


import tempfile

import torch

from diffusers import PNDMScheduler

from .test_schedulers import SchedulerCommonTest

class   A__ (   a_     ):







  """simple docstring"""

  __magic_name__        =		(PNDMScheduler,)
  __magic_name__        =		(('num_inference_steps', 50),)
  def 		a_      (	self      , **__snake_case   ):
       snake_case					=    {
           '''num_train_timesteps''': 1_0_0_0,
           '''beta_start''': 0.0001,
           '''beta_end''': 0.02,
           '''beta_schedule''': '''linear''',
       }

       config.update(**lowercase_   )
       return config
  def 		a_      (	self      , __snake_case=0      , **__snake_case   ):
       snake_case					=    dict(self.forward_default_kwargs   )
       snake_case					=    kwargs.pop('''num_inference_steps'''      , lowercase_   )
       snake_case					=    self.dummy_sample
       snake_case					=    0.1 * sample
       snake_case					=    [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

       for scheduler_class in self.scheduler_classes:
            snake_case					=    self.get_scheduler_config(**lowercase_   )
            snake_case					=    scheduler_class(**lowercase_   )
            scheduler.set_timesteps(lowercase_   )
            # copy over dummy past residuals
            snake_case					=    dummy_past_residuals[:]

            with tempfile.TemporaryDirectory() as tmpdirname:
                 scheduler.save_config(lowercase_   )
                 snake_case					=    scheduler_class.from_pretrained(lowercase_   )
                 new_scheduler.set_timesteps(lowercase_   )
                 # copy over dummy past residuals
                 snake_case					=    dummy_past_residuals[:]

            snake_case					=    scheduler.step_prk(lowercase_      , lowercase_      , lowercase_      , **lowercase_   ).prev_sample
            snake_case					=    new_scheduler.step_prk(lowercase_      , lowercase_      , lowercase_      , **lowercase_   ).prev_sample

            assert torch.sum(torch.abs(output - new_output   )   ) < 1E-5, "Scheduler outputs are not identical"

            snake_case					=    scheduler.step_plms(lowercase_      , lowercase_      , lowercase_      , **lowercase_   ).prev_sample
            snake_case					=    new_scheduler.step_plms(lowercase_      , lowercase_      , lowercase_      , **lowercase_   ).prev_sample

            assert torch.sum(torch.abs(output - new_output   )   ) < 1E-5, "Scheduler outputs are not identical"
  def 		a_      (	self   ):
       pass
  def 		a_      (	self      , __snake_case=0      , **__snake_case   ):
       snake_case					=    dict(self.forward_default_kwargs   )
       snake_case					=    kwargs.pop('''num_inference_steps'''      , lowercase_   )
       snake_case					=    self.dummy_sample
       snake_case					=    0.1 * sample
       snake_case					=    [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

       for scheduler_class in self.scheduler_classes:
            snake_case					=    self.get_scheduler_config()
            snake_case					=    scheduler_class(**lowercase_   )
            scheduler.set_timesteps(lowercase_   )

            # copy over dummy past residuals (must be after setting timesteps)
            snake_case					=    dummy_past_residuals[:]

            with tempfile.TemporaryDirectory() as tmpdirname:
                 scheduler.save_config(lowercase_   )
                 snake_case					=    scheduler_class.from_pretrained(lowercase_   )
                 # copy over dummy past residuals
                 new_scheduler.set_timesteps(lowercase_   )

                 # copy over dummy past residual (must be after setting timesteps)
                 snake_case					=    dummy_past_residuals[:]

            snake_case					=    scheduler.step_prk(lowercase_      , lowercase_      , lowercase_      , **lowercase_   ).prev_sample
            snake_case					=    new_scheduler.step_prk(lowercase_      , lowercase_      , lowercase_      , **lowercase_   ).prev_sample

            assert torch.sum(torch.abs(output - new_output   )   ) < 1E-5, "Scheduler outputs are not identical"

            snake_case					=    scheduler.step_plms(lowercase_      , lowercase_      , lowercase_      , **lowercase_   ).prev_sample
            snake_case					=    new_scheduler.step_plms(lowercase_      , lowercase_      , lowercase_      , **lowercase_   ).prev_sample

            assert torch.sum(torch.abs(output - new_output   )   ) < 1E-5, "Scheduler outputs are not identical"
  def 		a_      (	self      , **__snake_case   ):
       snake_case					=    self.scheduler_classes[0]
       snake_case					=    self.get_scheduler_config(**lowercase_   )
       snake_case					=    scheduler_class(**lowercase_   )

       snake_case					=    1_0
       snake_case					=    self.dummy_model()
       snake_case					=    self.dummy_sample_deter
       scheduler.set_timesteps(lowercase_   )

       for i, t in enumerate(scheduler.prk_timesteps   ):
            snake_case					=    model(lowercase_      , lowercase_   )
            snake_case					=    scheduler.step_prk(lowercase_      , lowercase_      , lowercase_   ).prev_sample

       for i, t in enumerate(scheduler.plms_timesteps   ):
            snake_case					=    model(lowercase_      , lowercase_   )
            snake_case					=    scheduler.step_plms(lowercase_      , lowercase_      , lowercase_   ).prev_sample

       return sample
  def 		a_      (	self   ):
       snake_case					=    dict(self.forward_default_kwargs   )

       snake_case					=    kwargs.pop('''num_inference_steps'''      , lowercase_   )

       for scheduler_class in self.scheduler_classes:
            snake_case					=    self.get_scheduler_config()
            snake_case					=    scheduler_class(**lowercase_   )

            snake_case					=    self.dummy_sample
            snake_case					=    0.1 * sample

            if num_inference_steps is not None and hasattr(lowercase_      , '''set_timesteps'''   ):
                 scheduler.set_timesteps(lowercase_   )
            elif num_inference_steps is not None and not hasattr(lowercase_      , '''set_timesteps'''   ):
                 snake_case					=    num_inference_steps

            # copy over dummy past residuals (must be done after set_timesteps)
            snake_case					=    [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
            snake_case					=    dummy_past_residuals[:]

            snake_case					=    scheduler.step_prk(lowercase_      , 0      , lowercase_      , **lowercase_   ).prev_sample
            snake_case					=    scheduler.step_prk(lowercase_      , 1      , lowercase_      , **lowercase_   ).prev_sample

            self.assertEqual(output_a.shape      , sample.shape   )
            self.assertEqual(output_a.shape      , output_a.shape   )

            snake_case					=    scheduler.step_plms(lowercase_      , 0      , lowercase_      , **lowercase_   ).prev_sample
            snake_case					=    scheduler.step_plms(lowercase_      , 1      , lowercase_      , **lowercase_   ).prev_sample

            self.assertEqual(output_a.shape      , sample.shape   )
            self.assertEqual(output_a.shape      , output_a.shape   )
  def 		a_      (	self   ):
       for timesteps in [1_0_0, 1_0_0_0]:
            self.check_over_configs(num_train_timesteps=lowercase_   )
  def 		a_      (	self   ):
       for steps_offset in [0, 1]:
            self.check_over_configs(steps_offset=lowercase_   )

       snake_case					=    self.scheduler_classes[0]
       snake_case					=    self.get_scheduler_config(steps_offset=1   )
       snake_case					=    scheduler_class(**lowercase_   )
       scheduler.set_timesteps(1_0   )
       assert torch.equal(
           scheduler.timesteps      , torch.LongTensor(
               [9_0_1, 8_5_1, 8_5_1, 8_0_1, 8_0_1, 7_5_1, 7_5_1, 7_0_1, 7_0_1, 6_5_1, 6_5_1, 6_0_1, 6_0_1, 5_0_1, 4_0_1, 3_0_1, 2_0_1, 1_0_1, 1]   )      , )
  def 		a_      (	self   ):
       for beta_start, beta_end in zip([0.0001, 0.001]      , [0.002, 0.02]   ):
            self.check_over_configs(beta_start=lowercase_      , beta_end=lowercase_   )
  def 		a_      (	self   ):
       for schedule in ["linear", "squaredcos_cap_v2"]:
            self.check_over_configs(beta_schedule=lowercase_   )
  def 		a_      (	self   ):
       for prediction_type in ["epsilon", "v_prediction"]:
            self.check_over_configs(prediction_type=lowercase_   )
  def 		a_      (	self   ):
       for t in [1, 5, 1_0]:
            self.check_over_forward(time_step=lowercase_   )
  def 		a_      (	self   ):
       for t, num_inference_steps in zip([1, 5, 1_0]      , [1_0, 5_0, 1_0_0]   ):
            self.check_over_forward(num_inference_steps=lowercase_   )
  def 		a_      (	self   ):
       # earlier version of set_timesteps() caused an error indexing alpha's with inference steps as power of 3
       snake_case					=    2_7

       for scheduler_class in self.scheduler_classes:
            snake_case					=    self.dummy_sample
            snake_case					=    0.1 * sample

            snake_case					=    self.get_scheduler_config()
            snake_case					=    scheduler_class(**lowercase_   )

            scheduler.set_timesteps(lowercase_   )

            # before power of 3 fix, would error on first step, so we only need to do two
            for i, t in enumerate(scheduler.prk_timesteps[:2]   ):
                 snake_case					=    scheduler.step_prk(lowercase_      , lowercase_      , lowercase_   ).prev_sample
  def 		a_      (	self   ):
       with self.assertRaises(lowercase_   ):
            snake_case					=    self.scheduler_classes[0]
            snake_case					=    self.get_scheduler_config()
            snake_case					=    scheduler_class(**lowercase_   )

            scheduler.step_plms(self.dummy_sample      , 1      , self.dummy_sample   ).prev_sample
  def 		a_      (	self   ):
       snake_case					=    self.full_loop()
       snake_case					=    torch.sum(torch.abs(lowercase_   )   )
       snake_case					=    torch.mean(torch.abs(lowercase_   )   )

       assert abs(result_sum.item() - 198.1318   ) < 1E-2
       assert abs(result_mean.item() - 0.2580   ) < 1E-3
  def 		a_      (	self   ):
       snake_case					=    self.full_loop(prediction_type='''v_prediction'''   )
       snake_case					=    torch.sum(torch.abs(lowercase_   )   )
       snake_case					=    torch.mean(torch.abs(lowercase_   )   )

       assert abs(result_sum.item() - 67.3986   ) < 1E-2
       assert abs(result_mean.item() - 0.0878   ) < 1E-3
  def 		a_      (	self   ):
       # We specify different beta, so that the first alpha is 0.99
       snake_case					=    self.full_loop(set_alpha_to_one=lowercase_      , beta_start=0.01   )
       snake_case					=    torch.sum(torch.abs(lowercase_   )   )
       snake_case					=    torch.mean(torch.abs(lowercase_   )   )

       assert abs(result_sum.item() - 230.0399   ) < 1E-2
       assert abs(result_mean.item() - 0.2995   ) < 1E-3




  def 		a_      (	self   ):
       # We specify different beta, so that the first alpha is 0.99
       snake_case					=    self.full_loop(set_alpha_to_one=lowercase_      , beta_start=0.01   )
       snake_case					=    torch.sum(torch.abs(lowercase_   )   )
       snake_case					=    torch.mean(torch.abs(lowercase_   )   )

       assert abs(result_sum.item() - 186.9482   ) < 1E-2
       assert abs(result_mean.item() - 0.2434   ) < 1E-3








from ...configuration_utils import PretrainedConfig
from ...utils import logging


_SCREAMING_SNAKE_CASE      :			Dict      				=   logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE      :			Optional[Any]      				=   {
    "alibaba-damo/mgp-str-base": "https://huggingface.co/alibaba-damo/mgp-str-base/resolve/main/config.json",
}

class   A__ (   snake_case__     ):







    """simple docstring"""

    __magic_name__        =		'mgp-str'
    def __init__(	self      , __snake_case=[3_2, 1_2_8]      , __snake_case=4      , __snake_case=3      , __snake_case=2_7      , __snake_case=3_8      , __snake_case=5_0_2_5_7      , __snake_case=3_0_5_2_2      , __snake_case=7_6_8      , __snake_case=1_2      , __snake_case=1_2      , __snake_case=4.0      , __snake_case=True      , __snake_case=False      , __snake_case=1E-5      , __snake_case=0.0      , __snake_case=0.0      , __snake_case=0.0      , __snake_case=False      , __snake_case=0.02      , **__snake_case      , ):
         super().__init__(**__snake_case   )

         snake_case					=    image_size
         snake_case					=    patch_size
         snake_case					=    num_channels
         snake_case					=    max_token_length
         snake_case					=    num_character_labels
         snake_case					=    num_bpe_labels
         snake_case					=    num_wordpiece_labels
         snake_case					=    hidden_size
         snake_case					=    num_hidden_layers
         snake_case					=    num_attention_heads
         snake_case					=    mlp_ratio
         snake_case					=    distilled
         snake_case					=    layer_norm_eps
         snake_case					=    drop_rate
         snake_case					=    qkv_bias
         snake_case					=    attn_drop_rate
         snake_case					=    drop_path_rate
         snake_case					=    output_aa_attentions
         snake_case					=    initializer_range




