase	(					self       :    Optional[Any]    ):
      return 1e-4




 def      lowercase	(					self       :    List[str]	,			snake_case_       :    Union["PreTrainedTokenizerBase", "FeatureExtractionMixin"]	,			snake_case_       :    int = -1	,			snake_case_       :    int = -1	,			snake_case_       :    int = -1	,			snake_case_       :    bool = False	,			snake_case_       :    Optional[TensorType] = None	,			snake_case_       :    int = 3	,			snake_case_       :    int = 4_0	,			snake_case_       :    int = 4_0	,			):
      # copied from `transformers.onnx.config.OnnxConfig` and slightly altered/simplified

      if isinstance(snake_case_	,			snake_case_    ):
           # If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
           _UpperCAmelCase     							=   compute_effective_axis_dimension(
               snake_case_	,			fixed_dimension=OnnxConfig.default_fixed_batch	,			num_token_to_add=0    )
           # If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
           _UpperCAmelCase     							=   preprocessor.num_special_tokens_to_add(snake_case_    )
           _UpperCAmelCase     							=   compute_effective_axis_dimension(
               snake_case_	,			fixed_dimension=OnnxConfig.default_fixed_sequence	,			num_token_to_add=snake_case_    )
           # Generate dummy inputs according to compute batch and sequence
           _UpperCAmelCase     							=   [" ".join(["a"]    ) * seq_length] * batch_size
           _UpperCAmelCase     							=   dict(preprocessor(snake_case_	,			return_tensors=snake_case_    )    )
           _UpperCAmelCase     							=   inputs.pop("input_ids"    )
           return inputs
      elif isinstance(snake_case_	,			snake_case_    ) and preprocessor.model_input_names[0] == "pixel_values":
           # If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
           _UpperCAmelCase     							=   compute_effective_axis_dimension(snake_case_	,			fixed_dimension=OnnxConfig.default_fixed_batch    )
           _UpperCAmelCase     							=   self._generate_dummy_images(snake_case_	,			snake_case_	,			snake_case_	,			snake_case_    )
           _UpperCAmelCase     							=   dict(preprocessor(images=snake_case_	,			return_tensors=snake_case_    )    )
           _UpperCAmelCase     							=   inputs.pop("pixel_values"    )
           return inputs
      else:
           raise ValueError(
               "Unable to generate dummy inputs for the model. Please provide a tokenizer or a preprocessor."    )




