





'''simple docstring'''







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

import numpy as np
import torch
from scipy import integrate

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin



@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->LMSDiscrete
class 							__SCREAMING_SNAKE_CASE			(       UpperCamelCase_      ):
						'''simple docstring'''



						lowerCAmelCase__       :      str 		=		42
						lowerCAmelCase__       :      Optional[Any] 		=		None

def 		SCREAMING_SNAKE_CASE      (					lowerCAmelCase__     :	Any       ,  lowerCAmelCase__     :	int=0.9_9_9       ,  lowerCAmelCase__     :	List[Any]="cosine"       ,  )			->						Tuple:

					'''simple docstring'''




					if alpha_transform_type == "cosine":

										def alpha_bar_fn(lowerCAmelCase__     :	Dict):
															return math.cos((t + 0.0_0_8) / 1.0_0_8 * math.pi / 2) ** 2

					elif alpha_transform_type == "exp":

										def alpha_bar_fn(lowerCAmelCase__     :	Union[str, Any]):
															return math.exp(t * -12.0)

					else:
										raise ValueError(F'''Unsupported alpha_tranform_type: {alpha_transform_type}''')

					_lowercase							:      List[str]  						=							[]
					for i in range(snake_case_):
										_lowercase							:      List[Any]  						=							i / num_diffusion_timesteps
										_lowercase							:      str  						=							(i + 1) / num_diffusion_timesteps
										betas.append(min(1 - alpha_bar_fn(snake_case_) / alpha_bar_fn(snake_case_)       ,  snake_case_))
					return torch.tensor(snake_case_       ,  dtype=torch.floataa)



class 							__SCREAMING_SNAKE_CASE			(       UpperCamelCase_							,   UpperCamelCase_      ):
						'''simple docstring'''



						lowerCAmelCase__       :      str 		=		[e.name for e in KarrasDiffusionSchedulers]
						lowerCAmelCase__       :      str 		=		1

						@register_to_config
						def __init__(							self    : List[Any]   ,UpperCamelCase    : List[str] = 1000   ,UpperCamelCase    : List[str] = 0.0_0_0_1   ,UpperCamelCase    : Union[str, Any] = 0.0_2   ,UpperCamelCase    : List[str] = "linear"   ,UpperCamelCase    : Any = None   ,UpperCamelCase    : Dict = False   ,UpperCamelCase    : List[Any] = "epsilon"   ,UpperCamelCase    : Optional[int] = "linspace"   ,UpperCamelCase    : List[Any] = 0   ,)		->  str:


											if trained_betas is not None:
																_lowercase							:      List[Any]  						=							torch.tensor(__UpperCamelCase   ,dtype=torch.floataa  )
											elif beta_schedule == "linear":
																_lowercase							:      Tuple  						=							torch.linspace(__UpperCamelCase   ,__UpperCamelCase   ,__UpperCamelCase   ,dtype=torch.floataa  )
											elif beta_schedule == "scaled_linear":
																# this schedule is very specific to the latent diffusion model.
																_lowercase							:      Any  						=							(
																    torch.linspace(beta_start**0.5   ,beta_end**0.5   ,__UpperCamelCase   ,dtype=torch.floataa  ) ** 2
																)
											elif beta_schedule == "squaredcos_cap_v2":
																# Glide cosine schedule
																_lowercase							:      Any  						=							betas_for_alpha_bar(__UpperCamelCase  )
											else:
																raise NotImplementedError(F'''{beta_schedule} does is not implemented for {self.__class__}'''  )

											_lowercase							:      Any  						=							1.0 - self.betas
											_lowercase							:      Union[str, Any]  						=							torch.cumprod(self.alphas   ,dim=0  )

											_lowercase							:      List[Any]  						=							np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5  )
											_lowercase							:      Optional[Any]  						=							np.concatenate([sigmas[::-1], [0.0]]  ).astype(np.floataa  )
											_lowercase							:      Union[str, Any]  						=							torch.from_numpy(__UpperCamelCase  )

											# setable values
											_lowercase							:      Union[str, Any]  						=							None
											_lowercase							:      str  						=							use_karras_sigmas
											self.set_timesteps(__UpperCamelCase   ,__UpperCamelCase  )
											_lowercase							:      Dict  						=							[]
											_lowercase							:      Optional[Any]  						=							False

						@property
						def   _lowerCamelCase		(							self    : Any  )		->  Union[str, Any]:


											if self.config.timestep_spacing in ["linspace", "trailing"]:
																return self.sigmas.max()

											return (self.sigmas.max() ** 2 + 1) ** 0.5

						def   _lowerCamelCase		(							self    : str   ,UpperCamelCase    : Any   ,UpperCamelCase    : Optional[int]  )		->  torch.FloatTensor:


											if isinstance(__UpperCamelCase   ,torch.Tensor  ):
																_lowercase							:      Union[str, Any]  						=							timestep.to(self.timesteps.device  )
											_lowercase							:      str  						=							(self.timesteps == timestep).nonzero().item()
											_lowercase							:      List[str]  						=							self.sigmas[step_index]
											_lowercase							:      Dict  						=							sample / ((sigma**2 + 1) ** 0.5)
											_lowercase							:      List[str]  						=							True
											return sample

						def   _lowerCamelCase		(							self    : Optional[Any]   ,UpperCamelCase    : List[Any]   ,UpperCamelCase    : Optional[Any]   ,UpperCamelCase    : Any  )		->  Optional[Any]:


											def lms_derivative(UpperCamelCase    : Tuple  ):
																_lowercase							:      int  						=							1.0
																for k in range(__UpperCamelCase  ):
																					if current_order == k:
																										continue
																					prod *= (tau - self.sigmas[t - k]) / (self.sigmas[t - current_order] - self.sigmas[t - k])
																return prod

											_lowercase							:      List[Any]  						=							integrate.quad(__UpperCamelCase   ,self.sigmas[t]   ,self.sigmas[t + 1]   ,epsrel=1e-4  )[0]

											return integrated_coeff

						def   _lowerCamelCase		(							self    : int   ,UpperCamelCase    : int   ,UpperCamelCase    : Dict = None  )		->  Dict:
											_lowercase							:      Any  						=							num_inference_steps

											# "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
											if self.config.timestep_spacing == "linspace":
																_lowercase							:      Any  						=							np.linspace(0   ,self.config.num_train_timesteps - 1   ,__UpperCamelCase   ,dtype=__UpperCamelCase  )[
																    ::-1
																].copy()
											elif self.config.timestep_spacing == "leading":
																_lowercase							:      Tuple  						=							self.config.num_train_timesteps // self.num_inference_steps
																# creates integer timesteps by multiplying by ratio
																# casting to int to avoid issues when num_inference_step is power of 3
																_lowercase							:      Any  						=							(np.arange(0   ,__UpperCamelCase  ) * step_ratio).round()[::-1].copy().astype(__UpperCamelCase  )
																timesteps += self.config.steps_offset
											elif self.config.timestep_spacing == "trailing":
																_lowercase							:      Any  						=							self.config.num_train_timesteps / self.num_inference_steps
																# creates integer timesteps by multiplying by ratio
																# casting to int to avoid issues when num_inference_step is power of 3
																_lowercase							:      Dict  						=							(np.arange(self.config.num_train_timesteps   ,0   ,-step_ratio  )).round().copy().astype(__UpperCamelCase  )
																timesteps -= 1
											else:
																raise ValueError(
																    F'''{self.config.timestep_spacing} is not supported. Please make sure to choose one of \'linspace\', \'leading\' or \'trailing\'.'''  )

											_lowercase							:      List[Any]  						=							np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5  )
											_lowercase							:      str  						=							np.log(__UpperCamelCase  )
											_lowercase							:      Any  						=							np.interp(__UpperCamelCase   ,np.arange(0   ,len(__UpperCamelCase  )  )   ,__UpperCamelCase  )

											if self.use_karras_sigmas:
																_lowercase							:      List[Any]  						=							self._convert_to_karras(in_sigmas=__UpperCamelCase  )
																_lowercase							:      List[Any]  						=							np.array([self._sigma_to_t(__UpperCamelCase   ,__UpperCamelCase  ) for sigma in sigmas]  )

											_lowercase							:      Tuple  						=							np.concatenate([sigmas, [0.0]]  ).astype(np.floataa  )

											_lowercase							:      str  						=							torch.from_numpy(__UpperCamelCase  ).to(device=__UpperCamelCase  )
											if str(__UpperCamelCase  ).startswith('mps'  ):
																# mps does not support float64
																_lowercase							:      Dict  						=							torch.from_numpy(__UpperCamelCase  ).to(__UpperCamelCase   ,dtype=torch.floataa  )
											else:
																_lowercase							:      Union[str, Any]  						=							torch.from_numpy(__UpperCamelCase  ).to(device=__UpperCamelCase  )

											_lowercase							:      Tuple  						=							[]

						def   _lowerCamelCase		(							self    : str   ,UpperCamelCase    : List[Any]   ,UpperCamelCase    : Optional[Any]  )		->  Optional[int]:
											_lowercase							:      Dict  						=							np.log(__UpperCamelCase  )

											# get distribution
											_lowercase							:      Any  						=							log_sigma - log_sigmas[:, np.newaxis]

											# get sigmas range
											_lowercase							:      Any  						=							np.cumsum((dists >= 0)   ,axis=0  ).argmax(axis=0  ).clip(max=log_sigmas.shape[0] - 2  )
											_lowercase							:      Tuple  						=							low_idx + 1

											_lowercase							:      Dict  						=							log_sigmas[low_idx]
											_lowercase							:      List[str]  						=							log_sigmas[high_idx]

											# interpolate sigmas
											_lowercase							:      Union[str, Any]  						=							(low - log_sigma) / (low - high)
											_lowercase							:      List[str]  						=							np.clip(__UpperCamelCase   ,0   ,1  )

											# transform interpolation to time range
											_lowercase							:      str  						=							(1 - w) * low_idx + w * high_idx
											_lowercase							:      Tuple  						=							t.reshape(sigma.shape  )
											return t

						def   _lowerCamelCase		(							self    : Any   ,UpperCamelCase    : Optional[int]  )		->  torch.FloatTensor:
											_lowercase							:      float  						=							in_sigmas[-1].item()
											_lowercase							:      float  						=							in_sigmas[0].item()

											_lowercase							:      List[str]  						=							7.0  # 7.0 is the value used in the paper
											_lowercase							:      Union[str, Any]  						=							np.linspace(0   ,1   ,self.num_inference_steps  )
											_lowercase							:      str  						=							sigma_min ** (1 / rho)
											_lowercase							:      Dict  						=							sigma_max ** (1 / rho)
											_lowercase							:      List[Any]  						=							(max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
											return sigmas

						def   _lowerCamelCase		(							self    : Union[str, Any]   ,UpperCamelCase    : Union[str, Any]   ,UpperCamelCase    : Dict   ,UpperCamelCase    : Union[str, Any]   ,UpperCamelCase    : List[str] = 4   ,UpperCamelCase    : Optional[Any] = True   ,)		->  Union[LMSDiscreteSchedulerOutput, Tuple]:


											if not self.is_scale_input_called:
																warnings.warn(
																    'The `scale_model_input` function should be called before `step` to ensure correct denoising. '
																    'See `StableDiffusionPipeline` for a usage example.'  )

											if isinstance(__UpperCamelCase   ,torch.Tensor  ):
																_lowercase							:      Any  						=							timestep.to(self.timesteps.device  )
											_lowercase							:      Optional[Any]  						=							(self.timesteps == timestep).nonzero().item()
											_lowercase							:      Optional[Any]  						=							self.sigmas[step_index]

											# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
											if self.config.prediction_type == "epsilon":
																_lowercase							:      Optional[Any]  						=							sample - sigma * model_output
											elif self.config.prediction_type == "v_prediction":
																# * c_out + input * c_skip
																_lowercase							:      List[Any]  						=							model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
											elif self.config.prediction_type == "sample":
																_lowercase							:      List[str]  						=							model_output
											else:
																raise ValueError(
																    F'''prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`'''  )

											# 2. Convert to an ODE derivative
											_lowercase							:      Union[str, Any]  						=							(sample - pred_original_sample) / sigma
											self.derivatives.append(__UpperCamelCase  )
											if len(self.derivatives  ) > order:
																self.derivatives.pop(0  )

											# 3. Compute linear multistep coefficients
											_lowercase							:      List[str]  						=							min(step_index + 1   ,__UpperCamelCase  )
											_lowercase							:      List[str]  						=							[self.get_lms_coefficient(__UpperCamelCase   ,__UpperCamelCase   ,__UpperCamelCase  ) for curr_order in range(__UpperCamelCase  )]

											# 4. Compute previous sample based on the derivatives path
											_lowercase							:      Optional[int]  						=							sample + sum(
											    coeff * derivative for coeff, derivative in zip(__UpperCamelCase   ,reversed(self.derivatives  )  )  )

											if not return_dict:
																return (prev_sample,)

											return LMSDiscreteSchedulerOutput(prev_sample=__UpperCamelCase   ,pred_original_sample=__UpperCamelCase  )

						def   _lowerCamelCase		(							self    : Union[str, Any]   ,UpperCamelCase    : int   ,UpperCamelCase    : Any   ,UpperCamelCase    : str   ,)		->  torch.FloatTensor:
											_lowercase							:      Optional[int]  						=							self.sigmas.to(device=original_samples.device   ,dtype=original_samples.dtype  )
											if original_samples.device.type == "mps" and torch.is_floating_point(__UpperCamelCase  ):
																# mps does not support float64
																_lowercase							:      Any  						=							self.timesteps.to(original_samples.device   ,dtype=torch.floataa  )
																_lowercase							:      Optional[Any]  						=							timesteps.to(original_samples.device   ,dtype=torch.floataa  )
											else:
																_lowercase							:      List[str]  						=							self.timesteps.to(original_samples.device  )
																_lowercase							:      Optional[int]  						=							timesteps.to(original_samples.device  )

											_lowercase							:      Union[str, Any]  						=							[(schedule_timesteps == t).nonzero().item() for t in timesteps]

											_lowercase							:      Any  						=							sigmas[step_indices].flatten()
											while len(sigma.shape  ) < len(original_samples.shape  ):
																_lowercase							:      List[Any]  						=							sigma.unsqueeze(-1  )

											_lowercase							:      Any  						=							original_samples + noise * sigma
											return noisy_samples



						def __len__(							self    : List[str]  )		->  str:
											return self.config.num_train_timesteps






'''simple docstring'''







import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import OPTConfig, is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, 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
    A						    =							'''platform'''

    import jax
    import jax.numpy as jnp

    from transformers import FlaxOPTForCausalLM, FlaxOPTModel, GPTaTokenizer

def 		SCREAMING_SNAKE_CASE      (					lowerCAmelCase__     :	Optional[int]       ,  lowerCAmelCase__     :	Optional[int]       ,  lowerCAmelCase__     :	int=None       ,  lowerCAmelCase__     :	List[Any]=None)			->						Union[str, Any]:

     '''simple docstring'''




     if attention_mask is None:
          _lowercase							:      str  						=							np.where(input_ids != config.pad_token_id       ,  1       ,  0)
     return {
         "input_ids": input_ids,
         "attention_mask": attention_mask,
     }



@require_flax
class 							__SCREAMING_SNAKE_CASE			:
      '''simple docstring'''



      def __init__(							self    : List[str]   ,UpperCamelCase    : Dict   ,UpperCamelCase    : Union[str, Any]=13   ,UpperCamelCase    : Optional[Any]=7   ,UpperCamelCase    : Optional[int]=True   ,UpperCamelCase    : Any=False   ,UpperCamelCase    : int=99   ,UpperCamelCase    : Optional[int]=16   ,UpperCamelCase    : Optional[int]=2   ,UpperCamelCase    : int=4   ,UpperCamelCase    : Optional[int]=4   ,UpperCamelCase    : Dict="gelu"   ,UpperCamelCase    : Optional[Any]=0.1   ,UpperCamelCase    : Optional[int]=0.1   ,UpperCamelCase    : str=20   ,UpperCamelCase    : List[Any]=2   ,UpperCamelCase    : Union[str, Any]=1   ,UpperCamelCase    : List[Any]=0   ,UpperCamelCase    : Optional[Any]=16   ,UpperCamelCase    : List[Any]=16   ,UpperCamelCase    : Optional[Any]=0.0_2   ,)		->  Dict:
           _lowercase							:      List[Any]  						=							parent
           _lowercase							:      Union[str, Any]  						=							batch_size
           _lowercase							:      int  						=							seq_length
           _lowercase							:      Dict  						=							is_training
           _lowercase							:      Tuple  						=							use_labels
           _lowercase							:      List[str]  						=							vocab_size
           _lowercase							:      str  						=							hidden_size
           _lowercase							:      List[str]  						=							num_hidden_layers
           _lowercase							:      List[str]  						=							num_attention_heads
           _lowercase							:      Dict  						=							intermediate_size
           _lowercase							:      Tuple  						=							hidden_act
           _lowercase							:      List[str]  						=							hidden_dropout_prob
           _lowercase							:      Optional[Any]  						=							attention_probs_dropout_prob
           _lowercase							:      List[str]  						=							max_position_embeddings
           _lowercase							:      Union[str, Any]  						=							eos_token_id
           _lowercase							:      Tuple  						=							pad_token_id
           _lowercase							:      str  						=							bos_token_id
           _lowercase							:      List[str]  						=							embed_dim
           _lowercase							:      Tuple  						=							word_embed_proj_dim
           _lowercase							:      Tuple  						=							initializer_range
           _lowercase							:      Any  						=							False

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

           _lowercase							:      Optional[int]  						=							OPTConfig(
               vocab_size=self.vocab_size   ,hidden_size=self.hidden_size   ,num_hidden_layers=self.num_hidden_layers   ,num_attention_heads=self.num_attention_heads   ,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   ,embed_dim=self.embed_dim   ,is_encoder_decoder=UpperCamelCase   ,word_embed_proj_dim=self.word_embed_proj_dim   ,initializer_range=self.initializer_range   ,use_cache=UpperCamelCase   ,)
           _lowercase							:      int  						=							prepare_opt_inputs_dict(UpperCamelCase   ,UpperCamelCase  )
           return config, inputs_dict

      def   _lowerCamelCase		(							self    : Tuple  )		->  List[str]:
           _lowercase						, _lowercase							:      Tuple  						=							self.prepare_config_and_inputs()
           return config, inputs_dict

      def   _lowerCamelCase		(							self    : Union[str, Any]   ,UpperCamelCase    : str   ,UpperCamelCase    : List[Any]   ,UpperCamelCase    : Optional[Any]  )		->  Optional[Any]:
           _lowercase							:      Dict  						=							20
           _lowercase							:      Any  						=							model_class_name(UpperCamelCase  )

           _lowercase							:      Dict  						=							inputs_dict['input_ids']
           _lowercase							:      Dict  						=							inputs_dict['attention_mask']

           _lowercase							:      Dict  						=							model.init_cache(input_ids.shape[0]   ,UpperCamelCase  )
           _lowercase							:      List[str]  						=							jnp.ones((input_ids.shape[0], max_length)   ,dtype='i4'  )

           _lowercase							:      Dict  						=							jnp.broadcast_to(
               jnp.arange(input_ids.shape[-1] - 1  )[None, :]   ,(input_ids.shape[0], input_ids.shape[-1] - 1)   ,)
           _lowercase							:      List[Any]  						=							model(
               input_ids[:, :-1]   ,attention_mask=UpperCamelCase   ,past_key_values=UpperCamelCase   ,position_ids=UpperCamelCase   ,)

           _lowercase							:      str  						=							jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]   ,dtype='i4'  )
           _lowercase							:      Optional[int]  						=							model(
               input_ids[:, -1:]   ,attention_mask=UpperCamelCase   ,past_key_values=outputs_cache.past_key_values   ,position_ids=UpperCamelCase   ,)

           _lowercase							:      Dict  						=							model(UpperCamelCase  )

           _lowercase							:      Dict  						=							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   _lowerCamelCase		(							self    : List[Any]   ,UpperCamelCase    : List[Any]   ,UpperCamelCase    : List[Any]   ,UpperCamelCase    : str  )		->  Optional[Any]:
           _lowercase							:      Optional[int]  						=							20
           _lowercase							:      Dict  						=							model_class_name(UpperCamelCase  )

           _lowercase						, _lowercase							:      Tuple  						=							(
               inputs_dict['input_ids'],
               inputs_dict['attention_mask'],
           )

           _lowercase							:      int  						=							jnp.concatenate(
               [
                   attention_mask,
                   jnp.zeros((attention_mask.shape[0], max_length - attention_mask.shape[1])  ),
               ]   ,axis=-1   ,)

           _lowercase							:      Dict  						=							model.init_cache(input_ids.shape[0]   ,UpperCamelCase  )
           _lowercase							:      Union[str, Any]  						=							jnp.broadcast_to(
               jnp.arange(input_ids.shape[-1] - 1  )[None, :]   ,(input_ids.shape[0], input_ids.shape[-1] - 1)   ,)

           _lowercase							:      List[Any]  						=							model(
               input_ids[:, :-1]   ,attention_mask=UpperCamelCase   ,past_key_values=UpperCamelCase   ,position_ids=UpperCamelCase   ,)
           _lowercase							:      Any  						=							jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]   ,dtype='i4'  )
           _lowercase							:      List[Any]  						=							model(
               input_ids[:, -1:]   ,past_key_values=outputs_cache.past_key_values   ,attention_mask=UpperCamelCase   ,position_ids=UpperCamelCase   ,)

           _lowercase							:      Tuple  						=							model(UpperCamelCase   ,attention_mask=UpperCamelCase  )

           _lowercase							:      List[Any]  						=							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 							__SCREAMING_SNAKE_CASE			(       lowerCAmelCase__							,   unittest.TestCase							,   lowerCAmelCase__      ):
      '''simple docstring'''



      lowerCAmelCase__       :      List[str] 		=		(FlaxOPTModel, FlaxOPTForCausalLM) if is_flax_available() else ()
      lowerCAmelCase__       :      List[Any] 		=		() if is_flax_available() else ()

      def   _lowerCamelCase		(							self    : Optional[int]  )		->  str:
           _lowercase							:      Dict  						=							FlaxOPTModelTester(self  )

      def   _lowerCamelCase		(							self    : int  )		->  List[str]:
           _lowercase						, _lowercase							:      List[Any]  						=							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   _lowerCamelCase		(							self    : Any  )		->  Dict:
           _lowercase						, _lowercase							:      Union[str, Any]  						=							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  )



      @slow
      def   _lowerCamelCase		(							self    : Optional[int]  )		->  int:
           for model_class_name in self.all_model_classes:
                _lowercase							:      Optional[int]  						=							model_class_name.from_pretrained('facebook/opt-125m'  )
                _lowercase							:      Any  						=							np.ones((1, 1)  ) * model.config.eos_token_id
                _lowercase							:      str  						=							model(UpperCamelCase  )
                self.assertIsNotNone(UpperCamelCase  )



@require_sentencepiece
@require_flax
class 							__SCREAMING_SNAKE_CASE			(       unittest.TestCase      ):
      '''simple docstring'''



      @slow
      def   _lowerCamelCase		(							self    : int  )		->  Optional[int]:
           _lowercase							:      Optional[int]  						=							FlaxOPTModel.from_pretrained('facebook/opt-350m'  )
           _lowercase							:      int  						=							jnp.array([[0, 3_1414, 232, 328, 740, 1140, 1_2695, 69, 4_6078, 1588, 2]]  )
           _lowercase							:      Tuple  						=							model(input_ids=UpperCamelCase  ).last_hidden_state
           _lowercase							:      Union[str, Any]  						=							(1, 11, 512)
           self.assertEqual(output.shape   ,UpperCamelCase  )
           _lowercase							:      Union[str, Any]  						=							jnp.array(
               [[-0.2_8_6_7, -1.9_2_5_6, -0.3_0_6_2], [-1.2_7_1_1, -0.1_3_3_7, -0.1_8_9_7], [0.4_1_0_9, 0.1_1_8_7, -1.3_1_4_2]]  )
           self.assertTrue(jnp.allclose(output[:, :3, :3]   ,UpperCamelCase   ,atol=4e-2  )  )



@require_flax
@slow
class 							__SCREAMING_SNAKE_CASE			(       unittest.TestCase      ):
      '''simple docstring'''



      def   _lowerCamelCase		(							self    : Optional[Any]  )		->  Tuple:
           super().setUp()
           _lowercase							:      Optional[int]  						=							'facebook/opt-350m'



      def   _lowerCamelCase		(							self    : Optional[int]  )		->  Union[str, Any]:
           _lowercase							:      Tuple  						=							FlaxOPTForCausalLM.from_pretrained(self.path_model  )
           _lowercase							:      str  						=							GPTaTokenizer.from_pretrained(self.path_model  )

           _lowercase							:      Any  						=							[
               'Today is a beautiful day and I want to',
               'In the city of',
               'Paris is the capital of France and',
               'Computers and mobile phones have taken',
           ]
           # verify that prompt without BOS token is identical to Metaseq -> add_special_tokens=False
           _lowercase							:      str  						=							tokenizer(UpperCamelCase   ,return_tensors='jax'   ,padding=UpperCamelCase   ,add_special_tokens=UpperCamelCase  )
           _lowercase							:      Optional[int]  						=							model(inputs.input_ids   ,attention_mask=inputs.attention_mask  )[0].mean(axis=-1  )
           _lowercase							:      int  						=							jnp.array(
               [
                   [1.3_8_5_1, -1_3.8_9_2_3, -1_0.5_2_2_9, -1_0.7_5_3_3, -0.2_3_0_9, -1_0.2_3_8_4, -0.5_3_6_5, -9.0_9_4_7, -5.1_6_7_0],
                   [-4.7_0_7_3, -1_0.6_2_7_6, -3.9_4_1_5, -2_1.5_2_4_2, -0.2_8_2_2, -0.2_8_2_2, -0.2_8_2_2, -0.2_8_2_2, -0.2_8_2_2],
                   [0.6_2_4_7, -3.4_2_2_9, -8.9_1_7_9, -1.4_2_9_7, -1_4.1_6_5_0, 1.4_1_4_6, -9.0_2_1_8, -0.2_7_0_3, -0.2_7_0_3],
                   [6.4_7_8_3, -1.9_9_1_3, -1_0.7_9_2_6, -2.3_3_3_6, 1.5_0_9_2, -0.9_9_7_4, -6.8_2_1_3, 1.3_4_7_7, 1.3_4_7_7],
               ]  )
           self.assertTrue(jnp.allclose(UpperCamelCase   ,UpperCamelCase   ,atol=4e-2  )  )

           _lowercase							:      Any  						=							jax.jit(UpperCamelCase  )
           _lowercase							:      Union[str, Any]  						=							model(inputs.input_ids   ,attention_mask=inputs.attention_mask  )[0].mean(axis=-1  )
           self.assertTrue(jnp.allclose(UpperCamelCase   ,UpperCamelCase   ,atol=4e-2  )  )



@require_flax
@slow
class 							__SCREAMING_SNAKE_CASE			(       unittest.TestCase      ):
      '''simple docstring'''



      @property
      def   _lowerCamelCase		(							self    : Optional[int]  )		->  Optional[int]:
           return [
               "Today is a beautiful day and I want",
               "In the city of",
               "Paris is the capital of France and",
               "Computers and mobile phones have taken",
           ]

      def   _lowerCamelCase		(							self    : int  )		->  Optional[int]:
           _lowercase							:      Dict  						=							'facebook/opt-125m'

           _lowercase							:      Optional[int]  						=							[
               'Today is a beautiful day and I want to',
               'In the city of New York, the city',
               'Paris is the capital of France and the capital',
               'Computers and mobile phones have taken over the',
           ]

           _lowercase							:      int  						=							[]

           _lowercase							:      Tuple  						=							FlaxOPTForCausalLM.from_pretrained(UpperCamelCase  )
           _lowercase							:      Union[str, Any]  						=							GPTaTokenizer.from_pretrained(UpperCamelCase  )

           for prompt in self.prompts:
                _lowercase							:      List[str]  						=							tokenizer(UpperCamelCase   ,return_tensors='jax'  ).input_ids

                _lowercase							:      Optional[Any]  						=							model.generate(UpperCamelCase   ,max_length=10  )
                _lowercase							:      Union[str, Any]  						=							generated_ids[0]

                _lowercase							:      Any  						=							tokenizer.batch_decode(UpperCamelCase   ,skip_special_tokens=UpperCamelCase  )
                predicted_outputs += generated_string

           self.assertListEqual(UpperCamelCase   ,UpperCamelCase  )

      def   _lowerCamelCase		(							self    : List[str]  )		->  Dict:
           _lowercase							:      int  						=							'facebook/opt-350m'

           _lowercase							:      Tuple  						=							[
               'Today is a beautiful day and I want to',
               'In the city of San Francisco, the city',
               'Paris is the capital of France and the capital',
               'Computers and mobile phones have taken over the',
           ]

           _lowercase							:      Union[str, Any]  						=							[]
           _lowercase							:      List[Any]  						=							FlaxOPTForCausalLM.from_pretrained(UpperCamelCase  )
           _lowercase							:      Optional[int]  						=							GPTaTokenizer.from_pretrained(UpperCamelCase  )

           for prompt in self.prompts:
                _lowercase							:      Optional[int]  						=							tokenizer(UpperCamelCase   ,return_tensors='jax'  ).input_ids

                _lowercase							:      Tuple  						=							model.generate(UpperCamelCase   ,max_length=10  )
                _lowercase							:      Any  						=							generated_ids[0]

                _lowercase							:      Tuple  						=							tokenizer.batch_decode(UpperCamelCase   ,skip_special_tokens=UpperCamelCase  )
                predicted_outputs += generated_string

           self.assertListEqual(UpperCamelCase   ,UpperCamelCase  )

      def   _lowerCamelCase		(							self    : Tuple  )		->  Union[str, Any]:
           _lowercase							:      Union[str, Any]  						=							'facebook/opt-125m'
           _lowercase							:      List[Any]  						=							[
               'Today is a beautiful day and I want to thank',
               'In the city of Rome Canaver Canaver Canaver Canaver',
           ]
           _lowercase							:      List[str]  						=							FlaxOPTForCausalLM.from_pretrained(UpperCamelCase  )
           _lowercase							:      Optional[int]  						=							GPTaTokenizer.from_pretrained(UpperCamelCase  )
           _lowercase							:      Optional[Any]  						=							tokenizer(
               [
                   'Today is a beautiful day and I want to',
                   'In the city of',
               ]   ,return_tensors='jax'   ,padding=UpperCamelCase   ,)

           _lowercase							:      str  						=							jax.jit(model.generate  )

           _lowercase							:      List[str]  						=							jit_generate(inputs['input_ids']   ,attention_mask=inputs['attention_mask']  ).sequences

           _lowercase							:      Union[str, Any]  						=							tokenizer.batch_decode(UpperCamelCase   ,skip_special_tokens=UpperCamelCase  )

           self.assertIsNotNone(UpperCamelCase   ,UpperCamelCase  )



      def   _lowerCamelCase		(							self    : Dict  )		->  List[str]:
           _lowercase							:      str  						=							'facebook/opt-350m'

           _lowercase							:      List[str]  						=							GPTaTokenizer.from_pretrained(UpperCamelCase  )
           _lowercase							:      List[str]  						=							FlaxOPTForCausalLM.from_pretrained(UpperCamelCase  )

           _lowercase							:      int  						=							'left'

           # use different length sentences to test batching
           _lowercase							:      Tuple  						=							[
               'Hello, my dog is a little',
               'Today, I',
           ]

           _lowercase							:      int  						=							tokenizer(UpperCamelCase   ,return_tensors='jax'   ,padding=UpperCamelCase  )
           _lowercase							:      Union[str, Any]  						=							inputs['input_ids']

           _lowercase							:      str  						=							model.generate(input_ids=UpperCamelCase   ,attention_mask=inputs['attention_mask']   ,trace=UpperCamelCase  )

           _lowercase							:      Tuple  						=							tokenizer(sentences[0]   ,return_tensors='jax'  ).input_ids
           _lowercase							:      Optional[Any]  						=							model.generate(input_ids=UpperCamelCase  )

           _lowercase							:      Optional[int]  						=							inputs_non_padded.shape[-1] - inputs['attention_mask'][-1].sum()
           _lowercase							:      Optional[int]  						=							tokenizer(sentences[1]   ,return_tensors='jax'  ).input_ids
           _lowercase							:      List[str]  						=							model.generate(input_ids=UpperCamelCase   ,max_length=model.config.max_length - num_paddings  )

           _lowercase							:      Dict  						=							tokenizer.batch_decode(outputs[0]   ,skip_special_tokens=UpperCamelCase  )
           _lowercase							:      Optional[Any]  						=							tokenizer.decode(output_non_padded[0][0]   ,skip_special_tokens=UpperCamelCase  )
           _lowercase							:      Optional[Any]  						=							tokenizer.decode(output_padded[0][0]   ,skip_special_tokens=UpperCamelCase  )

           _lowercase							:      Union[str, Any]  						=							[
               'Hello, my dog is a little bit of a dork.\nI\'m a little bit',
               'Today, I was in the middle of a conversation with a friend about the',
           ]
           self.assertListEqual(UpperCamelCase   ,UpperCamelCase  )
           self.assertListEqual(UpperCamelCase   ,[non_padded_sentence, padded_sentence]  )