


'''simple docstring'''

from collections import OrderedDict
from typing import Any, List, Mapping, Optional

from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging


_UpperCAmelCase :       Tuple										= logging.get_logger(__name__)

_UpperCAmelCase :       Optional[Any]										= {
    """EleutherAI/gpt-j-6B""": """https://huggingface.co/EleutherAI/gpt-j-6B/resolve/main/config.json""",
    # See all GPT-J models at https://huggingface.co/models?filter=gpt_j
}


class 					a__ (       __A					):







				"""simple docstring"""



				__UpperCamelCase			:						List[str]		=		'gptj'
				__UpperCamelCase			:						Dict		=		{
				    'max_position_embeddings': 'n_positions',
				    'hidden_size': 'n_embd',
				    'num_attention_heads': 'n_head',
				    'num_hidden_layers': 'n_layer',
				}




				def __init__(self				,	__lowercase=5_04_00				,	__lowercase=20_48				,	__lowercase=40_96				,	__lowercase=28				,	__lowercase=16				,	__lowercase=64				,	__lowercase=None				,	__lowercase="gelu_new"				,	__lowercase=0.0				,	__lowercase=0.0				,	__lowercase=0.0				,	__lowercase=1e-5				,	__lowercase=0.0_2				,	__lowercase=True				,	__lowercase=5_02_56				,	__lowercase=5_02_56				,	__lowercase=False				,	**__lowercase				,	):
										__lowerCAmelCase											= vocab_size
										__lowerCAmelCase											= n_positions
										__lowerCAmelCase											= n_embd
										__lowerCAmelCase											= n_layer
										__lowerCAmelCase											= n_head
										__lowerCAmelCase											= n_inner
										__lowerCAmelCase											= rotary_dim
										__lowerCAmelCase											= activation_function
										__lowerCAmelCase											= resid_pdrop
										__lowerCAmelCase											= embd_pdrop
										__lowerCAmelCase											= attn_pdrop
										__lowerCAmelCase											= layer_norm_epsilon
										__lowerCAmelCase											= initializer_range
										__lowerCAmelCase											= use_cache

										__lowerCAmelCase											= bos_token_id
										__lowerCAmelCase											= eos_token_id

										super().__init__(
										    bos_token_id=lowerCAmelCase_				,	eos_token_id=lowerCAmelCase_				,	tie_word_embeddings=lowerCAmelCase_				,	**lowerCAmelCase_						)


class 					a__ (       __A					):







				"""simple docstring"""



				def __init__(self				,	__lowercase				,	__lowercase = "default"				,	__lowercase = None				,	__lowercase = False				,	):
										super().__init__(lowerCAmelCase_				,	task=lowerCAmelCase_				,	patching_specs=lowerCAmelCase_				,	use_past=lowerCAmelCase_						)
										if not getattr(self._config				,	'''pad_token_id'''				,	lowerCAmelCase_						):
																# TODO: how to do that better?
																__lowerCAmelCase											= 0




				@property
				def     _snake_case     (self						):
										__lowerCAmelCase											= OrderedDict({'''input_ids''': {0: '''batch''', 1: '''sequence'''}}						)
										if self.use_past:
																self.fill_with_past_key_values_(lowerCAmelCase_				,	direction='''inputs'''						)
																__lowerCAmelCase											= {0: "batch", 1: "past_sequence + sequence"}
										else:
																__lowerCAmelCase											= {0: "batch", 1: "sequence"}

										return common_inputs




				@property
				def     _snake_case     (self						):
										return self._config.n_layer




				@property
				def     _snake_case     (self						):
										return self._config.n_head




				def     _snake_case     (self				,	__lowercase				,	__lowercase = -1				,	__lowercase = -1				,	__lowercase = False				,	__lowercase = None				,	):
										__lowerCAmelCase											= super(lowerCAmelCase_				,	self						).generate_dummy_inputs(
										    lowerCAmelCase_				,	batch_size=lowerCAmelCase_				,	seq_length=lowerCAmelCase_				,	is_pair=lowerCAmelCase_				,	framework=lowerCAmelCase_						)

										# We need to order the input in the way they appears in the forward()
										__lowerCAmelCase											= OrderedDict({'''input_ids''': common_inputs['''input_ids''']}						)

										# Need to add the past_keys
										if self.use_past:
																if not is_torch_available():
																						raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.'''						)
																else:
																						import torch

																						__lowerCAmelCase											= common_inputs["input_ids"].shape
																						# Not using the same length for past_key_values
																						__lowerCAmelCase											= seqlen + 2
																						__lowerCAmelCase											= (
																						    batch,
																						    self.num_attention_heads,
																						    past_key_values_length,
																						    self._config.hidden_size // self.num_attention_heads,
																						)
																						__lowerCAmelCase											= [
																						    (torch.zeros(lowerCAmelCase_						), torch.zeros(lowerCAmelCase_						)) for _ in range(self.num_layers						)
																						]

										__lowerCAmelCase											= common_inputs["attention_mask"]
										if self.use_past:
																__lowerCAmelCase											= ordered_inputs["attention_mask"].dtype
																__lowerCAmelCase											= torch.cat(
																    [ordered_inputs['''attention_mask'''], torch.ones(lowerCAmelCase_				,	lowerCAmelCase_				,	dtype=lowerCAmelCase_						)]				,	dim=1						)

										return ordered_inputs




				@property
				def     _snake_case     (self						):
										return 13









"""simple docstring"""
from dataclasses import dataclass
from typing import Optional, Tuple, Union

import flax
import jax.numpy as jnp
from jax import random

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .scheduling_utils_flax import FlaxSchedulerMixin







@flax.struct.dataclass
class 			UpperCamelCase_       :
	# setable values
	__magic_name__	      =				None
	__magic_name__	      =				None
	__magic_name__	      =				None  # sigma(t_i)





	@classmethod
	def      _SCREAMING_SNAKE_CASE      (      cls					: List[str]						)						->			Optional[Any]:
				return cls()







@dataclass
class 			UpperCamelCase_       (__A     ):
	__magic_name__	      =				42
	__magic_name__	      =				42
	__magic_name__	      =				42







class 			UpperCamelCase_       (__A				,						__A     ):





	@property
	def      _SCREAMING_SNAKE_CASE      (      self					: Tuple						)						->			str:
				return True





	@register_to_config
	def __init__(      self					: List[str]	,	lowerCAmelCase_					: float = 0.0_2	,	lowerCAmelCase_					: float = 100	,	lowerCAmelCase_					: float = 1.0_0_7	,	lowerCAmelCase_					: float = 80	,	lowerCAmelCase_					: float = 0.0_5	,	lowerCAmelCase_					: float = 50	,	)						->			Union[str, Any]:
				pass





	def      _SCREAMING_SNAKE_CASE      (      self					: Union[str, Any]						)						->			Tuple:
				return KarrasVeSchedulerState.create()





	def      _SCREAMING_SNAKE_CASE      (      self					: Union[str, Any]	,	lowerCAmelCase_					: KarrasVeSchedulerState	,	lowerCAmelCase_					: int	,	lowerCAmelCase_					: Tuple = ()						)						->			KarrasVeSchedulerState:
				UpperCAmelCase_     : Dict			     =					jnp.arange(0	,	lowerCAmelCase_						)[::-1].copy()
				UpperCAmelCase_     : Dict			     =					[
				    (
				        self.config.sigma_max**2
				        * (self.config.sigma_min**2 / self.config.sigma_max**2) ** (i / (num_inference_steps - 1))
				    )
				    for i in timesteps
				]

				return state.replace(
				    num_inference_steps=lowerCAmelCase_	,	schedule=jnp.array(lowerCAmelCase_	,	dtype=jnp.floataa						)	,	timesteps=lowerCAmelCase_	,	)





	def      _SCREAMING_SNAKE_CASE      (      self					: List[str]	,	lowerCAmelCase_					: KarrasVeSchedulerState	,	lowerCAmelCase_					: jnp.ndarray	,	lowerCAmelCase_					: float	,	lowerCAmelCase_					: random.KeyArray	,	)						->			Tuple[jnp.ndarray, float]:
				if self.config.s_min <= sigma <= self.config.s_max:
							UpperCAmelCase_     : Any			     =					min(self.config.s_churn / state.num_inference_steps	,	2**0.5 - 1						)
				else:
							UpperCAmelCase_     : Optional[int]			     =					0

				# sample eps ~ N(0, S_noise^2 * I)
				UpperCAmelCase_     : List[Any]			     =					random.split(lowerCAmelCase_	,	num=1						)
				UpperCAmelCase_     : List[str]			     =					self.config.s_noise * random.normal(key=lowerCAmelCase_	,	shape=sample.shape						)
				UpperCAmelCase_     : Optional[Any]			     =					sigma + gamma * sigma
				UpperCAmelCase_     : Any			     =					sample + ((sigma_hat**2 - sigma**2) ** 0.5 * eps)

				return sample_hat, sigma_hat





	def      _SCREAMING_SNAKE_CASE      (      self					: Union[str, Any]	,	lowerCAmelCase_					: KarrasVeSchedulerState	,	lowerCAmelCase_					: jnp.ndarray	,	lowerCAmelCase_					: float	,	lowerCAmelCase_					: float	,	lowerCAmelCase_					: jnp.ndarray	,	lowerCAmelCase_					: bool = True	,	)						->			Union[FlaxKarrasVeOutput, Tuple]:
				UpperCAmelCase_     : Union[str, Any]			     =					sample_hat + sigma_hat * model_output
				UpperCAmelCase_     : List[Any]			     =					(sample_hat - pred_original_sample) / sigma_hat
				UpperCAmelCase_     : Union[str, Any]			     =					sample_hat + (sigma_prev - sigma_hat) * derivative

				if not return_dict:
							return (sample_prev, derivative, state)

				return FlaxKarrasVeOutput(prev_sample=lowerCAmelCase_	,	derivative=lowerCAmelCase_	,	state=lowerCAmelCase_						)





	def      _SCREAMING_SNAKE_CASE      (      self					: List[str]	,	lowerCAmelCase_					: KarrasVeSchedulerState	,	lowerCAmelCase_					: jnp.ndarray	,	lowerCAmelCase_					: float	,	lowerCAmelCase_					: float	,	lowerCAmelCase_					: jnp.ndarray	,	lowerCAmelCase_					: jnp.ndarray	,	lowerCAmelCase_					: jnp.ndarray	,	lowerCAmelCase_					: bool = True	,	)						->			Union[FlaxKarrasVeOutput, Tuple]:
				UpperCAmelCase_     : str			     =					sample_prev + sigma_prev * model_output
				UpperCAmelCase_     : Any			     =					(sample_prev - pred_original_sample) / sigma_prev
				UpperCAmelCase_     : int			     =					sample_hat + (sigma_prev - sigma_hat) * (0.5 * derivative + 0.5 * derivative_corr)

				if not return_dict:
							return (sample_prev, derivative, state)

				return FlaxKarrasVeOutput(prev_sample=lowerCAmelCase_	,	derivative=lowerCAmelCase_	,	state=lowerCAmelCase_						)





	def      _SCREAMING_SNAKE_CASE      (      self					: Union[str, Any]	,	lowerCAmelCase_					: KarrasVeSchedulerState	,	lowerCAmelCase_					: List[Any]	,	lowerCAmelCase_					: List[str]	,	lowerCAmelCase_					: Optional[Any]						)						->			Dict:
				raise NotImplementedError()



