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      lowerCamelCase_					:






    '''simple docstring'''






    a__     :						Optional[int]      =				None
    a__     :						Optional[jnp.ndarray]      =				None
    a__     :						Optional[jnp.ndarray]      =				None  # sigma(t_i)







    @classmethod
    def       UpperCamelCase__       (     cls)	->     List[Any]:
        return cls()


@dataclass
class      lowerCamelCase_					(       UpperCAmelCase_			):






    '''simple docstring'''






    a__     :						jnp.ndarray
    a__     :						jnp.ndarray
    a__     :						KarrasVeSchedulerState


class      lowerCamelCase_					(       UpperCAmelCase_						,     UpperCAmelCase_			):






    '''simple docstring'''






    @property
    def       UpperCamelCase__       (     self)	->     List[str]:
        return True







    @register_to_config
    def __init__(     self			,			__lowercase = 0.02			,			__lowercase = 100			,			__lowercase = 1.0_07			,			__lowercase = 80			,			__lowercase = 0.05			,			__lowercase = 50			,			)	->     Tuple:
        pass







    def       UpperCamelCase__       (     self)	->     int:
        return KarrasVeSchedulerState.create()







    def       UpperCamelCase__       (     self			,			__lowercase			,			__lowercase			,			__lowercase = ())	->     KarrasVeSchedulerState:
        __UpperCamelCase					:Optional[Any]        =		jnp.arange(0			,			__lowercase)[::-1].copy()
        __UpperCamelCase					:List[str]        =		[
            (
                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=__lowercase			,			schedule=jnp.array(__lowercase			,			dtype=jnp.floataa)			,			timesteps=__lowercase			,			)







    def       UpperCamelCase__       (     self			,			__lowercase			,			__lowercase			,			__lowercase			,			__lowercase			,			)	->     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					:Any        =		0

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

        return sample_hat, sigma_hat







    def       UpperCamelCase__       (     self			,			__lowercase			,			__lowercase			,			__lowercase			,			__lowercase			,			__lowercase			,			__lowercase = True			,			)	->     Union[FlaxKarrasVeOutput, Tuple]:
        __UpperCamelCase					:Optional[Any]        =		sample_hat + sigma_hat * model_output
        __UpperCamelCase					:Tuple        =		(sample_hat - pred_original_sample) / sigma_hat
        __UpperCamelCase					:str        =		sample_hat + (sigma_prev - sigma_hat) * derivative

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

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







    def       UpperCamelCase__       (     self			,			__lowercase			,			__lowercase			,			__lowercase			,			__lowercase			,			__lowercase			,			__lowercase			,			__lowercase			,			__lowercase = True			,			)	->     Union[FlaxKarrasVeOutput, Tuple]:
        __UpperCamelCase					:int        =		sample_prev + sigma_prev * model_output
        __UpperCamelCase					:Dict        =		(sample_prev - pred_original_sample) / sigma_prev
        __UpperCamelCase					:Any        =		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=__lowercase			,			derivative=__lowercase			,			state=__lowercase)







    def       UpperCamelCase__       (     self			,			__lowercase			,			__lowercase			,			__lowercase			,			__lowercase)	->     Tuple:
        raise NotImplementedError()






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


__lowercase										= logging.get_logger(__name__)

__lowercase										= {
    '''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      lowerCamelCase_					(       UpperCAmelCase_			):






    '''simple docstring'''






    a__     :						Optional[Any]      =				"""gptj"""
    a__     :						Tuple      =				{
        """max_position_embeddings""": """n_positions""",
        """hidden_size""": """n_embd""",
        """num_attention_heads""": """n_head""",
        """num_hidden_layers""": """n_layer""",
    }







    def __init__(     self			,			__lowercase=50_400			,			__lowercase=2_048			,			__lowercase=4_096			,			__lowercase=28			,			__lowercase=16			,			__lowercase=64			,			__lowercase=None			,			__lowercase="gelu_new"			,			__lowercase=0.0			,			__lowercase=0.0			,			__lowercase=0.0			,			__lowercase=1E-5			,			__lowercase=0.02			,			__lowercase=True			,			__lowercase=50_256			,			__lowercase=50_256			,			__lowercase=False			,			**__lowercase			,			)	->     Tuple:
        __UpperCamelCase					:Any        =		vocab_size
        __UpperCamelCase					:Optional[int]        =		n_positions
        __UpperCamelCase					:Tuple        =		n_embd
        __UpperCamelCase					:int        =		n_layer
        __UpperCamelCase					:Any        =		n_head
        __UpperCamelCase					:Any        =		n_inner
        __UpperCamelCase					:Dict        =		rotary_dim
        __UpperCamelCase					:Tuple        =		activation_function
        __UpperCamelCase					:Optional[Any]        =		resid_pdrop
        __UpperCamelCase					:Any        =		embd_pdrop
        __UpperCamelCase					:List[str]        =		attn_pdrop
        __UpperCamelCase					:str        =		layer_norm_epsilon
        __UpperCamelCase					:List[Any]        =		initializer_range
        __UpperCamelCase					:Dict        =		use_cache

        __UpperCamelCase					:List[Any]        =		bos_token_id
        __UpperCamelCase					:Tuple        =		eos_token_id

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


class      lowerCamelCase_					(       UpperCAmelCase_			):






    '''simple docstring'''






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







    @property
    def       UpperCamelCase__       (     self)	->     Mapping[str, Mapping[int, str]]:
        __UpperCamelCase					:Tuple        =		OrderedDict({'''input_ids''': {0: '''batch''', 1: '''sequence'''}})
        if self.use_past:
            self.fill_with_past_key_values_(__lowercase			,			direction='''inputs''')
            __UpperCamelCase					:str        =		{0: '''batch''', 1: '''past_sequence + sequence'''}
        else:
            __UpperCamelCase					:Any        =		{0: '''batch''', 1: '''sequence'''}

        return common_inputs







    @property
    def       UpperCamelCase__       (     self)	->     int:
        return self._config.n_layer







    @property
    def       UpperCamelCase__       (     self)	->     int:
        return self._config.n_head







    def       UpperCamelCase__       (     self			,			__lowercase			,			__lowercase = -1			,			__lowercase = -1			,			__lowercase = False			,			__lowercase = None			,			)	->     Mapping[str, Any]:
        __UpperCamelCase					:Optional[int]        =		super(__lowercase			,			self).generate_dummy_inputs(
            __lowercase			,			batch_size=__lowercase			,			seq_length=__lowercase			,			is_pair=__lowercase			,			framework=__lowercase)

        # We need to order the input in the way they appears in the forward()
        __UpperCamelCase					:int        =		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

                __UpperCamelCase  ,	__UpperCamelCase					:str        =		common_inputs['''input_ids'''].shape
                # Not using the same length for past_key_values
                __UpperCamelCase					:List[str]        =		seqlen + 2
                __UpperCamelCase					:Union[str, Any]        =		(
                    batch,
                    self.num_attention_heads,
                    past_key_values_length,
                    self._config.hidden_size // self.num_attention_heads,
                )
                __UpperCamelCase					:Tuple        =		[
                    (torch.zeros(__lowercase), torch.zeros(__lowercase)) for _ in range(self.num_layers)
                ]

        __UpperCamelCase					:Tuple        =		common_inputs['''attention_mask''']
        if self.use_past:
            __UpperCamelCase					:Tuple        =		ordered_inputs['''attention_mask'''].dtype
            __UpperCamelCase					:Optional[Any]        =		torch.cat(
                [ordered_inputs['''attention_mask'''], torch.ones(__lowercase			,			__lowercase			,			dtype=__lowercase)]			,			dim=1)

        return ordered_inputs







    @property
    def       UpperCamelCase__       (     self)	->     int:
        return 13





