



'''simple docstring'''





from __future__ import annotations

import unittest

from transformers import BlenderbotConfig, BlenderbotTokenizer, 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, TFBlenderbotForConditionalGeneration, TFBlenderbotModel


@require_tf
class   snake_case_ :




       """simple docstring"""






       _lowerCamelCase			    =				BlenderbotConfig
       _lowerCamelCase			    =				{}
       _lowerCamelCase			    =				"gelu"



       def __init__(     self     ,lowercase     ,lowercase=13     ,lowercase=7     ,lowercase=True     ,lowercase=False     ,lowercase=99     ,lowercase=32     ,lowercase=2     ,lowercase=4     ,lowercase=37     ,lowercase=0.1     ,lowercase=0.1     ,lowercase=20     ,lowercase=2     ,lowercase=1     ,lowercase=0     ,):

             """simple docstring"""





             UpperCAmelCase_  :	List[Any]				      =		parent
             UpperCAmelCase_  :	List[str]				      =		batch_size
             UpperCAmelCase_  :	Tuple				      =		seq_length
             UpperCAmelCase_  :	str				      =		is_training
             UpperCAmelCase_  :	List[str]				      =		use_labels
             UpperCAmelCase_  :	List[Any]				      =		vocab_size
             UpperCAmelCase_  :	Dict				      =		hidden_size
             UpperCAmelCase_  :	List[str]				      =		num_hidden_layers
             UpperCAmelCase_  :	Tuple				      =		num_attention_heads
             UpperCAmelCase_  :	Tuple				      =		intermediate_size

             UpperCAmelCase_  :	Tuple				      =		hidden_dropout_prob
             UpperCAmelCase_  :	Any				      =		attention_probs_dropout_prob
             UpperCAmelCase_  :	Optional[Any]				      =		max_position_embeddings
             UpperCAmelCase_  :	List[Any]				      =		eos_token_id
             UpperCAmelCase_  :	Union[str, Any]				      =		pad_token_id
             UpperCAmelCase_  :	Union[str, Any]				      =		bos_token_id



       def  A_   (     self):

             """simple docstring"""





             UpperCAmelCase_  :	Any				      =		ids_tensor([self.batch_size, self.seq_length - 1]     ,self.vocab_size)
             UpperCAmelCase_  :	List[str]				      =		tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size)     ,1)
             UpperCAmelCase_  :	Optional[int]				      =		tf.concat([input_ids, eos_tensor]     ,axis=1)

             UpperCAmelCase_  :	Optional[Any]				      =		ids_tensor([self.batch_size, self.seq_length]     ,self.vocab_size)

             UpperCAmelCase_  :	Union[str, Any]				      =		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     ,)
             UpperCAmelCase_  :	Union[str, Any]				      =		prepare_blenderbot_inputs_dict(UpperCamelCase_     ,UpperCamelCase_     ,UpperCamelCase_)
             return config, inputs_dict



       def  A_   (     self     ,lowercase     ,lowercase):

             """simple docstring"""





             UpperCAmelCase_  :	str				      =		TFBlenderbotModel(config=UpperCamelCase_).get_decoder()
             UpperCAmelCase_  :	Union[str, Any]				      =		inputs_dict["input_ids"]

             UpperCAmelCase_  :	Dict				      =		input_ids[:1, :]
             UpperCAmelCase_  :	int				      =		inputs_dict["attention_mask"][:1, :]
             UpperCAmelCase_  :	Union[str, Any]				      =		inputs_dict["head_mask"]
             UpperCAmelCase_  :	Union[str, Any]				      =		1

             # first forward pass
             UpperCAmelCase_  :	List[Any]				      =		model(UpperCamelCase_     ,attention_mask=UpperCamelCase_     ,head_mask=UpperCamelCase_     ,use_cache=UpperCamelCase_)

             UpperCAmelCase_  :	List[str]				      =		outputs.to_tuple()

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

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

             UpperCAmelCase_  :	Tuple				      =		model(UpperCamelCase_     ,attention_mask=UpperCamelCase_)[0]
             UpperCAmelCase_  :	Union[str, Any]				      =		model(UpperCamelCase_     ,attention_mask=UpperCamelCase_     ,past_key_values=UpperCamelCase_)[0]

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

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

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







def    _snake_case (       __snake_case					,  __snake_case					,  __snake_case					,  __snake_case=None					,  __snake_case=None					,  __snake_case=None					,  __snake_case=None					,  __snake_case=None					,  )     ->     Union[str, Any]:






      '''simple docstring'''







      if attention_mask is None:
            UpperCAmelCase_  :	Union[str, Any]				      =		tf.cast(tf.math.not_equal(lowerCamelCase__					,  config.pad_token_id    )					,  tf.inta    )
      if decoder_attention_mask is None:
            UpperCAmelCase_  :	int				      =		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:
            UpperCAmelCase_  :	Dict				      =		tf.ones((config.encoder_layers, config.encoder_attention_heads)    )
      if decoder_head_mask is None:
            UpperCAmelCase_  :	Optional[int]				      =		tf.ones((config.decoder_layers, config.decoder_attention_heads)    )
      if cross_attn_head_mask is None:
            UpperCAmelCase_  :	Optional[int]				      =		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   snake_case_ (lowercase__ , lowercase__ , unittest.TestCase  ):




       """simple docstring"""






       _lowerCamelCase			    =				(TFBlenderbotForConditionalGeneration, TFBlenderbotModel) if is_tf_available() else ()
       _lowerCamelCase			    =				(TFBlenderbotForConditionalGeneration,) if is_tf_available() else ()
       _lowerCamelCase			    =				(
           {
               "conversational": TFBlenderbotForConditionalGeneration,
               "feature-extraction": TFBlenderbotModel,
               "summarization": TFBlenderbotForConditionalGeneration,
               "text2text-generation": TFBlenderbotForConditionalGeneration,
               "translation": TFBlenderbotForConditionalGeneration,
           }
           if is_tf_available()
           else {}
       )
       _lowerCamelCase			    =				True
       _lowerCamelCase			    =				False
       _lowerCamelCase			    =				False



       def  A_   (     self):

             """simple docstring"""





             UpperCAmelCase_  :	Optional[int]				      =		TFBlenderbotModelTester(self)
             UpperCAmelCase_  :	str				      =		ConfigTester(self     ,config_class=UpperCamelCase_)



       def  A_   (     self):

             """simple docstring"""





             self.config_tester.run_common_tests()



       def  A_   (     self):

             """simple docstring"""





             UpperCAmelCase_  :	Tuple				      =		self.model_tester.prepare_config_and_inputs_for_common()
             self.model_tester.check_decoder_model_past_large_inputs(*UpperCamelCase_)


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




       """simple docstring"""






       _lowerCamelCase			    =				["My friends are cool but they eat too many carbs."]
       _lowerCamelCase			    =				"facebook/blenderbot-400M-distill"



       @cached_property
       def  A_   (     self):

             """simple docstring"""





             return BlenderbotTokenizer.from_pretrained(self.model_name)



       @cached_property
       def  A_   (     self):

             """simple docstring"""





             UpperCAmelCase_  :	Union[str, Any]				      =		TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name)
             return model



       @slow
       def  A_   (     self):

             """simple docstring"""





             UpperCAmelCase_  :	List[str]				      =		self.tokenizer(self.src_text     ,return_tensors="tf")
             UpperCAmelCase_  :	List[str]				      =		self.model.generate(
                 model_inputs.input_ids     ,)
             UpperCAmelCase_  :	int				      =		self.tokenizer.batch_decode(generated_ids.numpy()     ,skip_special_tokens=UpperCamelCase_)[0]
             assert (
                 generated_words
                 == " That's unfortunate. Are they trying to lose weight or are they just trying to be healthier?"
             )





# 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   snake_case_ (lowercase__  ):




       """simple docstring"""






       _lowerCamelCase			    =				42
       _lowerCamelCase			    =				42


class   snake_case_ (lowercase__ , lowercase__  ):




       """simple docstring"""






       _lowerCamelCase			    =				1



       @register_to_config
       def __init__(     self     ,lowercase = 2000     ,lowercase = 0.15     ,lowercase = 0.01     ,lowercase = 1348.0     ,lowercase = 1E-5     ,lowercase = 1     ,):

             """simple docstring"""





             UpperCAmelCase_  :	Optional[int]				      =		sigma_max

             # setable values
             UpperCAmelCase_  :	Optional[int]				      =		None

             self.set_sigmas(lowercase     ,lowercase     ,lowercase     ,lowercase)



       def  A_   (     self     ,lowercase     ,lowercase = None):

             """simple docstring"""





             return sample



       def  A_   (     self     ,lowercase     ,lowercase = None     ,lowercase = None):

             """simple docstring"""





             UpperCAmelCase_  :	int				      =		sampling_eps if sampling_eps is not None else self.config.sampling_eps

             UpperCAmelCase_  :	List[Any]				      =		torch.linspace(1     ,lowercase     ,lowercase     ,device=lowercase)



       def  A_   (     self     ,lowercase     ,lowercase = None     ,lowercase = None     ,lowercase = None):

             """simple docstring"""





             UpperCAmelCase_  :	Any				      =		sigma_min if sigma_min is not None else self.config.sigma_min
             UpperCAmelCase_  :	int				      =		sigma_max if sigma_max is not None else self.config.sigma_max
             UpperCAmelCase_  :	Union[str, Any]				      =		sampling_eps if sampling_eps is not None else self.config.sampling_eps
             if self.timesteps is None:
                   self.set_timesteps(lowercase     ,lowercase)

             UpperCAmelCase_  :	Union[str, Any]				      =		sigma_min * (sigma_max / sigma_min) ** (self.timesteps / sampling_eps)
             UpperCAmelCase_  :	Optional[int]				      =		torch.exp(torch.linspace(math.log(lowercase)     ,math.log(lowercase)     ,lowercase))
             UpperCAmelCase_  :	Any				      =		torch.tensor([sigma_min * (sigma_max / sigma_min) ** t for t in self.timesteps])



       def  A_   (     self     ,lowercase     ,lowercase):

             """simple docstring"""





             return torch.where(
                 timesteps == 0     ,torch.zeros_like(t.to(timesteps.device))     ,self.discrete_sigmas[timesteps - 1].to(timesteps.device)     ,)



       def  A_   (     self     ,lowercase     ,lowercase     ,lowercase     ,lowercase = None     ,lowercase = True     ,):

             """simple docstring"""





             if self.timesteps is None:
                   raise ValueError(
                       "`self.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler")

             UpperCAmelCase_  :	Optional[int]				      =		timestep * torch.ones(
                 sample.shape[0]     ,device=sample.device)  # torch.repeat_interleave(timestep, sample.shape[0])
             UpperCAmelCase_  :	Tuple				      =		(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
             UpperCAmelCase_  :	Optional[int]				      =		timesteps.to(self.discrete_sigmas.device)

             UpperCAmelCase_  :	Optional[Any]				      =		self.discrete_sigmas[timesteps].to(sample.device)
             UpperCAmelCase_  :	Optional[Any]				      =		self.get_adjacent_sigma(lowercase     ,lowercase).to(sample.device)
             UpperCAmelCase_  :	Any				      =		torch.zeros_like(lowercase)
             UpperCAmelCase_  :	Dict				      =		(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
             UpperCAmelCase_  :	Dict				      =		diffusion.flatten()
             while len(diffusion.shape) < len(sample.shape):
                   UpperCAmelCase_  :	List[str]				      =		diffusion.unsqueeze(-1)
             UpperCAmelCase_  :	List[Any]				      =		drift - diffusion**2 * model_output

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



       def  A_   (     self     ,lowercase     ,lowercase     ,lowercase = None     ,lowercase = True     ,):

             """simple docstring"""





             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
             UpperCAmelCase_  :	int				      =		randn_tensor(sample.shape     ,layout=sample.layout     ,generator=lowercase).to(sample.device)

             # compute step size from the model_output, the noise, and the snr
             UpperCAmelCase_  :	Union[str, Any]				      =		torch.norm(model_output.reshape(model_output.shape[0]     ,-1)     ,dim=-1).mean()
             UpperCAmelCase_  :	Optional[Any]				      =		torch.norm(noise.reshape(noise.shape[0]     ,-1)     ,dim=-1).mean()
             UpperCAmelCase_  :	List[Any]				      =		(self.config.snr * noise_norm / grad_norm) ** 2 * 2
             UpperCAmelCase_  :	Optional[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
             UpperCAmelCase_  :	Any				      =		step_size.flatten()
             while len(step_size.shape) < len(sample.shape):
                   UpperCAmelCase_  :	Tuple				      =		step_size.unsqueeze(-1)
             UpperCAmelCase_  :	Dict				      =		sample + step_size * model_output
             UpperCAmelCase_  :	int				      =		prev_sample_mean + ((step_size * 2) ** 0.5) * noise

             if not return_dict:
                   return (prev_sample,)

             return SchedulerOutput(prev_sample=lowercase)



       def  A_   (     self     ,lowercase     ,lowercase     ,lowercase     ,):

             """simple docstring"""





             UpperCAmelCase_  :	Any				      =		timesteps.to(original_samples.device)
             UpperCAmelCase_  :	List[str]				      =		self.discrete_sigmas.to(original_samples.device)[timesteps]
             UpperCAmelCase_  :	Tuple				      =		(
                 noise * sigmas[:, None, None, None]
                 if noise is not None
                 else torch.randn_like(lowercase) * sigmas[:, None, None, None]
             )
             UpperCAmelCase_  :	Tuple				      =		noise + original_samples
             return noisy_samples



       def __len__(     self):

             """simple docstring"""





             return self.config.num_train_timesteps
