

'''simple docstring'''

from __future__ import annotations

import unittest

from transformers import AutoTokenizer, GPTJConfig, is_tf_available
from transformers.testing_utils import require_tf, slow, tooslow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
from ...utils.test_modeling_tf_core import TFCoreModelTesterMixin


if is_tf_available():
       import tensorflow as tf

       from transformers.models.gptj.modeling_tf_gptj import (
           TFGPTJForCausalLM,
           TFGPTJForQuestionAnswering,
           TFGPTJForSequenceClassification,
           TFGPTJModel,
           shape_list,
       )


class 					lowerCAmelCase       :







       def __init__(							self     :	int  , __lowercase     :	List[str] ):







            """simple docstring"""







            __lowercase     						=parent
            __lowercase     						=13
            __lowercase     						=7
            __lowercase     						=True
            __lowercase     						=True
            __lowercase     						=True
            __lowercase     						=True
            __lowercase     						=True
            __lowercase     						=99
            __lowercase     						=32
            __lowercase     						=4
            __lowercase     						=2
            __lowercase     						=4
            __lowercase     						=37
            __lowercase     						='gelu'
            __lowercase     						=0.1
            __lowercase     						=0.1
            __lowercase     						=512
            __lowercase     						=16
            __lowercase     						=2
            __lowercase     						=0.0_2
            __lowercase     						=3
            __lowercase     						=4
            __lowercase     						=None
            __lowercase     						=self.vocab_size - 1
            __lowercase     						=self.vocab_size - 1
            __lowercase     						=self.vocab_size - 1







       def      snake_case				(							self     :	Optional[int] ):







            """simple docstring"""







            __lowercase     						=ids_tensor([self.batch_size, self.seq_length]  , self.vocab_size )

            __lowercase     						=None
            if self.use_input_mask:
                 __lowercase     						=random_attention_mask([self.batch_size, self.seq_length] )

            __lowercase     						=None
            if self.use_token_type_ids:
                 __lowercase     						=ids_tensor([self.batch_size, self.seq_length]  , self.type_vocab_size )

            __lowercase     						=None
            if self.use_mc_token_ids:
                 __lowercase     						=ids_tensor([self.batch_size, self.num_choices]  , self.seq_length )

            __lowercase     						=None
            __lowercase     						=None
            __lowercase     						=None
            if self.use_labels:
                 __lowercase     						=ids_tensor([self.batch_size]  , self.type_sequence_label_size )
                 __lowercase     						=ids_tensor([self.batch_size, self.seq_length]  , self.num_labels )
                 __lowercase     						=ids_tensor([self.batch_size]  , self.num_choices )

            __lowercase     						=GPTJConfig(
                vocab_size=self.vocab_size  , n_embd=self.hidden_size  , n_layer=self.num_hidden_layers  , n_head=self.num_attention_heads  , intermediate_size=self.intermediate_size  , hidden_act=self.hidden_act  , hidden_dropout_prob=self.hidden_dropout_prob  , attention_probs_dropout_prob=self.attention_probs_dropout_prob  , n_positions=self.max_position_embeddings  , type_vocab_size=self.type_vocab_size  , initializer_range=self.initializer_range  , bos_token_id=self.bos_token_id  , eos_token_id=self.eos_token_id  , pad_token_id=self.pad_token_id  , rotary_dim=self.rotary_dim  , return_dict=__lowercase  , )

            __lowercase     						=ids_tensor([self.num_hidden_layers, self.num_attention_heads]  , 2 )

            return (
                config,
                input_ids,
                input_mask,
                head_mask,
                token_type_ids,
                mc_token_ids,
                sequence_labels,
                token_labels,
                choice_labels,
            )







       def      snake_case				(							self     :	Optional[Any]  , __lowercase     :	Tuple  , __lowercase     :	Dict  , __lowercase     :	List[Any]  , __lowercase     :	Tuple  , __lowercase     :	List[Any]  , *__lowercase     :	int ):







            """simple docstring"""







            __lowercase     						=TFGPTJModel(config=__lowercase )
            __lowercase     						={
                'input_ids': input_ids,
                'attention_mask': input_mask,
                'token_type_ids': token_type_ids,
            }
            __lowercase     						=model(__lowercase )

            __lowercase     						=[input_ids, None, input_mask]  # None is the input for 'past'
            __lowercase     						=model(__lowercase )

            __lowercase     						=model(__lowercase )

            self.parent.assertEqual(result.last_hidden_state.shape  , (self.batch_size, self.seq_length, self.hidden_size) )







       def      snake_case				(							self     :	Union[str, Any]  , __lowercase     :	Optional[int]  , __lowercase     :	int  , __lowercase     :	Any  , __lowercase     :	List[str]  , __lowercase     :	List[Any]  , *__lowercase     :	Union[str, Any] ):







            """simple docstring"""







            __lowercase     						=TFGPTJModel(config=__lowercase )

            # first forward pass
            __lowercase     						=model(__lowercase  , token_type_ids=__lowercase  , use_cache=__lowercase )
            __lowercase     						=model(__lowercase  , token_type_ids=__lowercase )
            __lowercase     						=model(__lowercase  , token_type_ids=__lowercase  , use_cache=__lowercase )

            self.parent.assertTrue(len(__lowercase ) == len(__lowercase ) )
            self.parent.assertTrue(len(__lowercase ) == len(__lowercase ) + 1 )

            __lowercase						,       __lowercase     						=outputs.to_tuple()

            # create hypothetical next token and extent to next_input_ids
            __lowercase     						=ids_tensor((self.batch_size, 1)  , config.vocab_size )
            __lowercase     						=ids_tensor([self.batch_size, 1]  , self.type_vocab_size )

            # append to next input_ids and token_type_ids
            __lowercase     						=tf.concat([input_ids, next_tokens]  , axis=-1 )
            __lowercase     						=tf.concat([token_type_ids, next_token_types]  , axis=-1 )

            __lowercase     						=model(__lowercase  , token_type_ids=__lowercase )['last_hidden_state']
            __lowercase     						=model(__lowercase  , token_type_ids=__lowercase  , past_key_values=__lowercase )[
                'last_hidden_state'
            ]

            # select random slice
            __lowercase     						=int(ids_tensor((1,)  , shape_list(__lowercase )[-1] ) )
            __lowercase     						=output_from_no_past[:, -1, random_slice_idx]
            __lowercase     						=output_from_past[:, 0, random_slice_idx]

            # test that outputs are equal for slice
            tf.debugging.assert_near(__lowercase  , __lowercase  , rtol=1E-6 )







       def      snake_case				(							self     :	List[Any]  , __lowercase     :	Union[str, Any]  , __lowercase     :	Optional[int]  , __lowercase     :	Any  , __lowercase     :	Optional[int]  , __lowercase     :	Any  , *__lowercase     :	Optional[int] ):







            """simple docstring"""







            __lowercase     						=TFGPTJModel(config=__lowercase )

            # create attention mask
            __lowercase     						=self.seq_length // 2
            __lowercase     						=tf.ones((self.batch_size, half_seq_length)  , dtype=tf.intaa )
            __lowercase     						=tf.zeros((self.batch_size, self.seq_length - half_seq_length)  , dtype=tf.intaa )
            __lowercase     						=tf.concat([attn_mask_begin, attn_mask_end]  , axis=1 )

            # first forward pass
            __lowercase						,       __lowercase     						=model(__lowercase  , attention_mask=__lowercase ).to_tuple()

            # create hypothetical next token and extent to next_input_ids
            __lowercase     						=ids_tensor((self.batch_size, 1)  , config.vocab_size )

            # change a random masked slice from input_ids
            __lowercase     						=ids_tensor((1,)  , __lowercase ).numpy() + 1
            __lowercase     						=ids_tensor((self.batch_size, self.seq_length)  , config.vocab_size )
            __lowercase     						=tf.range(self.seq_length ) == (self.seq_length - random_seq_idx_to_change)
            __lowercase     						=tf.transpose(
                tf.broadcast_to(tf.expand_dims(__lowercase  , -1 )  , (self.seq_length, self.batch_size) ) )
            __lowercase     						=tf.where(__lowercase  , __lowercase  , __lowercase )

            # append to next input_ids and attn_mask
            __lowercase     						=tf.concat([input_ids, next_tokens]  , axis=-1 )
            __lowercase     						=tf.concat([attn_mask, tf.ones((shape_list(__lowercase )[0], 1)  , dtype=tf.intaa )]  , axis=1 )

            # get two different outputs
            __lowercase     						=model(__lowercase  , attention_mask=__lowercase )['last_hidden_state']
            __lowercase     						=model(__lowercase  , past_key_values=__lowercase  , attention_mask=__lowercase )[
                'last_hidden_state'
            ]

            # select random slice
            __lowercase     						=int(ids_tensor((1,)  , shape_list(__lowercase )[-1] ) )
            __lowercase     						=output_from_no_past[:, -1, random_slice_idx]
            __lowercase     						=output_from_past[:, 0, random_slice_idx]

            # test that outputs are equal for slice
            tf.debugging.assert_near(__lowercase  , __lowercase  , rtol=1E-12 )







       def      snake_case				(							self     :	Any  , __lowercase     :	List[Any]  , __lowercase     :	Dict  , __lowercase     :	List[str]  , __lowercase     :	Optional[int]  , __lowercase     :	Optional[Any]  , *__lowercase     :	Optional[int] ):







            """simple docstring"""







            __lowercase     						=TFGPTJModel(config=__lowercase )

            __lowercase     						=input_ids[:1, :]
            __lowercase     						=input_mask[:1, :]
            __lowercase     						=token_type_ids[:1, :]
            __lowercase     						=1

            # first forward pass
            __lowercase     						=model(__lowercase  , attention_mask=__lowercase  , token_type_ids=__lowercase  , use_cache=__lowercase )

            __lowercase						,       __lowercase     						=outputs.to_tuple()

            # create hypothetical next token and extent to next_input_ids
            __lowercase     						=ids_tensor((self.batch_size, 3)  , config.vocab_size )
            __lowercase     						=ids_tensor((self.batch_size, 3)  , 2 )
            __lowercase     						=ids_tensor((self.batch_size, 3)  , self.type_vocab_size )

            # append to next input_ids and token_type_ids
            __lowercase     						=tf.concat([input_ids, next_tokens]  , axis=-1 )
            __lowercase     						=tf.concat([input_mask, next_attn_mask]  , axis=-1 )
            __lowercase     						=tf.concat([token_type_ids, next_token_types]  , axis=-1 )

            __lowercase     						=model(
                __lowercase  , token_type_ids=__lowercase  , attention_mask=__lowercase )['last_hidden_state']
            __lowercase     						=model(
                __lowercase  , token_type_ids=__lowercase  , attention_mask=__lowercase  , past_key_values=__lowercase  , )['last_hidden_state']
            self.parent.assertTrue(output_from_past.shape[1] == next_tokens.shape[1] )

            # select random slice
            __lowercase     						=int(ids_tensor((1,)  , shape_list(__lowercase )[-1] ) )
            __lowercase     						=output_from_no_past[:, -3:, random_slice_idx]
            __lowercase     						=output_from_past[:, :, random_slice_idx]

            # test that outputs are equal for slice
            tf.debugging.assert_near(__lowercase  , __lowercase  , rtol=1E-3 )







       def      snake_case				(							self     :	Any  , __lowercase     :	Optional[Any]  , __lowercase     :	Any  , __lowercase     :	Tuple  , __lowercase     :	Optional[int]  , __lowercase     :	str  , *__lowercase     :	int ):







            """simple docstring"""







            __lowercase     						=TFGPTJForCausalLM(config=__lowercase )
            __lowercase     						={
                'input_ids': input_ids,
                'attention_mask': input_mask,
                'token_type_ids': token_type_ids,
            }
            __lowercase     						=model(__lowercase )
            self.parent.assertEqual(result.logits.shape  , (self.batch_size, self.seq_length, self.vocab_size) )







       def      snake_case				(							self     :	Dict ):







            """simple docstring"""







            __lowercase     						=self.prepare_config_and_inputs()

            (
                (
                __lowercase
            )						,       (
                __lowercase
            )						,       (
                __lowercase
            )						,       (
                __lowercase
            )						,       (
                __lowercase
            )						,       (
                __lowercase
            )						,       (
                __lowercase
            )						,       (
                __lowercase
            )						,       (
                __lowercase
            )						,       
            )     						=config_and_inputs

            __lowercase     						={
                'input_ids': input_ids,
                'token_type_ids': token_type_ids,
                'attention_mask': input_mask,
            }
            return config, inputs_dict






@require_tf
class 					lowerCAmelCase       ( A				,     A				,     A				,     unittest.TestCase				):
       lowerCAmelCase_				=	(
           (TFGPTJForCausalLM, TFGPTJForSequenceClassification, TFGPTJForQuestionAnswering, TFGPTJModel)
           if is_tf_available()
           else ()
       )

       lowerCAmelCase_				=	(TFGPTJForCausalLM,) if is_tf_available() else ()
       lowerCAmelCase_				=	(
           {
               "feature-extraction": TFGPTJModel,
               "question-answering": TFGPTJForQuestionAnswering,
               "text-classification": TFGPTJForSequenceClassification,
               "text-generation": TFGPTJForCausalLM,
               "zero-shot": TFGPTJForSequenceClassification,
           }
           if is_tf_available()
           else {}
       )
       lowerCAmelCase_				=	False
       lowerCAmelCase_				=	False
       lowerCAmelCase_				=	False
       lowerCAmelCase_				=	False







       def      snake_case				(							self     :	Dict  , __lowercase     :	Union[str, Any]  , __lowercase     :	List[str]  , __lowercase     :	Optional[int]  , __lowercase     :	Optional[Any]  , __lowercase     :	Union[str, Any] ):







            """simple docstring"""







            if (
                pipeline_test_casse_name == "QAPipelineTests"
                and tokenizer_name is not None
                and not tokenizer_name.endswith('Fast' )
            ):
                 # `QAPipelineTests` fails for a few models when the slower tokenizer are used.
                 # (The slower tokenizers were never used for pipeline tests before the pipeline testing rework)
                 # TODO: check (and possibly fix) the `QAPipelineTests` with slower tokenizer
                 return True

            return False







       def      snake_case				(							self     :	Optional[Any] ):







            """simple docstring"""







            __lowercase     						=TFGPTJModelTester(self )
            __lowercase     						=ConfigTester(self  , config_class=__lowercase  , n_embd=37 )







       def      snake_case				(							self     :	Optional[Any] ):







            """simple docstring"""







            self.config_tester.run_common_tests()







       def      snake_case				(							self     :	List[str] ):







            """simple docstring"""







            __lowercase     						=self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_gptj_model(*__lowercase )







       def      snake_case				(							self     :	List[Any] ):







            """simple docstring"""







            __lowercase     						=self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_gptj_model_past(*__lowercase )







       def      snake_case				(							self     :	Optional[int] ):







            """simple docstring"""







            __lowercase     						=self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_gptj_model_attention_mask_past(*__lowercase )







       def      snake_case				(							self     :	Tuple ):







            """simple docstring"""







            __lowercase     						=self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_gptj_model_past_large_inputs(*__lowercase )







       def      snake_case				(							self     :	int ):







            """simple docstring"""







            __lowercase     						=self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_gptj_lm_head_model(*__lowercase )







       @slow
       @unittest.skipIf(
           not is_tf_available() or len(tf.config.list_physical_devices('GPU' ) ) > 0  , 'skip testing on GPU for now to avoid GPU OOM.'  , )
       def      snake_case				(							self     :	Union[str, Any] ):







            """simple docstring"""







            __lowercase     						=TFGPTJModel.from_pretrained('EleutherAI/gpt-j-6B'  , from_pt=__lowercase )
            self.assertIsNotNone(__lowercase )







       @unittest.skip(reason='Currently, model embeddings are going to undergo a major refactor.' )
       def      snake_case				(							self     :	Union[str, Any] ):







            """simple docstring"""







            super().test_resize_token_embeddings()






@require_tf
@tooslow
# Marked as @tooslow due to GPU OOM -- but still useful to run locally. Requires ~39GB of RAM.
class 					lowerCAmelCase       ( unittest.TestCase				):







       def      snake_case				(							self     :	List[Any] ):







            """simple docstring"""







            __lowercase     						=TFGPTJForCausalLM.from_pretrained('EleutherAI/gpt-j-6B'  , from_pt=__lowercase )
            __lowercase     						=tf.convert_to_tensor([[464, 3290]]  , dtype=tf.intaa )  # The dog
            # fmt: off
            # The dog is a man's best friend. It is a loyal companion, and it is a friend
            __lowercase     						=[464, 3290, 318, 257, 582, 338, 1266, 1545, 13, 632, 318, 257, 9112, 15185, 11, 290, 340, 318, 257, 1545]
            # fmt: on
            __lowercase     						=model.generate(__lowercase  , do_sample=__lowercase )
            self.assertListEqual(output_ids[0].numpy().tolist()  , __lowercase )







       def      snake_case				(							self     :	Optional[Any] ):







            """simple docstring"""







            __lowercase     						=AutoTokenizer.from_pretrained('EleutherAI/gpt-j-6B'  , revision='float16' )
            __lowercase     						=TFGPTJForCausalLM.from_pretrained('EleutherAI/gpt-j-6B'  , revision='float16'  , from_pt=__lowercase )

            __lowercase     						=tokenizer('Today is a nice day and'  , return_tensors='tf' )
            # forces the generation to happen on CPU, to avoid GPU-related quirks
            with tf.device(':/CPU:0' ):
                 __lowercase     						=model.generate(**__lowercase  , do_sample=__lowercase  , seed=[42, 0] )
            __lowercase     						=tokenizer.decode(output_ids[0]  , skip_special_tokens=__lowercase )

            __lowercase     						='Today is a nice day and I’m going to go for a walk. I’'
            self.assertEqual(__lowercase  , __lowercase )







       def      snake_case				(							self     :	str ):







            """simple docstring"""







            __lowercase     						=TFGPTJForCausalLM.from_pretrained('EleutherAI/gpt-j-6B'  , revision='float16'  , from_pt=__lowercase )
            __lowercase     						=AutoTokenizer.from_pretrained('EleutherAI/gpt-j-6B'  , revision='float16' )

            __lowercase     						='left'

            # Define PAD Token = EOS Token = 50256
            __lowercase     						=tokenizer.eos_token
            __lowercase     						=model.config.eos_token_id

            # use different length sentences to test batching
            __lowercase     						=[
                'Hello, my dog is a little',
                'Today, I',
            ]
            __lowercase     						=[
                'Hello, my dog is a little over a year old and has been diagnosed with hip dysplasia',
                'Today, I’m going to be talking about a topic that’',
            ]
            return model, tokenizer, sentences, expected_output_sentences







       def      snake_case				(							self     :	List[Any] ):







            """simple docstring"""







            __lowercase						,       __lowercase						,       __lowercase						,       __lowercase     						=self._get_beam_search_test_objects()

            __lowercase     						=tokenizer(__lowercase  , return_tensors='tf'  , padding=__lowercase )
            __lowercase     						=model.generate(**__lowercase  , do_sample=__lowercase  , num_beams=2 )
            __lowercase     						=tokenizer.batch_decode(__lowercase  , skip_special_tokens=__lowercase )
            self.assertListEqual(__lowercase  , __lowercase )







       def      snake_case				(							self     :	List[str] ):







            """simple docstring"""







            __lowercase						,       __lowercase						,       __lowercase						,       __lowercase     						=self._get_beam_search_test_objects()

            __lowercase     						=tokenizer(__lowercase  , return_tensors='tf'  , padding=__lowercase )
            __lowercase     						=tokenizer(sentences[0]  , return_tensors='tf' )
            __lowercase     						=model.generate(**__lowercase  , do_sample=__lowercase  , num_beams=2 )
            __lowercase     						=(
                shape_list(inputs_non_padded['input_ids'] )[-1]
                - tf.reduce_sum(tf.cast(inputs['attention_mask'][-1]  , tf.intaa ) ).numpy()
            )
            __lowercase     						=tokenizer(sentences[1]  , return_tensors='tf' )
            __lowercase     						=model.generate(
                **__lowercase  , do_sample=__lowercase  , num_beams=2  , max_length=model.config.max_length - num_paddings )
            __lowercase     						=tokenizer.decode(output_non_padded[0]  , skip_special_tokens=__lowercase )
            __lowercase     						=tokenizer.decode(output_padded[0]  , skip_special_tokens=__lowercase )
            self.assertListEqual(__lowercase  , [non_padded_sentence, padded_sentence] )







       def      snake_case				(							self     :	Union[str, Any] ):







            """simple docstring"""







            __lowercase						,       __lowercase						,       __lowercase						,       __lowercase     						=self._get_beam_search_test_objects()

            __lowercase     						=tokenizer(__lowercase  , return_tensors='tf'  , padding=__lowercase )
            __lowercase     						=tf.function(model.generate  , jit_compile=__lowercase )
            __lowercase     						=xla_generate(**__lowercase  , do_sample=__lowercase  , num_beams=2 )
            __lowercase     						=tokenizer.batch_decode(__lowercase  , skip_special_tokens=__lowercase )
            self.assertListEqual(__lowercase  , __lowercase )





'''simple docstring'''

import torch

from diffusers import DDIMScheduler

from .test_schedulers import SchedulerCommonTest


class 					lowerCAmelCase       ( A				):
       lowerCAmelCase_				=	(DDIMScheduler,)
       lowerCAmelCase_				=	(("eta", 0.0), ("num_inference_steps", 5_0))







       def      snake_case				(							self     :	List[Any]  , **__lowercase     :	Optional[Any] ):







            """simple docstring"""







            __lowercase     						={
                'num_train_timesteps': 1000,
                'beta_start': 0.0_0_0_1,
                'beta_end': 0.0_2,
                'beta_schedule': 'linear',
                'clip_sample': True,
            }

            config.update(**__lowercase )
            return config







       def      snake_case				(							self     :	Optional[Any]  , **__lowercase     :	str ):







            """simple docstring"""







            __lowercase     						=self.scheduler_classes[0]
            __lowercase     						=self.get_scheduler_config(**__lowercase )
            __lowercase     						=scheduler_class(**__lowercase )

            __lowercase						,       __lowercase     						=10, 0.0

            __lowercase     						=self.dummy_model()
            __lowercase     						=self.dummy_sample_deter

            scheduler.set_timesteps(__lowercase )

            for t in scheduler.timesteps:
                 __lowercase     						=model(__lowercase  , __lowercase )
                 __lowercase     						=scheduler.step(__lowercase  , __lowercase  , __lowercase  , __lowercase ).prev_sample

            return sample







       def      snake_case				(							self     :	List[Any] ):







            """simple docstring"""







            for timesteps in [100, 500, 1000]:
                 self.check_over_configs(num_train_timesteps=__lowercase )







       def      snake_case				(							self     :	List[Any] ):







            """simple docstring"""







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

            __lowercase     						=self.scheduler_classes[0]
            __lowercase     						=self.get_scheduler_config(steps_offset=1 )
            __lowercase     						=scheduler_class(**__lowercase )
            scheduler.set_timesteps(5 )
            assert torch.equal(scheduler.timesteps  , torch.LongTensor([801, 601, 401, 201, 1] ) )







       def      snake_case				(							self     :	List[str] ):







            """simple docstring"""







            for beta_start, beta_end in zip([0.0_0_0_1, 0.0_0_1, 0.0_1, 0.1]  , [0.0_0_2, 0.0_2, 0.2, 2] ):
                 self.check_over_configs(beta_start=__lowercase  , beta_end=__lowercase )







       def      snake_case				(							self     :	str ):







            """simple docstring"""







            for schedule in ["linear", "squaredcos_cap_v2"]:
                 self.check_over_configs(beta_schedule=__lowercase )







       def      snake_case				(							self     :	str ):







            """simple docstring"""







            for prediction_type in ["epsilon", "v_prediction"]:
                 self.check_over_configs(prediction_type=__lowercase )







       def      snake_case				(							self     :	List[str] ):







            """simple docstring"""







            for clip_sample in [True, False]:
                 self.check_over_configs(clip_sample=__lowercase )







       def      snake_case				(							self     :	Dict ):







            """simple docstring"""







            for timestep_spacing in ["trailing", "leading"]:
                 self.check_over_configs(timestep_spacing=__lowercase )







       def      snake_case				(							self     :	Tuple ):







            """simple docstring"""







            for rescale_betas_zero_snr in [True, False]:
                 self.check_over_configs(rescale_betas_zero_snr=__lowercase )







       def      snake_case				(							self     :	int ):







            """simple docstring"""







            self.check_over_configs(thresholding=__lowercase )
            for threshold in [0.5, 1.0, 2.0]:
                 for prediction_type in ["epsilon", "v_prediction"]:
                      self.check_over_configs(
                          thresholding=__lowercase  , prediction_type=__lowercase  , sample_max_value=__lowercase  , )







       def      snake_case				(							self     :	Dict ):







            """simple docstring"""







            for t in [1, 10, 49]:
                 self.check_over_forward(time_step=__lowercase )







       def      snake_case				(							self     :	List[str] ):







            """simple docstring"""







            for t, num_inference_steps in zip([1, 10, 50]  , [10, 50, 500] ):
                 self.check_over_forward(time_step=__lowercase  , num_inference_steps=__lowercase )







       def      snake_case				(							self     :	Tuple ):







            """simple docstring"""







            for t, eta in zip([1, 10, 49]  , [0.0, 0.5, 1.0] ):
                 self.check_over_forward(time_step=__lowercase  , eta=__lowercase )







       def      snake_case				(							self     :	Optional[int] ):







            """simple docstring"""







            __lowercase     						=self.scheduler_classes[0]
            __lowercase     						=self.get_scheduler_config()
            __lowercase     						=scheduler_class(**__lowercase )

            assert torch.sum(torch.abs(scheduler._get_variance(0  , 0 ) - 0.0 ) ) < 1E-5
            assert torch.sum(torch.abs(scheduler._get_variance(420  , 400 ) - 0.1_4_7_7_1 ) ) < 1E-5
            assert torch.sum(torch.abs(scheduler._get_variance(980  , 960 ) - 0.3_2_4_6_0 ) ) < 1E-5
            assert torch.sum(torch.abs(scheduler._get_variance(0  , 0 ) - 0.0 ) ) < 1E-5
            assert torch.sum(torch.abs(scheduler._get_variance(487  , 486 ) - 0.0_0_9_7_9 ) ) < 1E-5
            assert torch.sum(torch.abs(scheduler._get_variance(999  , 998 ) - 0.0_2 ) ) < 1E-5







       def      snake_case				(							self     :	Tuple ):







            """simple docstring"""







            __lowercase     						=self.full_loop()

            __lowercase     						=torch.sum(torch.abs(__lowercase ) )
            __lowercase     						=torch.mean(torch.abs(__lowercase ) )

            assert abs(result_sum.item() - 1_7_2.0_0_6_7 ) < 1E-2
            assert abs(result_mean.item() - 0.2_2_3_9_6_7 ) < 1E-3







       def      snake_case				(							self     :	List[str] ):







            """simple docstring"""







            __lowercase     						=self.full_loop(prediction_type='v_prediction' )

            __lowercase     						=torch.sum(torch.abs(__lowercase ) )
            __lowercase     						=torch.mean(torch.abs(__lowercase ) )

            assert abs(result_sum.item() - 5_2.5_3_0_2 ) < 1E-2
            assert abs(result_mean.item() - 0.0_6_8_4 ) < 1E-3







       def      snake_case				(							self     :	Optional[Any] ):







            """simple docstring"""







            __lowercase     						=self.full_loop(set_alpha_to_one=__lowercase  , beta_start=0.0_1 )
            __lowercase     						=torch.sum(torch.abs(__lowercase ) )
            __lowercase     						=torch.mean(torch.abs(__lowercase ) )

            assert abs(result_sum.item() - 1_4_9.8_2_9_5 ) < 1E-2
            assert abs(result_mean.item() - 0.1_9_5_1 ) < 1E-3







       def      snake_case				(							self     :	Dict ):







            """simple docstring"""







            __lowercase     						=self.full_loop(set_alpha_to_one=__lowercase  , beta_start=0.0_1 )
            __lowercase     						=torch.sum(torch.abs(__lowercase ) )
            __lowercase     						=torch.mean(torch.abs(__lowercase ) )

            assert abs(result_sum.item() - 1_4_9.0_7_8_4 ) < 1E-2
            assert abs(result_mean.item() - 0.1_9_4_1 ) < 1E-3


