



"""simple docstring"""






# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch

import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union

import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin, SchedulerOutput

@dataclass
class  __SCREAMING_SNAKE_CASE			(   __lowerCAmelCase     ):


     '''simple docstring'''





     _a         =							42
     _a         =							42





class  __SCREAMING_SNAKE_CASE			(   __lowerCAmelCase	,     __lowerCAmelCase     ):


     '''simple docstring'''





     _a         =							1



     @register_to_config
     def __init__( self							:						Optional[Any],						lowerCamelCase							:						Dict = 2000,						lowerCamelCase							:						List[str] = 0.15,						lowerCamelCase							:						Optional[Any] = 0.01,						lowerCamelCase							:						Dict = 1_348.0,						lowerCamelCase							:						List[str] = 1E-5,						lowerCamelCase							:						Union[str, Any] = 1,						)->      str:
        # standard deviation of the initial noise distribution
        lowerCamelCase__	:	int				=sigma_max

        # setable values
        lowerCamelCase__	:	Any				=None

        self.set_sigmas(lowerCAmelCase_,						lowerCAmelCase_,						lowerCAmelCase_,						lowerCAmelCase_  )



     def 	snake_case ( self							:						Any,						lowerCamelCase							:						Union[str, Any],						lowerCamelCase							:						Optional[int] = None  )->      torch.FloatTensor:
        return sample



     def 	snake_case ( self							:						Tuple,						lowerCamelCase							:						Optional[int],						lowerCamelCase							:						str = None,						lowerCamelCase							:						int = None  )->      Optional[Any]:
        lowerCamelCase__	:	Optional[int]				=sampling_eps if sampling_eps is not None else self.config.sampling_eps

        lowerCamelCase__	:	List[Any]				=torch.linspace(1,						lowerCAmelCase_,						lowerCAmelCase_,						device=lowerCAmelCase_  )



     def 	snake_case ( self							:						List[str],						lowerCamelCase							:						Optional[Any],						lowerCamelCase							:						str = None,						lowerCamelCase							:						Any = None,						lowerCamelCase							:						List[str] = None  )->      int:
        lowerCamelCase__	:	List[Any]				=sigma_min if sigma_min is not None else self.config.sigma_min
        lowerCamelCase__	:	int				=sigma_max if sigma_max is not None else self.config.sigma_max
        lowerCamelCase__	:	Any				=sampling_eps if sampling_eps is not None else self.config.sampling_eps
        if self.timesteps is None:
           self.set_timesteps(lowerCAmelCase_,						lowerCAmelCase_  )

        lowerCamelCase__	:	str				=sigma_min * (sigma_max / sigma_min) ** (self.timesteps / sampling_eps)
        lowerCamelCase__	:	Tuple				=torch.exp(torch.linspace(math.log(lowerCAmelCase_  ),						math.log(lowerCAmelCase_  ),						lowerCAmelCase_  )  )
        lowerCamelCase__	:	str				=torch.tensor([sigma_min * (sigma_max / sigma_min) ** t for t in self.timesteps]  )



     def 	snake_case ( self							:						str,						lowerCamelCase							:						int,						lowerCamelCase							:						Tuple  )->      int:
        return torch.where(
            timesteps == 0,						torch.zeros_like(t.to(timesteps.device  )  ),						self.discrete_sigmas[timesteps - 1].to(timesteps.device  ),						)



     def 	snake_case ( self							:						Dict,						lowerCamelCase							:						int,						lowerCamelCase							:						List[Any],						lowerCamelCase							:						Any,						lowerCamelCase							:						Optional[int] = None,						lowerCamelCase							:						List[Any] = True,						)->      Union[SdeVeOutput, Tuple]:
        if self.timesteps is None:
           raise ValueError(
               '''`self.timesteps` is not set, you need to run \'set_timesteps\' after creating the scheduler'''  )

        lowerCamelCase__	:	int				=timestep * torch.ones(
            sample.shape[0],						device=sample.device  )  # torch.repeat_interleave(timestep, sample.shape[0])
        lowerCamelCase__	:	int				=(timestep * (len(self.timesteps  ) - 1)).long()

        # mps requires indices to be in the same device, so we use cpu as is the default with cuda
        lowerCamelCase__	:	Optional[Any]				=timesteps.to(self.discrete_sigmas.device  )

        lowerCamelCase__	:	Optional[int]				=self.discrete_sigmas[timesteps].to(sample.device  )
        lowerCamelCase__	:	str				=self.get_adjacent_sigma(lowerCAmelCase_,						lowerCAmelCase_  ).to(sample.device  )
        lowerCamelCase__	:	Any				=torch.zeros_like(lowerCAmelCase_  )
        lowerCamelCase__	:	Tuple				=(sigma**2 - adjacent_sigma**2) ** 0.5

        # equation 6 in the paper: the model_output modeled by the network is grad_x log pt(x)
        # also equation 47 shows the analog from SDE models to ancestral sampling methods
        lowerCamelCase__	:	List[str]				=diffusion.flatten()
        while len(diffusion.shape  ) < len(sample.shape  ):
           lowerCamelCase__	:	Tuple				=diffusion.unsqueeze(-1  )
        lowerCamelCase__	:	List[Any]				=drift - diffusion**2 * model_output

        #  equation 6: sample noise for the diffusion term of
        lowerCamelCase__	:	Any				=randn_tensor(
            sample.shape,						layout=sample.layout,						generator=lowerCAmelCase_,						device=sample.device,						dtype=sample.dtype  )
        lowerCamelCase__	:	str				=sample - drift  # subtract because `dt` is a small negative timestep
        # TODO is the variable diffusion the correct scaling term for the noise?
        lowerCamelCase__	:	Union[str, Any]				=prev_sample_mean + diffusion * noise  # add impact of diffusion field g

        if not return_dict:
           return (prev_sample, prev_sample_mean)

        return SdeVeOutput(prev_sample=lowerCAmelCase_,						prev_sample_mean=lowerCAmelCase_  )



     def 	snake_case ( self							:						Tuple,						lowerCamelCase							:						Dict,						lowerCamelCase							:						Union[str, Any],						lowerCamelCase							:						List[str] = None,						lowerCamelCase							:						str = True,						)->      Union[SchedulerOutput, Tuple]:
        if self.timesteps is None:
           raise ValueError(
               '''`self.timesteps` is not set, you need to run \'set_timesteps\' after creating the scheduler'''  )

        # For small batch sizes, the paper "suggest replacing norm(z) with sqrt(d), where d is the dim. of z"
        # sample noise for correction
        lowerCamelCase__	:	Optional[int]				=randn_tensor(sample.shape,						layout=sample.layout,						generator=lowerCAmelCase_  ).to(sample.device  )

        # compute step size from the model_output, the noise, and the snr
        lowerCamelCase__	:	int				=torch.norm(model_output.reshape(model_output.shape[0],						-1  ),						dim=-1  ).mean()
        lowerCamelCase__	:	Optional[Any]				=torch.norm(noise.reshape(noise.shape[0],						-1  ),						dim=-1  ).mean()
        lowerCamelCase__	:	List[str]				=(self.config.snr * noise_norm / grad_norm) ** 2 * 2
        lowerCamelCase__	:	Any				=step_size * torch.ones(sample.shape[0]  ).to(sample.device  )
        # self.repeat_scalar(step_size, sample.shape[0])

        # compute corrected sample: model_output term and noise term
        lowerCamelCase__	:	Dict				=step_size.flatten()
        while len(step_size.shape  ) < len(sample.shape  ):
           lowerCamelCase__	:	Optional[int]				=step_size.unsqueeze(-1  )
        lowerCamelCase__	:	str				=sample + step_size * model_output
        lowerCamelCase__	:	Optional[int]				=prev_sample_mean + ((step_size * 2) ** 0.5) * noise

        if not return_dict:
           return (prev_sample,)

        return SchedulerOutput(prev_sample=lowerCAmelCase_  )



     def 	snake_case ( self							:						Any,						lowerCamelCase							:						Any,						lowerCamelCase							:						List[str],						lowerCamelCase							:						Union[str, Any],						)->      torch.FloatTensor:
        # Make sure sigmas and timesteps have the same device and dtype as original_samples
        lowerCamelCase__	:	Dict				=timesteps.to(original_samples.device  )
        lowerCamelCase__	:	Dict				=self.discrete_sigmas.to(original_samples.device  )[timesteps]
        lowerCamelCase__	:	List[Any]				=(
            noise * sigmas[:, None, None, None]
            if noise is not None
            else torch.randn_like(lowerCAmelCase_  ) * sigmas[:, None, None, None]
        )
        lowerCamelCase__	:	int				=noise + original_samples
        return noisy_samples




     def __len__( self							:						Optional[int]  )->      Tuple:
        return self.config.num_train_timesteps









"""simple docstring"""






from __future__ import annotations

import unittest

from transformers import BlenderbotSmallConfig, BlenderbotSmallTokenizer, is_tf_available
from transformers.testing_utils import require_tf, require_tokenizers, slow
from transformers.utils import cached_property

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
		import tensorflow as tf

		from transformers import TFAutoModelForSeqaSeqLM, TFBlenderbotSmallForConditionalGeneration, TFBlenderbotSmallModel

@require_tf
class  __SCREAMING_SNAKE_CASE			:


					'''simple docstring'''





					_a         =							BlenderbotSmallConfig
					_a         =							{}
					_a         =							'gelu'



					def __init__( self							:						Union[str, Any],						lowerCamelCase							:						List[str],						lowerCamelCase							:						Dict=13,						lowerCamelCase							:						Optional[Any]=7,						lowerCamelCase							:						Optional[int]=True,						lowerCamelCase							:						int=False,						lowerCamelCase							:						Union[str, Any]=99,						lowerCamelCase							:						str=32,						lowerCamelCase							:						List[Any]=2,						lowerCamelCase							:						Optional[int]=4,						lowerCamelCase							:						Union[str, Any]=37,						lowerCamelCase							:						str=0.1,						lowerCamelCase							:						Optional[int]=0.1,						lowerCamelCase							:						Optional[Any]=20,						lowerCamelCase							:						int=2,						lowerCamelCase							:						Any=1,						lowerCamelCase							:						Optional[Any]=0,						)->      List[str]:
								lowerCamelCase__	:	Any				=parent
								lowerCamelCase__	:	Dict				=batch_size
								lowerCamelCase__	:	Optional[int]				=seq_length
								lowerCamelCase__	:	Tuple				=is_training
								lowerCamelCase__	:	Dict				=use_labels
								lowerCamelCase__	:	List[Any]				=vocab_size
								lowerCamelCase__	:	str				=hidden_size
								lowerCamelCase__	:	str				=num_hidden_layers
								lowerCamelCase__	:	Union[str, Any]				=num_attention_heads
								lowerCamelCase__	:	Any				=intermediate_size

								lowerCamelCase__	:	Dict				=hidden_dropout_prob
								lowerCamelCase__	:	List[Any]				=attention_probs_dropout_prob
								lowerCamelCase__	:	str				=max_position_embeddings
								lowerCamelCase__	:	Optional[int]				=eos_token_id
								lowerCamelCase__	:	str				=pad_token_id
								lowerCamelCase__	:	Union[str, Any]				=bos_token_id



					def 	snake_case ( self							:						Any  )->      Any:
								lowerCamelCase__	:	Any				=ids_tensor([self.batch_size, self.seq_length - 1],						self.vocab_size  )
								lowerCamelCase__	:	Tuple				=tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size  ),						1  )
								lowerCamelCase__	:	Any				=tf.concat([input_ids, eos_tensor],						axis=1  )

								lowerCamelCase__	:	Optional[int]				=ids_tensor([self.batch_size, self.seq_length],						self.vocab_size  )

								lowerCamelCase__	:	int				=self.config_cls(
								    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_ids=[2],						bos_token_id=self.bos_token_id,						pad_token_id=self.pad_token_id,						decoder_start_token_id=self.pad_token_id,						**self.config_updates,						)
								lowerCamelCase__	:	Optional[int]				=prepare_blenderbot_small_inputs_dict(lowerCamelCase,						lowerCamelCase,						lowerCamelCase  )
								return config, inputs_dict




					def 	snake_case ( self							:						Any,						lowerCamelCase							:						str,						lowerCamelCase							:						Any  )->      Optional[Any]:
								lowerCamelCase__	:	Union[str, Any]				=TFBlenderbotSmallModel(config=lowerCamelCase  ).get_decoder()
								lowerCamelCase__	:	List[Any]				=inputs_dict['''input_ids''']

								lowerCamelCase__	:	Optional[int]				=input_ids[:1, :]
								lowerCamelCase__	:	str				=inputs_dict['''attention_mask'''][:1, :]
								lowerCamelCase__	:	Union[str, Any]				=inputs_dict['''head_mask''']
								lowerCamelCase__	:	Optional[Any]				=1

								# first forward pass
								lowerCamelCase__	:	Dict				=model(lowerCamelCase,						attention_mask=lowerCamelCase,						head_mask=lowerCamelCase,						use_cache=lowerCamelCase  )

								lowerCamelCase__			,     lowerCamelCase__	:	List[str]				=outputs.to_tuple()

								# create hypothetical next token and extent to next_input_ids
								lowerCamelCase__	:	Union[str, Any]				=ids_tensor((self.batch_size, 3),						config.vocab_size  )
								lowerCamelCase__	:	Tuple				=tf.cast(ids_tensor((self.batch_size, 3),						2  ),						tf.inta  )

								# append to next input_ids and
								lowerCamelCase__	:	List[str]				=tf.concat([input_ids, next_tokens],						axis=-1  )
								lowerCamelCase__	:	str				=tf.concat([attention_mask, next_attn_mask],						axis=-1  )

								lowerCamelCase__	:	Optional[int]				=model(lowerCamelCase,						attention_mask=lowerCamelCase  )[0]
								lowerCamelCase__	:	Union[str, Any]				=model(lowerCamelCase,						attention_mask=lowerCamelCase,						past_key_values=lowerCamelCase  )[0]

								self.parent.assertEqual(next_tokens.shape[1],						output_from_past.shape[1]  )

								# select random slice
								lowerCamelCase__	:	Tuple				=int(ids_tensor((1,),						output_from_past.shape[-1]  )  )
								lowerCamelCase__	:	int				=output_from_no_past[:, -3:, random_slice_idx]
								lowerCamelCase__	:	List[str]				=output_from_past[:, :, random_slice_idx]

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






def   snake_case__     (	__lowerCamelCase       :					Tuple							,	__lowerCamelCase       :					Union[str, Any]							,	__lowerCamelCase       :					Union[str, Any]							,	__lowerCamelCase       :					Any=None							,	__lowerCamelCase       :					List[str]=None							,	__lowerCamelCase       :					Tuple=None							,	__lowerCamelCase       :					Dict=None							,	__lowerCamelCase       :					Optional[int]=None							,	):
			"""simple docstring"""
			if attention_mask is None:
						lowerCamelCase__	:	List[str]				=tf.cast(tf.math.not_equal(__lowerCamelCase							,	config.pad_token_id			)							,	tf.inta			)
			if decoder_attention_mask is None:
						lowerCamelCase__	:	str				=tf.concat(
						    [
						        tf.ones(decoder_input_ids[:, :1].shape							,	dtype=tf.inta			),
						        tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:]							,	config.pad_token_id			)							,	tf.inta			),
						    ]							,	axis=-1							,	)
			if head_mask is None:
						lowerCamelCase__	:	int				=tf.ones((config.encoder_layers, config.encoder_attention_heads)			)
			if decoder_head_mask is None:
						lowerCamelCase__	:	int				=tf.ones((config.decoder_layers, config.decoder_attention_heads)			)
			if cross_attn_head_mask is None:
						lowerCamelCase__	:	List[str]				=tf.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": decoder_attention_mask,
			    "head_mask": head_mask,
			    "decoder_head_mask": decoder_head_mask,
			    "cross_attn_head_mask": cross_attn_head_mask,
			}

@require_tf
class  __SCREAMING_SNAKE_CASE			(   lowerCAmelCase_	,     lowerCAmelCase_	,     unittest.TestCase     ):


					'''simple docstring'''





					_a         =							(
					    (TFBlenderbotSmallForConditionalGeneration, TFBlenderbotSmallModel) if is_tf_available() else ()
					)
					_a         =							(TFBlenderbotSmallForConditionalGeneration,) if is_tf_available() else ()
					_a         =							(
					    {
					        'conversational': TFBlenderbotSmallForConditionalGeneration,
					        'feature-extraction': TFBlenderbotSmallModel,
					        'summarization': TFBlenderbotSmallForConditionalGeneration,
					        'text2text-generation': TFBlenderbotSmallForConditionalGeneration,
					        'translation': TFBlenderbotSmallForConditionalGeneration,
					    }
					    if is_tf_available()
					    else {}
					)
					_a         =							True
					_a         =							False
					_a         =							False



					def 	snake_case ( self							:						Any  )->      str:
								lowerCamelCase__	:	Tuple				=TFBlenderbotSmallModelTester(self  )
								lowerCamelCase__	:	Union[str, Any]				=ConfigTester(self,						config_class=lowerCamelCase  )



					def 	snake_case ( self							:						Any  )->      Optional[int]:
								self.config_tester.run_common_tests()




					def 	snake_case ( self							:						int  )->      str:
								lowerCamelCase__	:	List[Any]				=self.model_tester.prepare_config_and_inputs_for_common()
								self.model_tester.check_decoder_model_past_large_inputs(*lowerCamelCase  )





@require_tokenizers
@require_tf
class  __SCREAMING_SNAKE_CASE			(   unittest.TestCase     ):


					'''simple docstring'''





					_a         =							[
					    'Social anxiety\nWow, I am never shy. Do you have anxiety?\nYes. I end up sweating and blushing and feel like '
					    '  i\'m going to throw up.\nand why is that?'
					]
					_a         =							'facebook/blenderbot_small-90M'



					@cached_property
					def 	snake_case ( self							:						Any  )->      List[Any]:
								# use "old" tokenizer here because of bug when downloading new tokenizer
								return BlenderbotSmallTokenizer.from_pretrained('''facebook/blenderbot-90M'''  )



					@cached_property
					def 	snake_case ( self							:						int  )->      List[Any]:
								lowerCamelCase__	:	str				=TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name  )
								return model




					@slow
					def 	snake_case ( self							:						Tuple  )->      int:
								lowerCamelCase__	:	Dict				=self.tokenizer(self.src_text,						return_tensors='''tf'''  )
								lowerCamelCase__	:	Any				=self.model.generate(
								    model_inputs.input_ids,						attention_mask=model_inputs.attention_mask,						num_beams=2,						use_cache=lowerCamelCase,						)
								lowerCamelCase__	:	Any				=self.tokenizer.batch_decode(generated_ids.numpy(),						skip_special_tokens=lowerCamelCase  )[0]
								assert generated_words in (
								    "i don't know. i just feel like i'm going to throw up. it's not fun.",
								    "i'm not sure. i just feel like i've been feeling like i have to be in a certain place",
								    "i'm not sure. i just feel like i've been in a bad situation.",
								)




