

import gc
import importlib.metadata
import tempfile
import unittest

from packaging import version

from transformers import (
    AutoModel,
    AutoModelForCausalLM,
    AutoModelForSeqaSeqLM,
    AutoModelForSequenceClassification,
    AutoTokenizer,
    BitsAndBytesConfig,
    pipeline,
)
from transformers.testing_utils import (
    is_torch_available,
    require_accelerate,
    require_bitsandbytes,
    require_torch,
    require_torch_gpu,
    require_torch_multi_gpu,
    slow,
)





def    __lowerCamelCase      (					UpperCamelCase__ ):







							'''simple docstring'''






							if model.config.model_type == "gpt2":
														return model.transformer.h[0].mlp.c_fc
							return model.transformer.h[0].mlp.dense_ah_to_h


if is_torch_available():
					import torch
					import torch.nn as nn







					class 					lowercase							(			nn.Module     ):




									def __init__(   self ,  snake_case ,  snake_case			):
																super().__init__()
																snake_case_     							=       module
																snake_case_     							=       nn.Sequential(
																    nn.Linear(module.in_features ,  __UpperCamelCase ,  bias=__UpperCamelCase			) ,  nn.Linear(__UpperCamelCase ,  module.out_features ,  bias=__UpperCamelCase			) ,  )
																snake_case_     							=       (2.0 / (5 * min(module.in_features ,  module.out_features			))) ** 0.5
																nn.init.normal_(self.adapter[0].weight ,  std=__UpperCamelCase			)
																nn.init.zeros_(self.adapter[1].weight			)
																self.adapter.to(module.weight.device			)




									def        a				(   self ,  snake_case ,  *snake_case ,  **snake_case			):
																return self.module(__UpperCamelCase ,  *__UpperCamelCase ,  **__UpperCamelCase			) + self.adapter(__UpperCamelCase			)







@require_bitsandbytes
@require_accelerate
@require_torch
@require_torch_gpu
@slow
class 					lowercase							(			unittest.TestCase     ):
				# We keep the constants inside the init function and model loading inside setUp function

				# We need to test on relatively large models (aka >1b parameters otherwise the quantiztion may not work as expected)
				# Therefore here we use only bloom-1b3 to test our module
				__SCREAMING_SNAKE_CASE   :					List[Any]			     =     '''bigscience/bloom-1b7'''

				# Constant values
				__SCREAMING_SNAKE_CASE   :					List[str]			     =     2.109659552692574

				__SCREAMING_SNAKE_CASE   :					List[str]			     =     '''Hello my name is'''
				__SCREAMING_SNAKE_CASE   :					Dict			     =     set()
				EXPECTED_OUTPUTS.add('''Hello my name is John and I am a professional photographer. I'''     )
				EXPECTED_OUTPUTS.add('''Hello my name is John.\nI am a friend of your father.\n'''     )
				EXPECTED_OUTPUTS.add('''Hello my name is John Doe, I am a student at the University'''     )
				__SCREAMING_SNAKE_CASE   :					List[str]			     =     10




				def        a				(   self			):
											# Models and tokenizer
											snake_case_     							=       AutoTokenizer.from_pretrained(self.model_name			)







class 					lowercase							(			a_     ):




				def        a				(   self			):
											super().setUp()

											# Models and tokenizer
											snake_case_     							=       AutoModelForCausalLM.from_pretrained(
											    self.model_name ,  torch_dtype=torch.floataa ,  device_map='auto'			)
											snake_case_     							=       AutoModelForCausalLM.from_pretrained(self.model_name ,  load_in_abit=__UpperCamelCase ,  device_map='auto'			)




				def        a				(   self			):
											del self.model_fpaa
											del self.model_abit

											gc.collect()
											torch.cuda.empty_cache()




				def        a				(   self			):
											snake_case_     							=       self.model_abit.config

											self.assertTrue(hasattr(__UpperCamelCase ,  'quantization_config'			)			)

											snake_case_     							=       config.to_dict()
											snake_case_     							=       config.to_diff_dict()

											snake_case_     							=       config.to_json_string()




				def        a				(   self			):
											from bitsandbytes.nn import Paramsabit

											snake_case_     							=       self.model_fpaa.get_memory_footprint()
											snake_case_     							=       self.model_abit.get_memory_footprint()

											self.assertAlmostEqual(mem_fpaa / mem_abit ,  self.EXPECTED_RELATIVE_DIFFERENCE			)
											snake_case_     							=       get_some_linear_layer(self.model_abit			)
											self.assertTrue(linear.weight.__class__ == Paramsabit			)




				def        a				(   self			):
											from transformers import TaPreTrainedModel

											self.model_fpaa.get_memory_footprint()
											self.model_abit.get_memory_footprint()

											for name, module in self.model_abit.named_modules():
																		if isinstance(__UpperCamelCase ,  torch.nn.Linear			):
																									if name not in ["lm_head"] + TaPreTrainedModel._keep_in_fpaa_modules:
																																# 4-bit parameters are packed in uint8 variables
																																self.assertTrue(module.weight.dtype == torch.uinta			)




				def        a				(   self			):
											snake_case_     							=       self.tokenizer(self.input_text ,  return_tensors='pt'			)
											snake_case_     							=       self.model_abit.generate(input_ids=encoded_input['input_ids'].to(0			) ,  max_new_tokens=10			)

											self.assertIn(self.tokenizer.decode(output_sequences[0] ,  skip_special_tokens=__UpperCamelCase			) ,  self.EXPECTED_OUTPUTS			)




				def        a				(   self			):
											snake_case_     							=       BitsAndBytesConfig()
											snake_case_     							=       True

											snake_case_     							=       AutoModelForCausalLM.from_pretrained(
											    self.model_name ,  quantization_config=__UpperCamelCase ,  device_map='auto'			)

											snake_case_     							=       self.tokenizer(self.input_text ,  return_tensors='pt'			)
											snake_case_     							=       model_abit_from_config.generate(
											    input_ids=encoded_input['input_ids'].to(0			) ,  max_new_tokens=10			)

											self.assertIn(self.tokenizer.decode(output_sequences[0] ,  skip_special_tokens=__UpperCamelCase			) ,  self.EXPECTED_OUTPUTS			)




				def        a				(   self			):
											with self.assertRaises(__UpperCamelCase			), tempfile.TemporaryDirectory() as tmpdirname:
																		self.model_abit.save_pretrained(__UpperCamelCase			)




				def        a				(   self			):
											snake_case_     							=       BitsAndBytesConfig()

											with self.assertRaises(__UpperCamelCase			):
																		snake_case_     							=       AutoModelForCausalLM.from_pretrained(
																		    self.model_name ,  quantization_config=__UpperCamelCase ,  load_in_abit=__UpperCamelCase ,  device_map='auto' ,  bnb_abit_quant_type='nf4' ,  )




				def        a				(   self			):
											with self.assertRaises(__UpperCamelCase			):
																		# Tries with `str`
																		self.model_abit.to('cpu'			)

											with self.assertRaises(__UpperCamelCase			):
																		# Tries with a `dtype``
																		self.model_abit.to(torch.floataa			)

											with self.assertRaises(__UpperCamelCase			):
																		# Tries with a `device`
																		self.model_abit.to(torch.device('cuda:0'			)			)

											with self.assertRaises(__UpperCamelCase			):
																		# Tries with a `device`
																		self.model_abit.float()

											with self.assertRaises(__UpperCamelCase			):
																		# Tries with a `device`
																		self.model_abit.half()

											# Test if we did not break anything
											snake_case_     							=       self.tokenizer(self.input_text ,  return_tensors='pt'			)

											snake_case_     							=       self.model_fpaa.to(torch.floataa			)
											snake_case_     							=       self.model_fpaa.generate(input_ids=encoded_input['input_ids'].to(0			) ,  max_new_tokens=10			)

											# Check this does not throw an error
											snake_case_     							=       self.model_fpaa.to('cpu'			)

											# Check this does not throw an error
											snake_case_     							=       self.model_fpaa.half()

											# Check this does not throw an error
											snake_case_     							=       self.model_fpaa.float()




				def        a				(   self			):
											snake_case_     							=       AutoModelForSeqaSeqLM.from_pretrained('t5-small' ,  load_in_abit=__UpperCamelCase ,  device_map='auto'			)
											self.assertTrue(model.decoder.block[0].layer[2].DenseReluDense.wo.weight.dtype == torch.floataa			)







@require_bitsandbytes
@require_accelerate
@require_torch
@require_torch_gpu
@slow
class 					lowercase							(			unittest.TestCase     ):




				@classmethod
				def        a				(   cls			):
											snake_case_     							=       't5-small'
											snake_case_     							=       'google/flan-t5-small'  # flan-t5 uses dense-act instead of dense-relu-dense
											snake_case_     							=       AutoTokenizer.from_pretrained(cls.model_name			)
											snake_case_     							=       'Translate in German: Hello, my dog is cute'




				def        a				(   self			):
											gc.collect()
											torch.cuda.empty_cache()




				def        a				(   self			):
											from transformers import TaForConditionalGeneration

											snake_case_     							=       TaForConditionalGeneration._keep_in_fpaa_modules
											snake_case_     							=       None

											# test with `t5-small`
											snake_case_     							=       TaForConditionalGeneration.from_pretrained(self.model_name ,  load_in_abit=__UpperCamelCase ,  device_map='auto'			)
											snake_case_     							=       self.tokenizer(self.input_text ,  return_tensors='pt'			).to(0			)
											snake_case_     							=       model.generate(**__UpperCamelCase			)

											# test with `flan-t5-small`
											snake_case_     							=       TaForConditionalGeneration.from_pretrained(
											    self.dense_act_model_name ,  load_in_abit=__UpperCamelCase ,  device_map='auto'			)
											snake_case_     							=       self.tokenizer(self.input_text ,  return_tensors='pt'			).to(0			)
											snake_case_     							=       model.generate(**__UpperCamelCase			)
											snake_case_     							=       modules




				def        a				(   self			):
											import bitsandbytes as bnb

											from transformers import TaForConditionalGeneration

											# test with `t5-small`
											snake_case_     							=       TaForConditionalGeneration.from_pretrained(self.model_name ,  load_in_abit=__UpperCamelCase ,  device_map='auto'			)

											# there was a bug with decoders - this test checks that it is fixed
											self.assertTrue(isinstance(model.decoder.block[0].layer[0].SelfAttention.q ,  bnb.nn.Linearabit			)			)

											snake_case_     							=       self.tokenizer(self.input_text ,  return_tensors='pt'			).to(0			)
											snake_case_     							=       model.generate(**__UpperCamelCase			)

											# test with `flan-t5-small`
											snake_case_     							=       TaForConditionalGeneration.from_pretrained(
											    self.dense_act_model_name ,  load_in_abit=__UpperCamelCase ,  device_map='auto'			)
											snake_case_     							=       self.tokenizer(self.input_text ,  return_tensors='pt'			).to(0			)
											snake_case_     							=       model.generate(**__UpperCamelCase			)







class 					lowercase							(			a_     ):




				def        a				(   self			):
											super().setUp()
											# model_name
											snake_case_     							=       'bigscience/bloom-560m'
											snake_case_     							=       't5-small'

											# Different types of model

											snake_case_     							=       AutoModel.from_pretrained(self.model_name ,  load_in_abit=__UpperCamelCase ,  device_map='auto'			)
											# Sequence classification model
											snake_case_     							=       AutoModelForSequenceClassification.from_pretrained(
											    self.model_name ,  load_in_abit=__UpperCamelCase ,  device_map='auto'			)
											# CausalLM model
											snake_case_     							=       AutoModelForCausalLM.from_pretrained(self.model_name ,  load_in_abit=__UpperCamelCase ,  device_map='auto'			)
											# Seq2seq model
											snake_case_     							=       AutoModelForSeqaSeqLM.from_pretrained(
											    self.seq_to_seq_name ,  load_in_abit=__UpperCamelCase ,  device_map='auto'			)




				def        a				(   self			):
											del self.base_model
											del self.sequence_model
											del self.model_abit
											del self.seq_to_seq_model

											gc.collect()
											torch.cuda.empty_cache()




				def        a				(   self			):
											from bitsandbytes.nn import Paramsabit

											self.assertTrue(self.base_model.h[-1].mlp.dense_ah_to_h.weight.__class__ == Paramsabit			)

											# Other heads should be nn.Parameter
											self.assertTrue(self.model_abit.lm_head.weight.__class__ == torch.nn.Parameter			)
											self.assertTrue(self.sequence_model.score.weight.__class__ == torch.nn.Parameter			)
											self.assertTrue(self.seq_to_seq_model.lm_head.weight.__class__ == torch.nn.Parameter			)







class 					lowercase							(			a_     ):




				def        a				(   self			):
											super().setUp()




				def        a				(   self			):
											del self.pipe

											gc.collect()
											torch.cuda.empty_cache()




				def        a				(   self			):
											snake_case_     							=       pipeline(
											    'text-generation' ,  model=self.model_name ,  model_kwargs={'device_map': 'auto', 'load_in_4bit': True, 'torch_dtype': torch.floataa} ,  max_new_tokens=self.MAX_NEW_TOKENS ,  )

											# Real second forward pass
											snake_case_     							=       self.pipe(self.input_text			)
											self.assertIn(pipeline_output[0]['generated_text'] ,  self.EXPECTED_OUTPUTS			)







@require_torch_multi_gpu
class 					lowercase							(			a_     ):




				def        a				(   self			):
											super().setUp()




				def        a				(   self			):
											snake_case_     							=       AutoModelForCausalLM.from_pretrained(
											    self.model_name ,  load_in_abit=__UpperCamelCase ,  device_map='balanced'			)

											# Check correct device map
											self.assertEqual(set(model_parallel.hf_device_map.values()			) ,  {0, 1}			)

											# Check that inference pass works on the model
											snake_case_     							=       self.tokenizer(self.input_text ,  return_tensors='pt'			)

											# Second real batch
											snake_case_     							=       model_parallel.generate(input_ids=encoded_input['input_ids'].to(0			) ,  max_new_tokens=10			)
											self.assertIn(self.tokenizer.decode(output_parallel[0] ,  skip_special_tokens=__UpperCamelCase			) ,  self.EXPECTED_OUTPUTS			)







class 					lowercase							(			a_     ):




				def        a				(   self			):
											snake_case_     							=       'facebook/opt-350m'
											super().setUp()




				def        a				(   self			):
											if version.parse(importlib.metadata.version('bitsandbytes'			)			) < version.parse('0.37.0'			):
																		return

											# Step 1: freeze all parameters
											snake_case_     							=       AutoModelForCausalLM.from_pretrained(self.model_name ,  load_in_abit=__UpperCamelCase			)

											self.assertEqual(set(model.hf_device_map.values()			) ,  {torch.cuda.current_device()}			)

											for param in model.parameters():
																		snake_case_     							=       False  # freeze the model - train adapters later
																		if param.ndim == 1:
																									# cast the small parameters (e.g. layernorm) to fp32 for stability
																									snake_case_     							=       param.data.to(torch.floataa			)

        # Step 2: add adapters
											for _, module in model.named_modules():
																		if "OPTAttention" in repr(type(__UpperCamelCase			)			):
																									snake_case_     							=       LoRALayer(module.q_proj ,  rank=16			)
																									snake_case_     							=       LoRALayer(module.k_proj ,  rank=16			)
																									snake_case_     							=       LoRALayer(module.v_proj ,  rank=16			)

        # Step 3: dummy batch
											snake_case_     							=       self.tokenizer('Test batch ' ,  return_tensors='pt'			).to(0			)

											# Step 4: Check if the gradient is not None
											with torch.cuda.amp.autocast():
																		snake_case_     							=       model.forward(**__UpperCamelCase			)
																		out.logits.norm().backward()

											for module in model.modules():
																		if isinstance(__UpperCamelCase ,  __UpperCamelCase			):
																									self.assertTrue(module.adapter[1].weight.grad is not None			)
																									self.assertTrue(module.adapter[1].weight.grad.norm().item() > 0			)
																		elif isinstance(__UpperCamelCase ,  nn.Embedding			):
																									self.assertTrue(module.weight.grad is None			)







class 					lowercase							(			a_     ):
				__SCREAMING_SNAKE_CASE   :					Dict			     =     '''gpt2-xl'''
				__SCREAMING_SNAKE_CASE   :					List[str]			     =     3.3191854854152187












import tempfile

import torch

from diffusers import PNDMScheduler

from .test_schedulers import SchedulerCommonTest



class 				_UpperCamelCase      (a_      ):
      snake_case_	=				(PNDMScheduler,)
      snake_case_	=				(("""num_inference_steps""", 50),)


      def 			__UpperCAmelCase		(						self       ,    **__UpperCamelCase						)->						int:
           __lowerCAmelCase             =       {
               "num_train_timesteps": 1_0_0_0,
               "beta_start": 0.0_0_0_1,
               "beta_end": 0.0_2,
               "beta_schedule": "linear",
           }

           config.update(**__UpperCamelCase						)
           return config


      def 			__UpperCAmelCase		(						self       ,    __UpperCamelCase=0       ,    **__UpperCamelCase						)->						Optional[int]:
           __lowerCAmelCase             =       dict(self.forward_default_kwargs						)
           __lowerCAmelCase             =       kwargs.pop("num_inference_steps"       ,    __UpperCamelCase						)
           __lowerCAmelCase             =       self.dummy_sample
           __lowerCAmelCase             =       0.1 * sample
           __lowerCAmelCase             =       [residual + 0.2, residual + 0.1_5, residual + 0.1, residual + 0.0_5]

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

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

                __lowerCAmelCase             =       scheduler.step_prk(__UpperCamelCase       ,    __UpperCamelCase       ,    __UpperCamelCase       ,    **__UpperCamelCase						).prev_sample
                __lowerCAmelCase             =       new_scheduler.step_prk(__UpperCamelCase       ,    __UpperCamelCase       ,    __UpperCamelCase       ,    **__UpperCamelCase						).prev_sample

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

                __lowerCAmelCase             =       scheduler.step_plms(__UpperCamelCase       ,    __UpperCamelCase       ,    __UpperCamelCase       ,    **__UpperCamelCase						).prev_sample
                __lowerCAmelCase             =       new_scheduler.step_plms(__UpperCamelCase       ,    __UpperCamelCase       ,    __UpperCamelCase       ,    **__UpperCamelCase						).prev_sample

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


      def 			__UpperCAmelCase		(						self						)->						Dict:
           pass


      def 			__UpperCAmelCase		(						self       ,    __UpperCamelCase=0       ,    **__UpperCamelCase						)->						Dict:
           __lowerCAmelCase             =       dict(self.forward_default_kwargs						)
           __lowerCAmelCase             =       kwargs.pop("num_inference_steps"       ,    __UpperCamelCase						)
           __lowerCAmelCase             =       self.dummy_sample
           __lowerCAmelCase             =       0.1 * sample
           __lowerCAmelCase             =       [residual + 0.2, residual + 0.1_5, residual + 0.1, residual + 0.0_5]

           for scheduler_class in self.scheduler_classes:
                __lowerCAmelCase             =       self.get_scheduler_config()
                __lowerCAmelCase             =       scheduler_class(**__UpperCamelCase						)
                scheduler.set_timesteps(__UpperCamelCase						)

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

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

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

                __lowerCAmelCase             =       scheduler.step_prk(__UpperCamelCase       ,    __UpperCamelCase       ,    __UpperCamelCase       ,    **__UpperCamelCase						).prev_sample
                __lowerCAmelCase             =       new_scheduler.step_prk(__UpperCamelCase       ,    __UpperCamelCase       ,    __UpperCamelCase       ,    **__UpperCamelCase						).prev_sample

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

                __lowerCAmelCase             =       scheduler.step_plms(__UpperCamelCase       ,    __UpperCamelCase       ,    __UpperCamelCase       ,    **__UpperCamelCase						).prev_sample
                __lowerCAmelCase             =       new_scheduler.step_plms(__UpperCamelCase       ,    __UpperCamelCase       ,    __UpperCamelCase       ,    **__UpperCamelCase						).prev_sample

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


      def 			__UpperCAmelCase		(						self       ,    **__UpperCamelCase						)->						Tuple:
           __lowerCAmelCase             =       self.scheduler_classes[0]
           __lowerCAmelCase             =       self.get_scheduler_config(**__UpperCamelCase						)
           __lowerCAmelCase             =       scheduler_class(**__UpperCamelCase						)

           __lowerCAmelCase             =       1_0
           __lowerCAmelCase             =       self.dummy_model()
           __lowerCAmelCase             =       self.dummy_sample_deter
           scheduler.set_timesteps(__UpperCamelCase						)

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

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

           return sample


      def 			__UpperCAmelCase		(						self						)->						int:
           __lowerCAmelCase             =       dict(self.forward_default_kwargs						)

           __lowerCAmelCase             =       kwargs.pop("num_inference_steps"       ,    __UpperCamelCase						)

           for scheduler_class in self.scheduler_classes:
                __lowerCAmelCase             =       self.get_scheduler_config()
                __lowerCAmelCase             =       scheduler_class(**__UpperCamelCase						)

                __lowerCAmelCase             =       self.dummy_sample
                __lowerCAmelCase             =       0.1 * sample

                if num_inference_steps is not None and hasattr(__UpperCamelCase       ,    "set_timesteps"						):
                     scheduler.set_timesteps(__UpperCamelCase						)
                elif num_inference_steps is not None and not hasattr(__UpperCamelCase       ,    "set_timesteps"						):
                     __lowerCAmelCase             =       num_inference_steps

                # copy over dummy past residuals (must be done after set_timesteps)
                __lowerCAmelCase             =       [residual + 0.2, residual + 0.1_5, residual + 0.1, residual + 0.0_5]
                __lowerCAmelCase             =       dummy_past_residuals[:]

                __lowerCAmelCase             =       scheduler.step_prk(__UpperCamelCase       ,    0       ,    __UpperCamelCase       ,    **__UpperCamelCase						).prev_sample
                __lowerCAmelCase             =       scheduler.step_prk(__UpperCamelCase       ,    1       ,    __UpperCamelCase       ,    **__UpperCamelCase						).prev_sample

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

                __lowerCAmelCase             =       scheduler.step_plms(__UpperCamelCase       ,    0       ,    __UpperCamelCase       ,    **__UpperCamelCase						).prev_sample
                __lowerCAmelCase             =       scheduler.step_plms(__UpperCamelCase       ,    1       ,    __UpperCamelCase       ,    **__UpperCamelCase						).prev_sample

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


      def 			__UpperCAmelCase		(						self						)->						List[str]:
           for timesteps in [1_0_0, 1_0_0_0]:
                self.check_over_configs(num_train_timesteps=__UpperCamelCase						)


      def 			__UpperCAmelCase		(						self						)->						Any:
           for steps_offset in [0, 1]:
                self.check_over_configs(steps_offset=__UpperCamelCase						)

           __lowerCAmelCase             =       self.scheduler_classes[0]
           __lowerCAmelCase             =       self.get_scheduler_config(steps_offset=1						)
           __lowerCAmelCase             =       scheduler_class(**__UpperCamelCase						)
           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 			__UpperCAmelCase		(						self						)->						int:
           for beta_start, beta_end in zip([0.0_0_0_1, 0.0_0_1]       ,    [0.0_0_2, 0.0_2]						):
                self.check_over_configs(beta_start=__UpperCamelCase       ,    beta_end=__UpperCamelCase						)


      def 			__UpperCAmelCase		(						self						)->						Optional[int]:
           for schedule in ["linear", "squaredcos_cap_v2"]:
                self.check_over_configs(beta_schedule=__UpperCamelCase						)


      def 			__UpperCAmelCase		(						self						)->						Dict:
           for prediction_type in ["epsilon", "v_prediction"]:
                self.check_over_configs(prediction_type=__UpperCamelCase						)


      def 			__UpperCAmelCase		(						self						)->						List[Any]:
           for t in [1, 5, 1_0]:
                self.check_over_forward(time_step=__UpperCamelCase						)


      def 			__UpperCAmelCase		(						self						)->						Optional[Any]:
           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=__UpperCamelCase						)


      def 			__UpperCAmelCase		(						self						)->						int:
           # earlier version of set_timesteps() caused an error indexing alpha's with inference steps as power of 3
           __lowerCAmelCase             =       2_7

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

                __lowerCAmelCase             =       self.get_scheduler_config()
                __lowerCAmelCase             =       scheduler_class(**__UpperCamelCase						)

                scheduler.set_timesteps(__UpperCamelCase						)

                # 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]						):
                     __lowerCAmelCase             =       scheduler.step_prk(__UpperCamelCase       ,    __UpperCamelCase       ,    __UpperCamelCase						).prev_sample


      def 			__UpperCAmelCase		(						self						)->						Dict:
           with self.assertRaises(__UpperCamelCase						):
                __lowerCAmelCase             =       self.scheduler_classes[0]
                __lowerCAmelCase             =       self.get_scheduler_config()
                __lowerCAmelCase             =       scheduler_class(**__UpperCamelCase						)

                scheduler.step_plms(self.dummy_sample       ,    1       ,    self.dummy_sample						).prev_sample


      def 			__UpperCAmelCase		(						self						)->						Any:
           __lowerCAmelCase             =       self.full_loop()
           __lowerCAmelCase             =       torch.sum(torch.abs(__UpperCamelCase						)						)
           __lowerCAmelCase             =       torch.mean(torch.abs(__UpperCamelCase						)						)

           assert abs(result_sum.item() - 1_9_8.1_3_1_8						) < 1e-2
           assert abs(result_mean.item() - 0.2_5_8_0						) < 1e-3


      def 			__UpperCAmelCase		(						self						)->						str:
           __lowerCAmelCase             =       self.full_loop(prediction_type="v_prediction"						)
           __lowerCAmelCase             =       torch.sum(torch.abs(__UpperCamelCase						)						)
           __lowerCAmelCase             =       torch.mean(torch.abs(__UpperCamelCase						)						)

           assert abs(result_sum.item() - 6_7.3_9_8_6						) < 1e-2
           assert abs(result_mean.item() - 0.0_8_7_8						) < 1e-3


      def 			__UpperCAmelCase		(						self						)->						Any:
           # We specify different beta, so that the first alpha is 0.99
           __lowerCAmelCase             =       self.full_loop(set_alpha_to_one=__UpperCamelCase       ,    beta_start=0.0_1						)
           __lowerCAmelCase             =       torch.sum(torch.abs(__UpperCamelCase						)						)
           __lowerCAmelCase             =       torch.mean(torch.abs(__UpperCamelCase						)						)

           assert abs(result_sum.item() - 2_3_0.0_3_9_9						) < 1e-2
           assert abs(result_mean.item() - 0.2_9_9_5						) < 1e-3


      def 			__UpperCAmelCase		(						self						)->						Optional[Any]:
           # We specify different beta, so that the first alpha is 0.99
           __lowerCAmelCase             =       self.full_loop(set_alpha_to_one=__UpperCamelCase       ,    beta_start=0.0_1						)
           __lowerCAmelCase             =       torch.sum(torch.abs(__UpperCamelCase						)						)
           __lowerCAmelCase             =       torch.mean(torch.abs(__UpperCamelCase						)						)

           assert abs(result_sum.item() - 1_8_6.9_4_8_2						) < 1e-2
           assert abs(result_mean.item() - 0.2_4_3_4						) < 1e-3






