






import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import BlenderbotSmallConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor


if is_flax_available():
 import os

 # The slow tests are often failing with OOM error on GPU
 # This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
 # but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
 lowerCamelCase      :       Union[str, Any]    =						'''platform'''

 import jax
 import jax.numpy as jnp

 from transformers.models.blenderbot_small.modeling_flax_blenderbot_small import (
     FlaxBlenderbotSmallForConditionalGeneration,
     FlaxBlenderbotSmallModel,
     shift_tokens_right,
 )





def    __lowerCAmelCase   (    __snake_case       ,			__snake_case       ,			__snake_case=None       ,			__snake_case=None       ,			__snake_case=None       ,			__snake_case=None       ,			__snake_case=None       ,			__snake_case=None       ,			):
     if attention_mask is None:
          __lowerCAmelCase             =       np.where(input_ids != config.pad_token_id       ,			1       ,			0							)
     if decoder_attention_mask is None:
          __lowerCAmelCase             =       np.where(decoder_input_ids != config.pad_token_id       ,			1       ,			0							)
     if head_mask is None:
          __lowerCAmelCase             =       np.ones((config.encoder_layers, config.encoder_attention_heads)							)
     if decoder_head_mask is None:
          __lowerCAmelCase             =       np.ones((config.decoder_layers, config.decoder_attention_heads)							)
     if cross_attn_head_mask is None:
          __lowerCAmelCase             =       np.ones((config.decoder_layers, config.decoder_attention_heads)							)
     return {
         "input_ids": input_ids,
         "decoder_input_ids": decoder_input_ids,
         "attention_mask": attention_mask,
         "decoder_attention_mask": attention_mask,
     }



class 				_UpperCamelCase      :


      def __init__(						self       ,    __UpperCamelCase       ,    __UpperCamelCase=1_3       ,    __UpperCamelCase=7       ,    __UpperCamelCase=True       ,    __UpperCamelCase=False       ,    __UpperCamelCase=9_9       ,    __UpperCamelCase=1_6       ,    __UpperCamelCase=2       ,    __UpperCamelCase=4       ,    __UpperCamelCase=4       ,    __UpperCamelCase="gelu"       ,    __UpperCamelCase=0.1       ,    __UpperCamelCase=0.1       ,    __UpperCamelCase=3_2       ,    __UpperCamelCase=2       ,    __UpperCamelCase=1       ,    __UpperCamelCase=0       ,    __UpperCamelCase=0.0_2       ,    )->						List[Any]:
           __lowerCAmelCase             =       parent
           __lowerCAmelCase             =       batch_size
           __lowerCAmelCase             =       seq_length
           __lowerCAmelCase             =       is_training
           __lowerCAmelCase             =       use_labels
           __lowerCAmelCase             =       vocab_size
           __lowerCAmelCase             =       hidden_size
           __lowerCAmelCase             =       num_hidden_layers
           __lowerCAmelCase             =       num_attention_heads
           __lowerCAmelCase             =       intermediate_size
           __lowerCAmelCase             =       hidden_act
           __lowerCAmelCase             =       hidden_dropout_prob
           __lowerCAmelCase             =       attention_probs_dropout_prob
           __lowerCAmelCase             =       max_position_embeddings
           __lowerCAmelCase             =       eos_token_id
           __lowerCAmelCase             =       pad_token_id
           __lowerCAmelCase             =       bos_token_id
           __lowerCAmelCase             =       initializer_range


      def 			__UpperCAmelCase		(						self						)->						str:
           __lowerCAmelCase             =       np.clip(ids_tensor([self.batch_size, self.seq_length - 1]       ,    self.vocab_size						)       ,    3       ,    self.vocab_size						)
           __lowerCAmelCase             =       np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)       ,    dtype=np.intaa						))       ,    -1						)

           __lowerCAmelCase             =       shift_tokens_right(__UpperCamelCase       ,    1       ,    2						)

           __lowerCAmelCase             =       BlenderbotSmallConfig(
               vocab_size=self.vocab_size       ,    d_model=self.hidden_size       ,    encoder_layers=self.num_hidden_layers       ,    decoder_layers=self.num_hidden_layers       ,    encoder_attention_heads=self.num_attention_heads       ,    decoder_attention_heads=self.num_attention_heads       ,    encoder_ffn_dim=self.intermediate_size       ,    decoder_ffn_dim=self.intermediate_size       ,    dropout=self.hidden_dropout_prob       ,    attention_dropout=self.attention_probs_dropout_prob       ,    max_position_embeddings=self.max_position_embeddings       ,    eos_token_id=self.eos_token_id       ,    bos_token_id=self.bos_token_id       ,    pad_token_id=self.pad_token_id       ,    initializer_range=self.initializer_range       ,    use_cache=__UpperCamelCase       ,    )
           __lowerCAmelCase             =       prepare_blenderbot_inputs_dict(__UpperCamelCase       ,    __UpperCamelCase       ,    __UpperCamelCase						)
           return config, inputs_dict


      def 			__UpperCAmelCase		(						self						)->						Optional[int]:
           __lowerCAmelCase      ,			__lowerCAmelCase             =       self.prepare_config_and_inputs()
           return config, inputs_dict


      def 			__UpperCAmelCase		(						self       ,    __UpperCamelCase       ,    __UpperCamelCase       ,    __UpperCamelCase						)->						List[str]:
           __lowerCAmelCase             =       2_0
           __lowerCAmelCase             =       model_class_name(__UpperCamelCase						)

           __lowerCAmelCase             =       model.encode(inputs_dict["input_ids"]						)

           __lowerCAmelCase      ,			__lowerCAmelCase             =       (
               inputs_dict["decoder_input_ids"],
               inputs_dict["decoder_attention_mask"],
           )

           __lowerCAmelCase             =       model.init_cache(decoder_input_ids.shape[0]       ,    __UpperCamelCase       ,    __UpperCamelCase						)
           __lowerCAmelCase             =       jnp.ones((decoder_input_ids.shape[0], max_decoder_length)       ,    dtype="i4"						)

           __lowerCAmelCase             =       jnp.broadcast_to(
               jnp.arange(decoder_input_ids.shape[-1] - 1						)[None, :]       ,    (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)       ,    )
           __lowerCAmelCase             =       model.decode(
               decoder_input_ids[:, :-1]       ,    __UpperCamelCase       ,    decoder_attention_mask=__UpperCamelCase       ,    past_key_values=__UpperCamelCase       ,    decoder_position_ids=__UpperCamelCase       ,    )

           __lowerCAmelCase             =       jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]       ,    dtype="i4"						)
           __lowerCAmelCase             =       model.decode(
               decoder_input_ids[:, -1:]       ,    __UpperCamelCase       ,    decoder_attention_mask=__UpperCamelCase       ,    past_key_values=outputs_cache.past_key_values       ,    decoder_position_ids=__UpperCamelCase       ,    )

           __lowerCAmelCase             =       model.decode(__UpperCamelCase       ,    __UpperCamelCase						)

           __lowerCAmelCase             =       np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])						)						)
           self.parent.assertTrue(diff < 1e-3       ,    msg=F"""Max diff is {diff}"""						)


      def 			__UpperCAmelCase		(						self       ,    __UpperCamelCase       ,    __UpperCamelCase       ,    __UpperCamelCase						)->						Dict:
           __lowerCAmelCase             =       2_0
           __lowerCAmelCase             =       model_class_name(__UpperCamelCase						)

           __lowerCAmelCase             =       model.encode(inputs_dict["input_ids"]						)

           __lowerCAmelCase      ,			__lowerCAmelCase             =       (
               inputs_dict["decoder_input_ids"],
               inputs_dict["decoder_attention_mask"],
           )

           __lowerCAmelCase             =       jnp.concatenate(
               [
                   decoder_attention_mask,
                   jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])						),
               ]       ,    axis=-1       ,    )

           __lowerCAmelCase             =       model.init_cache(decoder_input_ids.shape[0]       ,    __UpperCamelCase       ,    __UpperCamelCase						)
           __lowerCAmelCase             =       jnp.broadcast_to(
               jnp.arange(decoder_input_ids.shape[-1] - 1						)[None, :]       ,    (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)       ,    )

           __lowerCAmelCase             =       model.decode(
               decoder_input_ids[:, :-1]       ,    __UpperCamelCase       ,    decoder_attention_mask=__UpperCamelCase       ,    past_key_values=__UpperCamelCase       ,    decoder_position_ids=__UpperCamelCase       ,    )
           __lowerCAmelCase             =       jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]       ,    dtype="i4"						)
           __lowerCAmelCase             =       model.decode(
               decoder_input_ids[:, -1:]       ,    __UpperCamelCase       ,    past_key_values=outputs_cache.past_key_values       ,    decoder_attention_mask=__UpperCamelCase       ,    decoder_position_ids=__UpperCamelCase       ,    )

           __lowerCAmelCase             =       model.decode(__UpperCamelCase       ,    __UpperCamelCase       ,    decoder_attention_mask=__UpperCamelCase						)

           __lowerCAmelCase             =       np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])						)						)
           self.parent.assertTrue(diff < 1e-3       ,    msg=F"""Max diff is {diff}"""						)



@require_flax
class 				_UpperCamelCase      (unittest.TestCase      ):
      snake_case_	=				99


      def 			__UpperCAmelCase		(						self						)->						int:
           __lowerCAmelCase             =       np.array(
               [
                   [7_1, 8_2, 1_8, 3_3, 4_6, 9_1, 2],
                   [6_8, 3_4, 2_6, 5_8, 3_0, 8_2, 2],
                   [5, 9_7, 1_7, 3_9, 9_4, 4_0, 2],
                   [7_6, 8_3, 9_4, 2_5, 7_0, 7_8, 2],
                   [8_7, 5_9, 4_1, 3_5, 4_8, 6_6, 2],
                   [5_5, 1_3, 1_6, 5_8, 5, 2, 1],  # note padding
                   [6_4, 2_7, 3_1, 5_1, 1_2, 7_5, 2],
                   [5_2, 6_4, 8_6, 1_7, 8_3, 3_9, 2],
                   [4_8, 6_1, 9, 2_4, 7_1, 8_2, 2],
                   [2_6, 1, 6_0, 4_8, 2_2, 1_3, 2],
                   [2_1, 5, 6_2, 2_8, 1_4, 7_6, 2],
                   [4_5, 9_8, 3_7, 8_6, 5_9, 4_8, 2],
                   [7_0, 7_0, 5_0, 9, 2_8, 0, 2],
               ]       ,    dtype=np.intaa       ,    )

           __lowerCAmelCase             =       input_ids.shape[0]
           __lowerCAmelCase             =       BlenderbotSmallConfig(
               vocab_size=self.vocab_size       ,    d_model=2_4       ,    encoder_layers=2       ,    decoder_layers=2       ,    encoder_attention_heads=2       ,    decoder_attention_heads=2       ,    encoder_ffn_dim=3_2       ,    decoder_ffn_dim=3_2       ,    max_position_embeddings=4_8       ,    eos_token_id=2       ,    pad_token_id=1       ,    bos_token_id=0       ,    )
           return config, input_ids, batch_size


      def 			__UpperCAmelCase		(						self						)->						Any:
           __lowerCAmelCase      ,			__lowerCAmelCase      ,			__lowerCAmelCase             =       self._get_config_and_data()
           __lowerCAmelCase             =       FlaxBlenderbotSmallForConditionalGeneration(__UpperCamelCase						)
           __lowerCAmelCase             =       lm_model(input_ids=__UpperCamelCase						)
           __lowerCAmelCase             =       (batch_size, input_ids.shape[1], config.vocab_size)
           self.assertEqual(outputs["logits"].shape       ,    __UpperCamelCase						)


      def 			__UpperCAmelCase		(						self						)->						Dict:
           __lowerCAmelCase             =       BlenderbotSmallConfig(
               vocab_size=self.vocab_size       ,    d_model=1_4       ,    encoder_layers=2       ,    decoder_layers=2       ,    encoder_attention_heads=2       ,    decoder_attention_heads=2       ,    encoder_ffn_dim=8       ,    decoder_ffn_dim=8       ,    max_position_embeddings=4_8       ,    )
           __lowerCAmelCase             =       FlaxBlenderbotSmallForConditionalGeneration(__UpperCamelCase						)
           __lowerCAmelCase             =       np.array([[7_1, 8_2, 1_8, 3_3, 4_6, 9_1, 2], [6_8, 3_4, 2_6, 5_8, 3_0, 2, 1]]       ,    dtype=np.intaa						)
           __lowerCAmelCase             =       np.array([[8_2, 7_1, 8_2, 1_8, 2], [5_8, 6_8, 2, 1, 1]]       ,    dtype=np.intaa						)
           __lowerCAmelCase             =       lm_model(input_ids=__UpperCamelCase       ,    decoder_input_ids=__UpperCamelCase						)
           __lowerCAmelCase             =       (*summary.shape, config.vocab_size)
           self.assertEqual(outputs["logits"].shape       ,    __UpperCamelCase						)


      def 			__UpperCAmelCase		(						self						)->						Tuple:
           __lowerCAmelCase             =       np.array([[7_1, 8_2, 1_8, 3_3, 2, 1, 1], [6_8, 3_4, 2_6, 5_8, 3_0, 8_2, 2]]       ,    dtype=np.intaa						)
           __lowerCAmelCase             =       shift_tokens_right(__UpperCamelCase       ,    1       ,    2						)
           __lowerCAmelCase             =       np.equal(__UpperCamelCase       ,    1						).astype(np.floataa						).sum()
           __lowerCAmelCase             =       np.equal(__UpperCamelCase       ,    1						).astype(np.floataa						).sum()
           self.assertEqual(shifted.shape       ,    input_ids.shape						)
           self.assertEqual(__UpperCamelCase       ,    n_pad_before - 1						)
           self.assertTrue(np.equal(shifted[:, 0]       ,    2						).all()						)



@require_flax
class 				_UpperCamelCase      (a_				,		unittest.TestCase				,		a_      ):
      snake_case_	=				True
      snake_case_	=				(
          (
              FlaxBlenderbotSmallModel,
              FlaxBlenderbotSmallForConditionalGeneration,
          )
          if is_flax_available()
          else ()
      )
      snake_case_	=				(FlaxBlenderbotSmallForConditionalGeneration,) if is_flax_available() else ()


      def 			__UpperCAmelCase		(						self						)->						int:
           __lowerCAmelCase             =       FlaxBlenderbotSmallModelTester(self						)


      def 			__UpperCAmelCase		(						self						)->						List[Any]:
           __lowerCAmelCase      ,			__lowerCAmelCase             =       self.model_tester.prepare_config_and_inputs()
           for model_class in self.all_model_classes:
                self.model_tester.check_use_cache_forward(__UpperCamelCase       ,    __UpperCamelCase       ,    __UpperCamelCase						)


      def 			__UpperCAmelCase		(						self						)->						Optional[int]:
           __lowerCAmelCase      ,			__lowerCAmelCase             =       self.model_tester.prepare_config_and_inputs()
           for model_class in self.all_model_classes:
                self.model_tester.check_use_cache_forward_with_attn_mask(__UpperCamelCase       ,    __UpperCamelCase       ,    __UpperCamelCase						)


      def 			__UpperCAmelCase		(						self						)->						Dict:
           __lowerCAmelCase      ,			__lowerCAmelCase             =       self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
                with self.subTest(model_class.__name__						):
                     __lowerCAmelCase             =       self._prepare_for_class(__UpperCamelCase       ,    __UpperCamelCase						)
                     __lowerCAmelCase             =       model_class(__UpperCamelCase						)

                     @jax.jit
                     def encode_jitted(__UpperCamelCase       ,    __UpperCamelCase=None       ,    **__UpperCamelCase						):
                          return model.encode(input_ids=__UpperCamelCase       ,    attention_mask=__UpperCamelCase						)

                     with self.subTest("JIT Enabled"						):
                          __lowerCAmelCase             =       encode_jitted(**__UpperCamelCase						).to_tuple()

                     with self.subTest("JIT Disabled"						):
                          with jax.disable_jit():
                               __lowerCAmelCase             =       encode_jitted(**__UpperCamelCase						).to_tuple()

                     self.assertEqual(len(__UpperCamelCase						)       ,    len(__UpperCamelCase						)						)
                     for jitted_output, output in zip(__UpperCamelCase       ,    __UpperCamelCase						):
                          self.assertEqual(jitted_output.shape       ,    output.shape						)


      def 			__UpperCAmelCase		(						self						)->						Any:
           __lowerCAmelCase      ,			__lowerCAmelCase             =       self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
                with self.subTest(model_class.__name__						):
                     __lowerCAmelCase             =       model_class(__UpperCamelCase						)
                     __lowerCAmelCase             =       model.encode(inputs_dict["input_ids"]       ,    inputs_dict["attention_mask"]						)

                     __lowerCAmelCase             =       {
                         "decoder_input_ids": inputs_dict["decoder_input_ids"],
                         "decoder_attention_mask": inputs_dict["decoder_attention_mask"],
                         "encoder_outputs": encoder_outputs,
                     }

                     @jax.jit
                     def decode_jitted(__UpperCamelCase       ,    __UpperCamelCase       ,    __UpperCamelCase						):
                          return model.decode(
                              decoder_input_ids=__UpperCamelCase       ,    decoder_attention_mask=__UpperCamelCase       ,    encoder_outputs=__UpperCamelCase       ,    )

                     with self.subTest("JIT Enabled"						):
                          __lowerCAmelCase             =       decode_jitted(**__UpperCamelCase						).to_tuple()

                     with self.subTest("JIT Disabled"						):
                          with jax.disable_jit():
                               __lowerCAmelCase             =       decode_jitted(**__UpperCamelCase						).to_tuple()

                     self.assertEqual(len(__UpperCamelCase						)       ,    len(__UpperCamelCase						)						)
                     for jitted_output, output in zip(__UpperCamelCase       ,    __UpperCamelCase						):
                          self.assertEqual(jitted_output.shape       ,    output.shape						)


      @slow
      def 			__UpperCAmelCase		(						self						)->						str:
           for model_class_name in self.all_model_classes:
                __lowerCAmelCase             =       model_class_name.from_pretrained("facebook/blenderbot_small-90M"						)
                # FlaxBlenderbotForSequenceClassification expects eos token in input_ids
                __lowerCAmelCase             =       np.ones((1, 1)						) * model.config.eos_token_id
                __lowerCAmelCase             =       model(__UpperCamelCase						)
                self.assertIsNotNone(__UpperCamelCase						)














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






