






"""simple docstring"""






import argparse
import os

import gluonnlp as nlp
import mxnet as mx
import numpy as np
import torch
from gluonnlp.base import get_home_dir
from gluonnlp.model.bert import BERTEncoder
from gluonnlp.model.utils import _load_vocab
from gluonnlp.vocab import Vocab
from packaging import version
from torch import nn

from transformers import BertConfig, BertForMaskedLM, BertModel, RobertaTokenizer
from transformers.models.bert.modeling_bert import (
    BertIntermediate,
    BertLayer,
    BertOutput,
    BertSelfAttention,
    BertSelfOutput,
)
from transformers.utils import logging


if version.parse(nlp.__version__) != version.parse("""0.8.3"""):
  raise Exception("""requires gluonnlp == 0.8.3""")

if version.parse(mx.__version__) != version.parse("""1.5.0"""):
  raise Exception("""requires mxnet == 1.5.0""")

logging.set_verbosity_info()
UpperCAmelCase_   : List[str]   			= logging.get_logger(__name__)

UpperCAmelCase_   : Optional[int]   			= """The Nymphenburg Palace is a beautiful palace in Munich!"""



def 					_A			(__a    ,				__a							) ->       int:
    """simple docstring"""






    SCREAMING_SNAKE_CASE_      :  str    						=		{
        '''attention_cell''': '''multi_head''',
        '''num_layers''': 4,
        '''units''': 10_24,
        '''hidden_size''': 7_68,
        '''max_length''': 5_12,
        '''num_heads''': 8,
        '''scaled''': True,
        '''dropout''': 0.1,
        '''use_residual''': True,
        '''embed_size''': 10_24,
        '''embed_dropout''': 0.1,
        '''word_embed''': None,
        '''layer_norm_eps''': 1e-5,
        '''token_type_vocab_size''': 2,
    }

    SCREAMING_SNAKE_CASE_      :  str    						=		bort_4_8_768_1024_hparams

    # Let's construct the original Bort model here
    # Taken from official BERT implementation, see:
    # https://github.com/alexa/bort/blob/master/bort/bort.py
    SCREAMING_SNAKE_CASE_      :  Dict    						=		BERTEncoder(
        attention_cell=predefined_args['''attention_cell''']    ,				num_layers=predefined_args['''num_layers''']    ,				units=predefined_args['''units''']    ,				hidden_size=predefined_args['''hidden_size''']    ,				max_length=predefined_args['''max_length''']    ,				num_heads=predefined_args['''num_heads''']    ,				scaled=predefined_args['''scaled''']    ,				dropout=predefined_args['''dropout''']    ,				output_attention=__a    ,				output_all_encodings=__a    ,				use_residual=predefined_args['''use_residual''']    ,				activation=predefined_args.get('''activation'''    ,				'''gelu'''							)    ,				layer_norm_eps=predefined_args.get('''layer_norm_eps'''    ,				__a							)    ,				)

    # Vocab information needs to be fetched first
    # It's the same as RoBERTa, so RobertaTokenizer can be used later
    SCREAMING_SNAKE_CASE_      :  str    						=		'''openwebtext_ccnews_stories_books_cased'''

    # Specify download folder to Gluonnlp's vocab
    SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		os.path.join(get_home_dir()    ,				'''models'''							)
    SCREAMING_SNAKE_CASE_      :  List[Any]    						=		_load_vocab(__a    ,				__a    ,				__a    ,				cls=__a							)

    SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		nlp.model.BERTModel(
        __a    ,				len(__a							)    ,				units=predefined_args['''units''']    ,				embed_size=predefined_args['''embed_size''']    ,				embed_dropout=predefined_args['''embed_dropout''']    ,				word_embed=predefined_args['''word_embed''']    ,				use_pooler=__a    ,				use_token_type_embed=__a    ,				token_type_vocab_size=predefined_args['''token_type_vocab_size''']    ,				use_classifier=__a    ,				use_decoder=__a    ,				)

    original_bort.load_parameters(__a    ,				cast_dtype=__a    ,				ignore_extra=__a							)
    SCREAMING_SNAKE_CASE_      :  List[str]    						=		original_bort._collect_params_with_prefix()

    # Build our config 🤗
    SCREAMING_SNAKE_CASE_      :  List[Any]    						=		{
        '''architectures''': ['''BertForMaskedLM'''],
        '''attention_probs_dropout_prob''': predefined_args['''dropout'''],
        '''hidden_act''': '''gelu''',
        '''hidden_dropout_prob''': predefined_args['''dropout'''],
        '''hidden_size''': predefined_args['''embed_size'''],
        '''initializer_range''': 0.02,
        '''intermediate_size''': predefined_args['''hidden_size'''],
        '''layer_norm_eps''': predefined_args['''layer_norm_eps'''],
        '''max_position_embeddings''': predefined_args['''max_length'''],
        '''model_type''': '''bort''',
        '''num_attention_heads''': predefined_args['''num_heads'''],
        '''num_hidden_layers''': predefined_args['''num_layers'''],
        '''pad_token_id''': 1,  # 2 = BERT, 1 = RoBERTa
        '''type_vocab_size''': 1,  # 2 = BERT, 1 = RoBERTa
        '''vocab_size''': len(__a							),
    }

    SCREAMING_SNAKE_CASE_      :  List[str]    						=		BertConfig.from_dict(__a							)
    SCREAMING_SNAKE_CASE_      :  str    						=		BertForMaskedLM(__a							)
    hf_bort_model.eval()

    # Parameter mapping table (Gluonnlp to Transformers)
    # * denotes layer index
    #
    # | Gluon Parameter                                                | Transformers Parameter
    # | -------------------------------------------------------------- | ----------------------
    # | `encoder.layer_norm.beta`                                      | `bert.embeddings.LayerNorm.bias`
    # | `encoder.layer_norm.gamma`                                     | `bert.embeddings.LayerNorm.weight`
    # | `encoder.position_weight`                                      | `bert.embeddings.position_embeddings.weight`
    # | `word_embed.0.weight`                                          | `bert.embeddings.word_embeddings.weight`
    # | `encoder.transformer_cells.*.attention_cell.proj_key.bias`     | `bert.encoder.layer.*.attention.self.key.bias`
    # | `encoder.transformer_cells.*.attention_cell.proj_key.weight`   | `bert.encoder.layer.*.attention.self.key.weight`
    # | `encoder.transformer_cells.*.attention_cell.proj_query.bias`   | `bert.encoder.layer.*.attention.self.query.bias`
    # | `encoder.transformer_cells.*.attention_cell.proj_query.weight` | `bert.encoder.layer.*.attention.self.query.weight`
    # | `encoder.transformer_cells.*.attention_cell.proj_value.bias`   | `bert.encoder.layer.*.attention.self.value.bias`
    # | `encoder.transformer_cells.*.attention_cell.proj_value.weight` | `bert.encoder.layer.*.attention.self.value.weight`
    # | `encoder.transformer_cells.*.ffn.ffn_2.bias`                   | `bert.encoder.layer.*.attention.output.dense.bias`
    # | `encoder.transformer_cells.*.ffn.ffn_2.weight`                 | `bert.encoder.layer.*.attention.output.dense.weight`
    # | `encoder.transformer_cells.*.layer_norm.beta`                  | `bert.encoder.layer.*.attention.output.LayerNorm.bias`
    # | `encoder.transformer_cells.*.layer_norm.gamma`                 | `bert.encoder.layer.*.attention.output.LayerNorm.weight`
    # | `encoder.transformer_cells.*.ffn.ffn_1.bias`                   | `bert.encoder.layer.*.intermediate.dense.bias`
    # | `encoder.transformer_cells.*.ffn.ffn_1.weight`                 | `bert.encoder.layer.*.intermediate.dense.weight`
    # | `encoder.transformer_cells.*.ffn.layer_norm.beta`              | `bert.encoder.layer.*.output.LayerNorm.bias`
    # | `encoder.transformer_cells.*.ffn.layer_norm.gamma`             | `bert.encoder.layer.*.output.LayerNorm.weight`
    # | `encoder.transformer_cells.*.proj.bias`                        | `bert.encoder.layer.*.output.dense.bias`
    # | `encoder.transformer_cells.*.proj.weight`                      | `bert.encoder.layer.*.output.dense.weight`

    # Helper function to convert MXNET Arrays to PyTorch
    def to_torch(__a							) -> nn.Parameter:
        return nn.Parameter(torch.FloatTensor(mx_array.data().asnumpy()							)							)

    # Check param shapes and map new HF param back
    def check_and_map_params(__a    ,				__a							):
        SCREAMING_SNAKE_CASE_      :  Tuple    						=		hf_param.shape

        SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		to_torch(params[gluon_param]							)
        SCREAMING_SNAKE_CASE_      :  Tuple    						=		gluon_param.shape

        assert (
            shape_hf == shape_gluon
        ), f'The gluon parameter {gluon_param} has shape {shape_gluon}, but expects shape {shape_hf} for Transformers'

        return gluon_param

    SCREAMING_SNAKE_CASE_      :  str    						=		check_and_map_params(
        hf_bort_model.bert.embeddings.word_embeddings.weight    ,				'''word_embed.0.weight'''							)
    SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		check_and_map_params(
        hf_bort_model.bert.embeddings.position_embeddings.weight    ,				'''encoder.position_weight'''							)
    SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		check_and_map_params(
        hf_bort_model.bert.embeddings.LayerNorm.bias    ,				'''encoder.layer_norm.beta'''							)
    SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		check_and_map_params(
        hf_bort_model.bert.embeddings.LayerNorm.weight    ,				'''encoder.layer_norm.gamma'''							)

    # Inspired by RoBERTa conversion script, we just zero them out (Bort does not use them)
    SCREAMING_SNAKE_CASE_      :  Dict    						=		torch.zeros_like(
        hf_bort_model.bert.embeddings.token_type_embeddings.weight.data							)

    for i in range(hf_bort_config.num_hidden_layers							):
        SCREAMING_SNAKE_CASE_      :  BertLayer    						=		hf_bort_model.bert.encoder.layer[i]

        # self attention
        SCREAMING_SNAKE_CASE_      :  BertSelfAttention    						=		layer.attention.self

        SCREAMING_SNAKE_CASE_      :  Dict    						=		check_and_map_params(
            self_attn.key.bias.data    ,				f'encoder.transformer_cells.{i}.attention_cell.proj_key.bias'							)

        SCREAMING_SNAKE_CASE_      :  str    						=		check_and_map_params(
            self_attn.key.weight.data    ,				f'encoder.transformer_cells.{i}.attention_cell.proj_key.weight'							)
        SCREAMING_SNAKE_CASE_      :  Any    						=		check_and_map_params(
            self_attn.query.bias.data    ,				f'encoder.transformer_cells.{i}.attention_cell.proj_query.bias'							)
        SCREAMING_SNAKE_CASE_      :  str    						=		check_and_map_params(
            self_attn.query.weight.data    ,				f'encoder.transformer_cells.{i}.attention_cell.proj_query.weight'							)
        SCREAMING_SNAKE_CASE_      :  Tuple    						=		check_and_map_params(
            self_attn.value.bias.data    ,				f'encoder.transformer_cells.{i}.attention_cell.proj_value.bias'							)
        SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		check_and_map_params(
            self_attn.value.weight.data    ,				f'encoder.transformer_cells.{i}.attention_cell.proj_value.weight'							)

        # self attention output
        SCREAMING_SNAKE_CASE_      :  BertSelfOutput    						=		layer.attention.output

        SCREAMING_SNAKE_CASE_      :  List[str]    						=		check_and_map_params(
            self_output.dense.bias    ,				f'encoder.transformer_cells.{i}.proj.bias'							)
        SCREAMING_SNAKE_CASE_      :  List[str]    						=		check_and_map_params(
            self_output.dense.weight    ,				f'encoder.transformer_cells.{i}.proj.weight'							)
        SCREAMING_SNAKE_CASE_      :  Any    						=		check_and_map_params(
            self_output.LayerNorm.bias    ,				f'encoder.transformer_cells.{i}.layer_norm.beta'							)
        SCREAMING_SNAKE_CASE_      :  Tuple    						=		check_and_map_params(
            self_output.LayerNorm.weight    ,				f'encoder.transformer_cells.{i}.layer_norm.gamma'							)

        # intermediate
        SCREAMING_SNAKE_CASE_      :  BertIntermediate    						=		layer.intermediate

        SCREAMING_SNAKE_CASE_      :  List[Any]    						=		check_and_map_params(
            intermediate.dense.bias    ,				f'encoder.transformer_cells.{i}.ffn.ffn_1.bias'							)
        SCREAMING_SNAKE_CASE_      :  Any    						=		check_and_map_params(
            intermediate.dense.weight    ,				f'encoder.transformer_cells.{i}.ffn.ffn_1.weight'							)

        # output
        SCREAMING_SNAKE_CASE_      :  BertOutput    						=		layer.output

        SCREAMING_SNAKE_CASE_      :  Dict    						=		check_and_map_params(
            bert_output.dense.bias    ,				f'encoder.transformer_cells.{i}.ffn.ffn_2.bias'							)
        SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		check_and_map_params(
            bert_output.dense.weight    ,				f'encoder.transformer_cells.{i}.ffn.ffn_2.weight'							)
        SCREAMING_SNAKE_CASE_      :  Dict    						=		check_and_map_params(
            bert_output.LayerNorm.bias    ,				f'encoder.transformer_cells.{i}.ffn.layer_norm.beta'							)
        SCREAMING_SNAKE_CASE_      :  str    						=		check_and_map_params(
            bert_output.LayerNorm.weight    ,				f'encoder.transformer_cells.{i}.ffn.layer_norm.gamma'							)

    # Save space and energy 🎄
    hf_bort_model.half()

    # Compare output of both models
    SCREAMING_SNAKE_CASE_      :  int    						=		RobertaTokenizer.from_pretrained('''roberta-base'''							)

    SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		tokenizer.encode_plus(__a							)['''input_ids''']

    # Get gluon output
    SCREAMING_SNAKE_CASE_      :  List[str]    						=		mx.nd.array([input_ids]							)
    SCREAMING_SNAKE_CASE_      :  int    						=		original_bort(inputs=__a    ,				token_types=[]							)

    # Get Transformer output (save and reload model again)
    hf_bort_model.save_pretrained(__a							)
    SCREAMING_SNAKE_CASE_      :  List[str]    						=		BertModel.from_pretrained(__a							)
    hf_bort_model.eval()

    SCREAMING_SNAKE_CASE_      :  Dict    						=		tokenizer.encode_plus(__a    ,				return_tensors='''pt'''							)
    SCREAMING_SNAKE_CASE_      :  List[Any]    						=		hf_bort_model(**__a							)[0]

    SCREAMING_SNAKE_CASE_      :  List[Any]    						=		output_gluon[0].asnumpy()
    SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		output_hf[0].detach().numpy()

    SCREAMING_SNAKE_CASE_      :  int    						=		np.max(np.abs(hf_layer - gluon_layer							)							).item()
    SCREAMING_SNAKE_CASE_      :  Tuple    						=		np.allclose(__a    ,				__a    ,				atol=1e-3							)

    if success:
        print('''✔️ Both model do output the same tensors'''							)
    else:
        print('''❌ Both model do **NOT** output the same tensors'''							)
        print('''Absolute difference is:'''    ,				__a							)


if __name__ == "__main__":
  UpperCAmelCase_   : Dict   			= argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      """--bort_checkpoint_path""", default=None, type=str, required=True, help="""Path the official Bort params file."""
  )
  parser.add_argument(
      """--pytorch_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
  )
  UpperCAmelCase_   : Optional[Any]   			= parser.parse_args()
  convert_bort_checkpoint_to_pytorch(args.bort_checkpoint_path, args.pytorch_dump_folder_path)










"""simple docstring"""






import tempfile

import torch

from diffusers import PNDMScheduler

from .test_schedulers import SchedulerCommonTest




class 							lowerCAmelCase__				(			UpperCAmelCase__ ):






      '''simple docstring'''

      __UpperCamelCase	 =  (PNDMScheduler,)
      __UpperCamelCase	 =  (("num_inference_steps", 5_0),)
      def _SCREAMING_SNAKE_CASE (						self     :  Any						,	**lowercase_     :  Optional[Any]):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  Any    						=		{
              '''num_train_timesteps''': 1000,
              '''beta_start''': 0.00_01,
              '''beta_end''': 0.02,
              '''beta_schedule''': '''linear''',
          }

          config.update(**lowercase_)
          return config
      def _SCREAMING_SNAKE_CASE (						self     :  Optional[Any]						,	lowercase_     :  List[str]=0						,	**lowercase_     :  Union[str, Any]):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  List[Any]    						=		dict(self.forward_default_kwargs)
          SCREAMING_SNAKE_CASE_      :  List[str]    						=		kwargs.pop('''num_inference_steps'''						,	lowercase_)
          SCREAMING_SNAKE_CASE_      :  Tuple    						=		self.dummy_sample
          SCREAMING_SNAKE_CASE_      :  List[Any]    						=		0.1 * sample
          SCREAMING_SNAKE_CASE_      :  Dict    						=		[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

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

              with tempfile.TemporaryDirectory() as tmpdirname:
                  scheduler.save_config(lowercase_)
                  SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		scheduler_class.from_pretrained(lowercase_)
                  new_scheduler.set_timesteps(lowercase_)
                  # copy over dummy past residuals
                  SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		dummy_past_residuals[:]

              SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		scheduler.step_prk(lowercase_						,	lowercase_						,	lowercase_						,	**lowercase_).prev_sample
              SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		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"

              SCREAMING_SNAKE_CASE_      :  List[str]    						=		scheduler.step_plms(lowercase_						,	lowercase_						,	lowercase_						,	**lowercase_).prev_sample
              SCREAMING_SNAKE_CASE_      :  Dict    						=		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 _SCREAMING_SNAKE_CASE (						self     :  Any):




          '''simple docstring'''


          pass
      def _SCREAMING_SNAKE_CASE (						self     :  List[Any]						,	lowercase_     :  List[str]=0						,	**lowercase_     :  List[str]):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		dict(self.forward_default_kwargs)
          SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		kwargs.pop('''num_inference_steps'''						,	lowercase_)
          SCREAMING_SNAKE_CASE_      :  List[str]    						=		self.dummy_sample
          SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		0.1 * sample
          SCREAMING_SNAKE_CASE_      :  int    						=		[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

          for scheduler_class in self.scheduler_classes:
              SCREAMING_SNAKE_CASE_      :  Dict    						=		self.get_scheduler_config()
              SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		scheduler_class(**lowercase_)
              scheduler.set_timesteps(lowercase_)

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

              with tempfile.TemporaryDirectory() as tmpdirname:
                  scheduler.save_config(lowercase_)
                  SCREAMING_SNAKE_CASE_      :  str    						=		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)
                  SCREAMING_SNAKE_CASE_      :  Any    						=		dummy_past_residuals[:]

              SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		scheduler.step_prk(lowercase_						,	lowercase_						,	lowercase_						,	**lowercase_).prev_sample
              SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		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"

              SCREAMING_SNAKE_CASE_      :  List[str]    						=		scheduler.step_plms(lowercase_						,	lowercase_						,	lowercase_						,	**lowercase_).prev_sample
              SCREAMING_SNAKE_CASE_      :  Tuple    						=		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 _SCREAMING_SNAKE_CASE (						self     :  str						,	**lowercase_     :  str):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  Tuple    						=		self.scheduler_classes[0]
          SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		self.get_scheduler_config(**lowercase_)
          SCREAMING_SNAKE_CASE_      :  List[Any]    						=		scheduler_class(**lowercase_)

          SCREAMING_SNAKE_CASE_      :  Dict    						=		10
          SCREAMING_SNAKE_CASE_      :  List[Any]    						=		self.dummy_model()
          SCREAMING_SNAKE_CASE_      :  str    						=		self.dummy_sample_deter
          scheduler.set_timesteps(lowercase_)

          for i, t in enumerate(scheduler.prk_timesteps):
              SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		model(lowercase_						,	lowercase_)
              SCREAMING_SNAKE_CASE_      :  str    						=		scheduler.step_prk(lowercase_						,	lowercase_						,	lowercase_).prev_sample

          for i, t in enumerate(scheduler.plms_timesteps):
              SCREAMING_SNAKE_CASE_      :  List[Any]    						=		model(lowercase_						,	lowercase_)
              SCREAMING_SNAKE_CASE_      :  List[str]    						=		scheduler.step_plms(lowercase_						,	lowercase_						,	lowercase_).prev_sample

          return sample
      def _SCREAMING_SNAKE_CASE (						self     :  Dict):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  Any    						=		dict(self.forward_default_kwargs)

          SCREAMING_SNAKE_CASE_      :  Dict    						=		kwargs.pop('''num_inference_steps'''						,	lowercase_)

          for scheduler_class in self.scheduler_classes:
              SCREAMING_SNAKE_CASE_      :  Tuple    						=		self.get_scheduler_config()
              SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		scheduler_class(**lowercase_)

              SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		self.dummy_sample
              SCREAMING_SNAKE_CASE_      :  Any    						=		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'''):
                  SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		num_inference_steps

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

              SCREAMING_SNAKE_CASE_      :  Dict    						=		scheduler.step_prk(lowercase_						,	0						,	lowercase_						,	**lowercase_).prev_sample
              SCREAMING_SNAKE_CASE_      :  List[Any]    						=		scheduler.step_prk(lowercase_						,	1						,	lowercase_						,	**lowercase_).prev_sample

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

              SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		scheduler.step_plms(lowercase_						,	0						,	lowercase_						,	**lowercase_).prev_sample
              SCREAMING_SNAKE_CASE_      :  Any    						=		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 _SCREAMING_SNAKE_CASE (						self     :  str):




          '''simple docstring'''


          for timesteps in [100, 1000]:
              self.check_over_configs(num_train_timesteps=lowercase_)
      def _SCREAMING_SNAKE_CASE (						self     :  Dict):




          '''simple docstring'''


          for steps_offset in [0, 1]:
              self.check_over_configs(steps_offset=lowercase_)

          SCREAMING_SNAKE_CASE_      :  Dict    						=		self.scheduler_classes[0]
          SCREAMING_SNAKE_CASE_      :  List[str]    						=		self.get_scheduler_config(steps_offset=1)
          SCREAMING_SNAKE_CASE_      :  Tuple    						=		scheduler_class(**lowercase_)
          scheduler.set_timesteps(10)
          assert torch.equal(
              scheduler.timesteps						,	torch.LongTensor(
                  [901, 851, 851, 801, 801, 751, 751, 701, 701, 651, 651, 601, 601, 501, 401, 301, 201, 101, 1])						,	)
      def _SCREAMING_SNAKE_CASE (						self     :  Any):




          '''simple docstring'''


          for beta_start, beta_end in zip([0.00_01, 0.0_01]						,	[0.0_02, 0.02]):
              self.check_over_configs(beta_start=lowercase_						,	beta_end=lowercase_)
      def _SCREAMING_SNAKE_CASE (						self     :  List[Any]):




          '''simple docstring'''


          for schedule in ["linear", "squaredcos_cap_v2"]:
              self.check_over_configs(beta_schedule=lowercase_)
      def _SCREAMING_SNAKE_CASE (						self     :  Any):




          '''simple docstring'''


          for prediction_type in ["epsilon", "v_prediction"]:
              self.check_over_configs(prediction_type=lowercase_)
      def _SCREAMING_SNAKE_CASE (						self     :  int):




          '''simple docstring'''


          for t in [1, 5, 10]:
              self.check_over_forward(time_step=lowercase_)
      def _SCREAMING_SNAKE_CASE (						self     :  Optional[Any]):




          '''simple docstring'''


          for t, num_inference_steps in zip([1, 5, 10]						,	[10, 50, 100]):
              self.check_over_forward(num_inference_steps=lowercase_)
      def _SCREAMING_SNAKE_CASE (						self     :  Any):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		27

          for scheduler_class in self.scheduler_classes:
              SCREAMING_SNAKE_CASE_      :  List[Any]    						=		self.dummy_sample
              SCREAMING_SNAKE_CASE_      :  str    						=		0.1 * sample

              SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		self.get_scheduler_config()
              SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		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]):
                  SCREAMING_SNAKE_CASE_      :  int    						=		scheduler.step_prk(lowercase_						,	lowercase_						,	lowercase_).prev_sample
      def _SCREAMING_SNAKE_CASE (						self     :  List[Any]):




          '''simple docstring'''


          with self.assertRaises(lowercase_):
              SCREAMING_SNAKE_CASE_      :  int    						=		self.scheduler_classes[0]
              SCREAMING_SNAKE_CASE_      :  List[str]    						=		self.get_scheduler_config()
              SCREAMING_SNAKE_CASE_      :  Dict    						=		scheduler_class(**lowercase_)

              scheduler.step_plms(self.dummy_sample						,	1						,	self.dummy_sample).prev_sample
      def _SCREAMING_SNAKE_CASE (						self     :  Optional[int]):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  Any    						=		self.full_loop()
          SCREAMING_SNAKE_CASE_      :  List[Any]    						=		torch.sum(torch.abs(lowercase_))
          SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		torch.mean(torch.abs(lowercase_))

          assert abs(result_sum.item() - 1_98.13_18) < 1e-2
          assert abs(result_mean.item() - 0.25_80) < 1e-3
      def _SCREAMING_SNAKE_CASE (						self     :  Optional[int]):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  str    						=		self.full_loop(prediction_type='''v_prediction''')
          SCREAMING_SNAKE_CASE_      :  str    						=		torch.sum(torch.abs(lowercase_))
          SCREAMING_SNAKE_CASE_      :  Any    						=		torch.mean(torch.abs(lowercase_))

          assert abs(result_sum.item() - 67.39_86) < 1e-2
          assert abs(result_mean.item() - 0.08_78) < 1e-3
      def _SCREAMING_SNAKE_CASE (						self     :  Optional[Any]):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  Tuple    						=		self.full_loop(set_alpha_to_one=lowercase_						,	beta_start=0.01)
          SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		torch.sum(torch.abs(lowercase_))
          SCREAMING_SNAKE_CASE_      :  Any    						=		torch.mean(torch.abs(lowercase_))

          assert abs(result_sum.item() - 2_30.03_99) < 1e-2
          assert abs(result_mean.item() - 0.29_95) < 1e-3



      def _SCREAMING_SNAKE_CASE (						self     :  Optional[Any]):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		self.full_loop(set_alpha_to_one=lowercase_						,	beta_start=0.01)
          SCREAMING_SNAKE_CASE_      :  int    						=		torch.sum(torch.abs(lowercase_))
          SCREAMING_SNAKE_CASE_      :  List[str]    						=		torch.mean(torch.abs(lowercase_))

          assert abs(result_sum.item() - 1_86.94_82) < 1e-2
          assert abs(result_mean.item() - 0.24_34) < 1e-3


