


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 			lowercase_				(lowercase__      ):
				snake_case  						=42
				snake_case  						=None







def      _lowercase(				__a							:	List[str]						,    __a							:	Any=0.9_99						,    __a							:	Optional[int]="cosine"						,    ):
							if alpha_transform_type == "cosine":

														def alpha_bar_fn(__a							:	Dict    ):
																					return math.cos((t + 0.0_08) / 1.0_08 * math.pi / 2    ) ** 2

							elif alpha_transform_type == "exp":

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

							else:
														raise ValueError(f"""Unsupported alpha_tranform_type: {alpha_transform_type}"""    )

							a__     						=[]
							for i in range(UpperCamelCase__    ):
														a__     						=i / num_diffusion_timesteps
														a__     						=(i + 1) / num_diffusion_timesteps
														betas.append(min(1 - alpha_bar_fn(UpperCamelCase__    ) / alpha_bar_fn(UpperCamelCase__    )						,    UpperCamelCase__    )    )
							return torch.tensor(UpperCamelCase__						,    dtype=torch.floataa    )







class 			lowercase_				(lowercase__			,				lowercase__      ):
				snake_case  						=[e.name for e in KarrasDiffusionSchedulers]
				snake_case  						=1





				@register_to_config
				def __init__(							self					,     lowercase_ = 1000					,     lowercase_ = 0.00_01					,     lowercase_ = 0.02					,     lowercase_ = "linear"					,     lowercase_ = None					,     lowercase_ = False					,     lowercase_ = "epsilon"					,     lowercase_ = "linspace"					,     lowercase_ = 0					,     )     ->   List[Any]:
											if trained_betas is not None:
																		a__     						=torch.tensor(__lowerCamelCase					,     dtype=torch.floataa)
											elif beta_schedule == "linear":
																		a__     						=torch.linspace(__lowerCamelCase					,     __lowerCamelCase					,     __lowerCamelCase					,     dtype=torch.floataa)
											elif beta_schedule == "scaled_linear":
																		# this schedule is very specific to the latent diffusion model.
																		a__     						=(
																		    torch.linspace(beta_start**0.5					,     beta_end**0.5					,     __lowerCamelCase					,     dtype=torch.floataa) ** 2
																		)
											elif beta_schedule == "squaredcos_cap_v2":
																		# Glide cosine schedule
																		a__     						=betas_for_alpha_bar(__lowerCamelCase)
											else:
																		raise NotImplementedError(F"""{beta_schedule} does is not implemented for {self.__class__}""")

											a__     						=1.0 - self.betas
											a__     						=torch.cumprod(self.alphas					,     dim=0)

											a__     						=np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5)
											a__     						=np.concatenate([sigmas[::-1], [0.0]]).astype(np.floataa)
											a__     						=torch.from_numpy(__lowerCamelCase)

											# setable values
											a__     						=None
											a__     						=use_karras_sigmas
											self.set_timesteps(__lowerCamelCase					,     __lowerCamelCase)
											a__     						=[]
											a__     						=False





				@property
				def 	__UpperCamelCase						(							self)     ->   Optional[int]:
											# standard deviation of the initial noise distribution
											if self.config.timestep_spacing in ["linspace", "trailing"]:
																		return self.sigmas.max()

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





				def 	__UpperCamelCase						(							self					,     lowercase_					,     lowercase_)     ->   torch.FloatTensor:
											if isinstance(__lowerCamelCase					,     torch.Tensor):
																		a__     						=timestep.to(self.timesteps.device)
											a__     						=(self.timesteps == timestep).nonzero().item()
											a__     						=self.sigmas[step_index]
											a__     						=sample / ((sigma**2 + 1) ** 0.5)
											a__     						=True
											return sample





				def 	__UpperCamelCase						(							self					,     lowercase_					,     lowercase_					,     lowercase_)     ->   Optional[int]:

											def lms_derivative(lowercase_):
																		a__     						=1.0
																		for k in range(__lowerCamelCase):
																									if current_order == k:
																																continue
																									prod *= (tau - self.sigmas[t - k]) / (self.sigmas[t - current_order] - self.sigmas[t - k])
																		return prod

											a__     						=integrate.quad(__lowerCamelCase					,     self.sigmas[t]					,     self.sigmas[t + 1]					,     epsrel=1e-4)[0]

											return integrated_coeff





				def 	__UpperCamelCase						(							self					,     lowercase_					,     lowercase_ = None)     ->   Optional[Any]:
											a__     						=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":
																		a__     						=np.linspace(0					,     self.config.num_train_timesteps - 1					,     __lowerCamelCase					,     dtype=__lowerCamelCase)[
																		    ::-1
																		].copy()
											elif self.config.timestep_spacing == "leading":
																		a__     						=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
																		a__     						=(np.arange(0					,     __lowerCamelCase) * step_ratio).round()[::-1].copy().astype(__lowerCamelCase)
																		timesteps += self.config.steps_offset
											elif self.config.timestep_spacing == "trailing":
																		a__     						=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
																		a__     						=(np.arange(self.config.num_train_timesteps					,     0					,     -step_ratio)).round().copy().astype(__lowerCamelCase)
																		timesteps -= 1
											else:
																		raise ValueError(
																		    F"""{self.config.timestep_spacing} is not supported. Please make sure to choose one of 'linspace', 'leading' or 'trailing'.""")

											a__     						=np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5)
											a__     						=np.log(__lowerCamelCase)
											a__     						=np.interp(__lowerCamelCase					,     np.arange(0					,     len(__lowerCamelCase))					,     __lowerCamelCase)

											if self.use_karras_sigmas:
																		a__     						=self._convert_to_karras(in_sigmas=__lowerCamelCase)
																		a__     						=np.array([self._sigma_to_t(__lowerCamelCase					,     __lowerCamelCase) for sigma in sigmas])

											a__     						=np.concatenate([sigmas, [0.0]]).astype(np.floataa)

											a__     						=torch.from_numpy(__lowerCamelCase).to(device=__lowerCamelCase)
											if str(__lowerCamelCase).startswith('mps'):
																		# mps does not support float64
																		a__     						=torch.from_numpy(__lowerCamelCase).to(__lowerCamelCase					,     dtype=torch.floataa)
											else:
																		a__     						=torch.from_numpy(__lowerCamelCase).to(device=__lowerCamelCase)

											a__     						=[]





				def 	__UpperCamelCase						(							self					,     lowercase_					,     lowercase_)     ->   List[Any]:
											# get log sigma
											a__     						=np.log(__lowerCamelCase)

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

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

											a__     						=log_sigmas[low_idx]
											a__     						=log_sigmas[high_idx]

											# interpolate sigmas
											a__     						=(low - log_sigma) / (low - high)
											a__     						=np.clip(__lowerCamelCase					,     0					,     1)

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





				def 	__UpperCamelCase						(							self					,     lowercase_)     ->   torch.FloatTensor:
											a__     						=in_sigmas[-1].item()
											a__     						=in_sigmas[0].item()

											a__     						=7.0  # 7.0 is the value used in the paper
											a__     						=np.linspace(0					,     1					,     self.num_inference_steps)
											a__     						=sigma_min ** (1 / rho)
											a__     						=sigma_max ** (1 / rho)
											a__     						=(max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
											return sigmas





				def 	__UpperCamelCase						(							self					,     lowercase_					,     lowercase_					,     lowercase_					,     lowercase_ = 4					,     lowercase_ = 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(__lowerCamelCase					,     torch.Tensor):
																		a__     						=timestep.to(self.timesteps.device)
											a__     						=(self.timesteps == timestep).nonzero().item()
											a__     						=self.sigmas[step_index]

											# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
											if self.config.prediction_type == "epsilon":
																		a__     						=sample - sigma * model_output
											elif self.config.prediction_type == "v_prediction":
																		# * c_out + input * c_skip
																		a__     						=model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
											elif self.config.prediction_type == "sample":
																		a__     						=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
											a__     						=(sample - pred_original_sample) / sigma
											self.derivatives.append(__lowerCamelCase)
											if len(self.derivatives) > order:
																		self.derivatives.pop(0)

											# 3. Compute linear multistep coefficients
											a__     						=min(step_index + 1					,     __lowerCamelCase)
											a__     						=[self.get_lms_coefficient(__lowerCamelCase					,     __lowerCamelCase					,     __lowerCamelCase) for curr_order in range(__lowerCamelCase)]

											# 4. Compute previous sample based on the derivatives path
											a__     						=sample + sum(
											    coeff * derivative for coeff, derivative in zip(__lowerCamelCase					,     reversed(self.derivatives)))

											if not return_dict:
																		return (prev_sample,)

											return LMSDiscreteSchedulerOutput(prev_sample=__lowerCamelCase					,     pred_original_sample=__lowerCamelCase)





				def 	__UpperCamelCase						(							self					,     lowercase_					,     lowercase_					,     lowercase_					,     )     ->   torch.FloatTensor:
											# Make sure sigmas and timesteps have the same device and dtype as original_samples
											a__     						=self.sigmas.to(device=original_samples.device					,     dtype=original_samples.dtype)
											if original_samples.device.type == "mps" and torch.is_floating_point(__lowerCamelCase):
																		# mps does not support float64
																		a__     						=self.timesteps.to(original_samples.device					,     dtype=torch.floataa)
																		a__     						=timesteps.to(original_samples.device					,     dtype=torch.floataa)
											else:
																		a__     						=self.timesteps.to(original_samples.device)
																		a__     						=timesteps.to(original_samples.device)

											a__     						=[(schedule_timesteps == t).nonzero().item() for t in timesteps]

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

											a__     						=original_samples + noise * sigma
											return noisy_samples





				def __len__(							self)     ->   Dict:
											return self.config.num_train_timesteps





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
	_lowerCAmelCase:	Tuple      							=					'platform'

	import jax
	import jax.numpy as jnp

	from transformers import FlaxOPTForCausalLM, FlaxOPTModel, GPTaTokenizer






def      _lowercase(				__a							:	Any						,    __a							:	Any						,    __a							:	Tuple=None						,    __a							:	List[str]=None    ):
							if attention_mask is None:
														a__     						=np.where(input_ids != config.pad_token_id						,    1						,    0    )
							return {
							    "input_ids": input_ids,
							    "attention_mask": attention_mask,
							}




@require_flax
class 			lowercase_				:





				def __init__(							self					,     lowercase_					,     lowercase_=13					,     lowercase_=7					,     lowercase_=True					,     lowercase_=False					,     lowercase_=99					,     lowercase_=16					,     lowercase_=2					,     lowercase_=4					,     lowercase_=4					,     lowercase_="gelu"					,     lowercase_=0.1					,     lowercase_=0.1					,     lowercase_=20					,     lowercase_=2					,     lowercase_=1					,     lowercase_=0					,     lowercase_=16					,     lowercase_=16					,     lowercase_=0.02					,     )     ->   Union[str, Any]:
											a__     						=parent
											a__     						=batch_size
											a__     						=seq_length
											a__     						=is_training
											a__     						=use_labels
											a__     						=vocab_size
											a__     						=hidden_size
											a__     						=num_hidden_layers
											a__     						=num_attention_heads
											a__     						=intermediate_size
											a__     						=hidden_act
											a__     						=hidden_dropout_prob
											a__     						=attention_probs_dropout_prob
											a__     						=max_position_embeddings
											a__     						=eos_token_id
											a__     						=pad_token_id
											a__     						=bos_token_id
											a__     						=embed_dim
											a__     						=word_embed_proj_dim
											a__     						=initializer_range
											a__     						=False





				def 	__UpperCamelCase						(							self)     ->   Optional[int]:
											a__     						=np.clip(ids_tensor([self.batch_size, self.seq_length - 1]					,     self.vocab_size)					,     3					,     self.vocab_size)
											a__     						=np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)					,     dtype=np.intaa))					,     -1)

											a__     						=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=lowercase_					,     word_embed_proj_dim=self.word_embed_proj_dim					,     initializer_range=self.initializer_range					,     use_cache=lowercase_					,     )
											a__     						=prepare_opt_inputs_dict(lowercase_					,     lowercase_)
											return config, inputs_dict





				def 	__UpperCamelCase						(							self)     ->   List[Any]:
											a__						,    a__     						=self.prepare_config_and_inputs()
											return config, inputs_dict





				def 	__UpperCamelCase						(							self					,     lowercase_					,     lowercase_					,     lowercase_)     ->   Any:
											a__     						=20
											a__     						=model_class_name(lowercase_)

											a__     						=inputs_dict['input_ids']
											a__     						=inputs_dict['attention_mask']

											a__     						=model.init_cache(input_ids.shape[0]					,     lowercase_)
											a__     						=jnp.ones((input_ids.shape[0], max_length)					,     dtype='i4')

											a__     						=jnp.broadcast_to(
											    jnp.arange(input_ids.shape[-1] - 1)[None, :]					,     (input_ids.shape[0], input_ids.shape[-1] - 1)					,     )
											a__     						=model(
											    input_ids[:, :-1]					,     attention_mask=lowercase_					,     past_key_values=lowercase_					,     position_ids=lowercase_					,     )

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

											a__     						=model(lowercase_)

											a__     						=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					,     lowercase_					,     lowercase_					,     lowercase_)     ->   Optional[Any]:
											a__     						=20
											a__     						=model_class_name(lowercase_)

											a__						,    a__     						=(
											    inputs_dict['input_ids'],
											    inputs_dict['attention_mask'],
											)

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

											a__     						=model.init_cache(input_ids.shape[0]					,     lowercase_)
											a__     						=jnp.broadcast_to(
											    jnp.arange(input_ids.shape[-1] - 1)[None, :]					,     (input_ids.shape[0], input_ids.shape[-1] - 1)					,     )

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

											a__     						=model(lowercase_					,     attention_mask=lowercase_)

											a__     						=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 			lowercase_				(lowercase__			,				unittest.TestCase			,				lowercase__      ):
				snake_case  						=(FlaxOPTModel, FlaxOPTForCausalLM) if is_flax_available() else ()
				snake_case  						=() if is_flax_available() else ()





				def 	__UpperCamelCase						(							self)     ->   Optional[Any]:
											a__     						=FlaxOPTModelTester(self)





				def 	__UpperCamelCase						(							self)     ->   Optional[Any]:
											a__						,    a__     						=self.model_tester.prepare_config_and_inputs()
											for model_class in self.all_model_classes:
																		self.model_tester.check_use_cache_forward(lowercase_					,     lowercase_					,     lowercase_)





				def 	__UpperCamelCase						(							self)     ->   List[Any]:
											a__						,    a__     						=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(lowercase_					,     lowercase_					,     lowercase_)





				@slow
				def 	__UpperCamelCase						(							self)     ->   Tuple:
											for model_class_name in self.all_model_classes:
																		a__     						=model_class_name.from_pretrained('facebook/opt-125m')
																		a__     						=np.ones((1, 1)) * model.config.eos_token_id
																		a__     						=model(lowercase_)
																		self.assertIsNotNone(lowercase_)







@require_sentencepiece
@require_flax
class 			lowercase_				(unittest.TestCase      ):





				@slow
				def 	__UpperCamelCase						(							self)     ->   Union[str, Any]:
											a__     						=FlaxOPTModel.from_pretrained('facebook/opt-350m')
											a__     						=jnp.array([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]])
											a__     						=model(input_ids=lowercase_).last_hidden_state
											a__     						=(1, 11, 512)
											self.assertEqual(output.shape					,     lowercase_)
											a__     						=jnp.array(
											    [[-0.28_67, -1.92_56, -0.30_62], [-1.27_11, -0.13_37, -0.18_97], [0.41_09, 0.11_87, -1.31_42]])
											self.assertTrue(jnp.allclose(output[:, :3, :3]					,     lowercase_					,     atol=4e-2))







@require_flax
@slow
class 			lowercase_				(unittest.TestCase      ):





				def 	__UpperCamelCase						(							self)     ->   Optional[Any]:
											super().setUp()
											a__     						='facebook/opt-350m'





				def 	__UpperCamelCase						(							self)     ->   List[Any]:
											a__     						=FlaxOPTForCausalLM.from_pretrained(self.path_model)
											a__     						=GPTaTokenizer.from_pretrained(self.path_model)

											a__     						=[
											    '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
											a__     						=tokenizer(lowercase_					,     return_tensors='jax'					,     padding=lowercase_					,     add_special_tokens=lowercase_)
											a__     						=model(inputs.input_ids					,     attention_mask=inputs.attention_mask)[0].mean(axis=-1)
											a__     						=jnp.array(
											    [
											        [1.38_51, -13.89_23, -10.52_29, -10.75_33, -0.23_09, -10.23_84, -0.53_65, -9.09_47, -5.16_70],
											        [-4.70_73, -10.62_76, -3.94_15, -21.52_42, -0.28_22, -0.28_22, -0.28_22, -0.28_22, -0.28_22],
											        [0.62_47, -3.42_29, -8.91_79, -1.42_97, -14.16_50, 1.41_46, -9.02_18, -0.27_03, -0.27_03],
											        [6.47_83, -1.99_13, -10.79_26, -2.33_36, 1.50_92, -0.99_74, -6.82_13, 1.34_77, 1.34_77],
											    ])
											self.assertTrue(jnp.allclose(lowercase_					,     lowercase_					,     atol=4e-2))

											a__     						=jax.jit(lowercase_)
											a__     						=model(inputs.input_ids					,     attention_mask=inputs.attention_mask)[0].mean(axis=-1)
											self.assertTrue(jnp.allclose(lowercase_					,     lowercase_					,     atol=4e-2))







@require_flax
@slow
class 			lowercase_				(unittest.TestCase      ):





				@property
				def 	__UpperCamelCase						(							self)     ->   List[Any]:
											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 	__UpperCamelCase						(							self)     ->   List[str]:
											a__     						='facebook/opt-125m'

											a__     						=[
											    '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',
											]

											a__     						=[]

											a__     						=FlaxOPTForCausalLM.from_pretrained(lowercase_)
											a__     						=GPTaTokenizer.from_pretrained(lowercase_)

											for prompt in self.prompts:
																		a__     						=tokenizer(lowercase_					,     return_tensors='jax').input_ids

																		a__     						=model.generate(lowercase_					,     max_length=10)
																		a__     						=generated_ids[0]

																		a__     						=tokenizer.batch_decode(lowercase_					,     skip_special_tokens=lowercase_)
																		predicted_outputs += generated_string

											self.assertListEqual(lowercase_					,     lowercase_)





				def 	__UpperCamelCase						(							self)     ->   List[Any]:
											a__     						='facebook/opt-350m'

											a__     						=[
											    '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',
											]

											a__     						=[]
											a__     						=FlaxOPTForCausalLM.from_pretrained(lowercase_)
											a__     						=GPTaTokenizer.from_pretrained(lowercase_)

											for prompt in self.prompts:
																		a__     						=tokenizer(lowercase_					,     return_tensors='jax').input_ids

																		a__     						=model.generate(lowercase_					,     max_length=10)
																		a__     						=generated_ids[0]

																		a__     						=tokenizer.batch_decode(lowercase_					,     skip_special_tokens=lowercase_)
																		predicted_outputs += generated_string

											self.assertListEqual(lowercase_					,     lowercase_)





				def 	__UpperCamelCase						(							self)     ->   Optional[Any]:
											a__     						='facebook/opt-125m'
											a__     						=[
											    'Today is a beautiful day and I want to thank',
											    'In the city of Rome Canaver Canaver Canaver Canaver',
											]
											a__     						=FlaxOPTForCausalLM.from_pretrained(lowercase_)
											a__     						=GPTaTokenizer.from_pretrained(lowercase_)
											a__     						=tokenizer(
											    [
											        'Today is a beautiful day and I want to',
											        'In the city of',
											    ]					,     return_tensors='jax'					,     padding=lowercase_					,     )

											a__     						=jax.jit(model.generate)

											a__     						=jit_generate(inputs['input_ids']					,     attention_mask=inputs['attention_mask']).sequences

											a__     						=tokenizer.batch_decode(lowercase_					,     skip_special_tokens=lowercase_)

											self.assertIsNotNone(lowercase_					,     lowercase_)





				def 	__UpperCamelCase						(							self)     ->   int:
											a__     						='facebook/opt-350m'

											a__     						=GPTaTokenizer.from_pretrained(lowercase_)
											a__     						=FlaxOPTForCausalLM.from_pretrained(lowercase_)

											a__     						='left'

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

											a__     						=tokenizer(lowercase_					,     return_tensors='jax'					,     padding=lowercase_)
											a__     						=inputs['input_ids']

											a__     						=model.generate(input_ids=lowercase_					,     attention_mask=inputs['attention_mask']					,     trace=lowercase_)

											a__     						=tokenizer(sentences[0]					,     return_tensors='jax').input_ids
											a__     						=model.generate(input_ids=lowercase_)

											a__     						=inputs_non_padded.shape[-1] - inputs['attention_mask'][-1].sum()
											a__     						=tokenizer(sentences[1]					,     return_tensors='jax').input_ids
											a__     						=model.generate(input_ids=lowercase_					,     max_length=model.config.max_length - num_paddings)

											a__     						=tokenizer.batch_decode(outputs[0]					,     skip_special_tokens=lowercase_)
											a__     						=tokenizer.decode(output_non_padded[0][0]					,     skip_special_tokens=lowercase_)
											a__     						=tokenizer.decode(output_padded[0][0]					,     skip_special_tokens=lowercase_)

											a__     						=[
											    '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(lowercase_					,     lowercase_)
											self.assertListEqual(lowercase_					,     [non_padded_sentence, padded_sentence])

