





"""simple docstring"""


import copy

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


a__   :       Optional[Any]   						=			logging.get_logger(__name__)

a__   :       List[str]   						=			{}



class        lowercase_			(       __lowerCAmelCase							):
       snake_case__			:							List[str]										=       """fsmt_decoder"""

       def __init__(     self				,   __SCREAMING_SNAKE_CASE=0				,   __SCREAMING_SNAKE_CASE=0       ):






        '''simple docstring'''

        super().__init__()
        snake_case__      							=			vocab_size
        snake_case__      							=			bos_token_id



class        lowercase_			(       __lowerCAmelCase							):
       snake_case__			:							str										=       """fsmt"""
       snake_case__			:							List[Any]										=       {"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}

       def __init__(     self				,   __SCREAMING_SNAKE_CASE=["en", "de"]				,   __SCREAMING_SNAKE_CASE=4_2024				,   __SCREAMING_SNAKE_CASE=4_2024				,   __SCREAMING_SNAKE_CASE="relu"				,   __SCREAMING_SNAKE_CASE=1024				,   __SCREAMING_SNAKE_CASE=200				,   __SCREAMING_SNAKE_CASE=1024				,   __SCREAMING_SNAKE_CASE=4096				,   __SCREAMING_SNAKE_CASE=12				,   __SCREAMING_SNAKE_CASE=16				,   __SCREAMING_SNAKE_CASE=0.0				,   __SCREAMING_SNAKE_CASE=4096				,   __SCREAMING_SNAKE_CASE=12				,   __SCREAMING_SNAKE_CASE=16				,   __SCREAMING_SNAKE_CASE=0.0				,   __SCREAMING_SNAKE_CASE=0.0				,   __SCREAMING_SNAKE_CASE=0.1				,   __SCREAMING_SNAKE_CASE=0.0				,   __SCREAMING_SNAKE_CASE=0.02				,   __SCREAMING_SNAKE_CASE=2				,   __SCREAMING_SNAKE_CASE=True				,   __SCREAMING_SNAKE_CASE=True				,   __SCREAMING_SNAKE_CASE=False				,   __SCREAMING_SNAKE_CASE=5				,   __SCREAMING_SNAKE_CASE=1.0				,   __SCREAMING_SNAKE_CASE=False				,   __SCREAMING_SNAKE_CASE=True				,   __SCREAMING_SNAKE_CASE=1				,   __SCREAMING_SNAKE_CASE=0				,   __SCREAMING_SNAKE_CASE=2				,   __SCREAMING_SNAKE_CASE=2				,   **__SCREAMING_SNAKE_CASE				,   ):






        '''simple docstring'''

        snake_case__      							=			langs
        snake_case__      							=			src_vocab_size
        snake_case__      							=			tgt_vocab_size
        snake_case__      							=			d_model  # encoder_embed_dim and decoder_embed_dim

        snake_case__      							=			encoder_ffn_dim
        snake_case__      							=			snake_case__      							=			encoder_layers
        snake_case__      							=			encoder_attention_heads
        snake_case__      							=			encoder_layerdrop
        snake_case__      							=			decoder_layerdrop
        snake_case__      							=			decoder_ffn_dim
        snake_case__      							=			decoder_layers
        snake_case__      							=			decoder_attention_heads
        snake_case__      							=			max_position_embeddings
        snake_case__      							=			init_std  # Normal(0, this parameter)
        snake_case__      							=			activation_function

        snake_case__      							=			DecoderConfig(vocab_size=__SCREAMING_SNAKE_CASE				,   bos_token_id=__SCREAMING_SNAKE_CASE       )
        if "decoder" in common_kwargs:
         del common_kwargs["decoder"]

        snake_case__      							=			scale_embedding  # scale factor will be sqrt(d_model) if True

        # 3 Types of Dropout
        snake_case__      							=			attention_dropout
        snake_case__      							=			activation_dropout
        snake_case__      							=			dropout

        snake_case__      							=			use_cache
        super().__init__(
            pad_token_id=__SCREAMING_SNAKE_CASE				,   bos_token_id=__SCREAMING_SNAKE_CASE				,   eos_token_id=__SCREAMING_SNAKE_CASE				,   decoder_start_token_id=__SCREAMING_SNAKE_CASE				,   is_encoder_decoder=__SCREAMING_SNAKE_CASE				,   tie_word_embeddings=__SCREAMING_SNAKE_CASE				,   forced_eos_token_id=__SCREAMING_SNAKE_CASE				,   max_length=__SCREAMING_SNAKE_CASE				,   num_beams=__SCREAMING_SNAKE_CASE				,   length_penalty=__SCREAMING_SNAKE_CASE				,   early_stopping=__SCREAMING_SNAKE_CASE				,   **__SCREAMING_SNAKE_CASE				,   )

       def 						_SCREAMING_SNAKE_CASE(     self       ):






        '''simple docstring'''

        snake_case__      							=			copy.deepcopy(self.__dict__       )
        snake_case__      							=			self.decoder.to_dict()
        snake_case__      							=			self.__class__.model_type
        return output








"""simple docstring"""


import torch

from diffusers import LMSDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest



class        lowercase_			(       __lowerCAmelCase							):
       snake_case__			:							Optional[int]										=       (LMSDiscreteScheduler,)
       snake_case__			:							int										=       10

       def 						_SCREAMING_SNAKE_CASE(     self				,   **__SCREAMING_SNAKE_CASE       ):






        '''simple docstring'''

        snake_case__      							=			{
            "num_train_timesteps": 1100,
            "beta_start": 0.0001,
            "beta_end": 0.02,
            "beta_schedule": "linear",
        }

        config.update(**__SCREAMING_SNAKE_CASE       )
        return config

       def 						_SCREAMING_SNAKE_CASE(     self       ):






        '''simple docstring'''

        for timesteps in [10, 50, 100, 1000]:
         self.check_over_configs(num_train_timesteps=__SCREAMING_SNAKE_CASE       )

       def 						_SCREAMING_SNAKE_CASE(     self       ):






        '''simple docstring'''

        for beta_start, beta_end in zip([0.00001, 0.0001, 0.001]				,   [0.0002, 0.002, 0.02]       ):
         self.check_over_configs(beta_start=__SCREAMING_SNAKE_CASE				,   beta_end=__SCREAMING_SNAKE_CASE       )

       def 						_SCREAMING_SNAKE_CASE(     self       ):






        '''simple docstring'''

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

       def 						_SCREAMING_SNAKE_CASE(     self       ):






        '''simple docstring'''

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

       def 						_SCREAMING_SNAKE_CASE(     self       ):






        '''simple docstring'''

        for t in [0, 500, 800]:
         self.check_over_forward(time_step=__SCREAMING_SNAKE_CASE       )

       def 						_SCREAMING_SNAKE_CASE(     self       ):






        '''simple docstring'''

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

        scheduler.set_timesteps(self.num_inference_steps       )

        snake_case__      							=			self.dummy_model()
        snake_case__      							=			self.dummy_sample_deter * scheduler.init_noise_sigma

        for i, t in enumerate(scheduler.timesteps       ):
         snake_case__      							=			scheduler.scale_model_input(__SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE       )

         snake_case__      							=			model(__SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE       )

         snake_case__      							=			scheduler.step(__SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE       )
         snake_case__      							=			output.prev_sample

        snake_case__      							=			torch.sum(torch.abs(__SCREAMING_SNAKE_CASE       )       )
        snake_case__      							=			torch.mean(torch.abs(__SCREAMING_SNAKE_CASE       )       )

        assert abs(result_sum.item() - 1006.388       ) < 1e-2
        assert abs(result_mean.item() - 1.31       ) < 1e-3

       def 						_SCREAMING_SNAKE_CASE(     self       ):






        '''simple docstring'''

        snake_case__      							=			self.scheduler_classes[0]
        snake_case__      							=			self.get_scheduler_config(prediction_type="v_prediction"       )
        snake_case__      							=			scheduler_class(**__SCREAMING_SNAKE_CASE       )

        scheduler.set_timesteps(self.num_inference_steps       )

        snake_case__      							=			self.dummy_model()
        snake_case__      							=			self.dummy_sample_deter * scheduler.init_noise_sigma

        for i, t in enumerate(scheduler.timesteps       ):
         snake_case__      							=			scheduler.scale_model_input(__SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE       )

         snake_case__      							=			model(__SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE       )

         snake_case__      							=			scheduler.step(__SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE       )
         snake_case__      							=			output.prev_sample

        snake_case__      							=			torch.sum(torch.abs(__SCREAMING_SNAKE_CASE       )       )
        snake_case__      							=			torch.mean(torch.abs(__SCREAMING_SNAKE_CASE       )       )

        assert abs(result_sum.item() - 0.0017       ) < 1e-2
        assert abs(result_mean.item() - 2.2676e-06       ) < 1e-3

       def 						_SCREAMING_SNAKE_CASE(     self       ):






        '''simple docstring'''

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

        scheduler.set_timesteps(self.num_inference_steps				,   device=__SCREAMING_SNAKE_CASE       )

        snake_case__      							=			self.dummy_model()
        snake_case__      							=			self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
        snake_case__      							=			sample.to(__SCREAMING_SNAKE_CASE       )

        for i, t in enumerate(scheduler.timesteps       ):
         snake_case__      							=			scheduler.scale_model_input(__SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE       )

         snake_case__      							=			model(__SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE       )

         snake_case__      							=			scheduler.step(__SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE       )
         snake_case__      							=			output.prev_sample

        snake_case__      							=			torch.sum(torch.abs(__SCREAMING_SNAKE_CASE       )       )
        snake_case__      							=			torch.mean(torch.abs(__SCREAMING_SNAKE_CASE       )       )

        assert abs(result_sum.item() - 1006.388       ) < 1e-2
        assert abs(result_mean.item() - 1.31       ) < 1e-3

       def 						_SCREAMING_SNAKE_CASE(     self       ):






        '''simple docstring'''

        snake_case__      							=			self.scheduler_classes[0]
        snake_case__      							=			self.get_scheduler_config()
        snake_case__      							=			scheduler_class(**__SCREAMING_SNAKE_CASE				,   use_karras_sigmas=__SCREAMING_SNAKE_CASE       )

        scheduler.set_timesteps(self.num_inference_steps				,   device=__SCREAMING_SNAKE_CASE       )

        snake_case__      							=			self.dummy_model()
        snake_case__      							=			self.dummy_sample_deter.to(__SCREAMING_SNAKE_CASE       ) * scheduler.init_noise_sigma
        snake_case__      							=			sample.to(__SCREAMING_SNAKE_CASE       )

        for t in scheduler.timesteps:
         snake_case__      							=			scheduler.scale_model_input(__SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE       )

         snake_case__      							=			model(__SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE       )

         snake_case__      							=			scheduler.step(__SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE       )
         snake_case__      							=			output.prev_sample

        snake_case__      							=			torch.sum(torch.abs(__SCREAMING_SNAKE_CASE       )       )
        snake_case__      							=			torch.mean(torch.abs(__SCREAMING_SNAKE_CASE       )       )

        assert abs(result_sum.item() - 3812.9927       ) < 2e-2
        assert abs(result_mean.item() - 4.9648       ) < 1e-3

