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

from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...file_utils import TensorType, is_torch_available
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import logging


a_						     =    logging.get_logger(__name__)

a_						     =    {
    'facebook/blenderbot_small-90M': 'https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/config.json',
    # See all BlenderbotSmall models at https://huggingface.co/models?filter=blenderbot_small
}






class _UpperCamelCase							(							__A   ):
			'''simple docstring'''







			lowerCamelCase__           ='blenderbot-small'
			lowerCamelCase__           =['past_key_values']
			lowerCamelCase__           ={'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}
			def __init__(			self							: Any      ,      a							: Optional[Any]=5_0265      ,      a							: Optional[Any]=512      ,      a							: List[Any]=8      ,      a							: Dict=2048      ,      a							: Any=16      ,      a							: str=8      ,      a							: Optional[Any]=2048      ,      a							: Optional[int]=16      ,      a							: Union[str, Any]=0.0      ,      a							: str=0.0      ,      a							: Any=True      ,      a							: Union[str, Any]=True      ,      a							: int="gelu"      ,      a							: Any=512      ,      a							: List[str]=0.1      ,      a							: Optional[Any]=0.0      ,      a							: str=0.0      ,      a							: str=0.02      ,      a							: List[str]=1      ,      a							: Union[str, Any]=False      ,      a							: str=0      ,      a							: str=1      ,      a							: List[str]=2      ,      a							: List[str]=2      ,      **a							: List[str]      ,      )							->       List[str]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Any				     =       vocab_size
				SCREAMING_SNAKE_CASE   :      Tuple				     =       max_position_embeddings
				SCREAMING_SNAKE_CASE   :      List[str]				     =       d_model
				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       encoder_ffn_dim
				SCREAMING_SNAKE_CASE   :      List[str]				     =       encoder_layers
				SCREAMING_SNAKE_CASE   :      Dict				     =       encoder_attention_heads
				SCREAMING_SNAKE_CASE   :      Optional[int]				     =       decoder_ffn_dim
				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       decoder_layers
				SCREAMING_SNAKE_CASE   :      Tuple				     =       decoder_attention_heads
				SCREAMING_SNAKE_CASE   :      str				     =       dropout
				SCREAMING_SNAKE_CASE   :      Tuple				     =       attention_dropout
				SCREAMING_SNAKE_CASE   :      str				     =       activation_dropout
				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       activation_function
				SCREAMING_SNAKE_CASE   :      int				     =       init_std
				SCREAMING_SNAKE_CASE   :      List[Any]				     =       encoder_layerdrop
				SCREAMING_SNAKE_CASE   :      int				     =       decoder_layerdrop
				SCREAMING_SNAKE_CASE   :      Any				     =       use_cache
				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       encoder_layers
				SCREAMING_SNAKE_CASE   :      List[Any]				     =       scale_embedding  # scale factor will be sqrt(d_model) if True

				super().__init__(
				    pad_token_id=a      ,      bos_token_id=a      ,      eos_token_id=a      ,      is_encoder_decoder=a      ,      decoder_start_token_id=a      ,      forced_eos_token_id=a      ,      **a      ,      )






class _UpperCamelCase							(							__A   ):
			'''simple docstring'''







			@property
			def 							__UpperCamelCase (			self							: Optional[Any]	)							->       Mapping[str, Mapping[int, str]]:

				"""simple docstring"""

				if self.task in ["default", "seq2seq-lm"]:
					SCREAMING_SNAKE_CASE   :      Tuple				     =       OrderedDict(
					    [
					        ("input_ids", {0: "batch", 1: "encoder_sequence"}),
					        ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
					    ]	)

					if self.use_past:
						SCREAMING_SNAKE_CASE   :      Optional[int]				     =       {0: "batch"}
						SCREAMING_SNAKE_CASE   :      Dict				     =       {0: "batch", 1: "past_decoder_sequence + sequence"}
					else:
						SCREAMING_SNAKE_CASE   :      List[str]				     =       {0: "batch", 1: "decoder_sequence"}
						SCREAMING_SNAKE_CASE   :      Optional[int]				     =       {0: "batch", 1: "decoder_sequence"}

					if self.use_past:
						self.fill_with_past_key_values_(a      ,      direction="inputs"	)
				elif self.task == "causal-lm":
					# TODO: figure this case out.
					SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       OrderedDict(
					    [
					        ("input_ids", {0: "batch", 1: "encoder_sequence"}),
					        ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
					    ]	)
					if self.use_past:
						SCREAMING_SNAKE_CASE   :      Tuple				     =       self.num_layers
						for i in range(a	):
							SCREAMING_SNAKE_CASE   :      List[Any]				     =       {0: "batch", 2: "past_sequence + sequence"}
							SCREAMING_SNAKE_CASE   :      int				     =       {0: "batch", 2: "past_sequence + sequence"}
				else:
					SCREAMING_SNAKE_CASE   :      int				     =       OrderedDict(
					    [
					        ("input_ids", {0: "batch", 1: "encoder_sequence"}),
					        ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
					        ("decoder_input_ids", {0: "batch", 1: "decoder_sequence"}),
					        ("decoder_attention_mask", {0: "batch", 1: "decoder_sequence"}),
					    ]	)

				return common_inputs
			@property
			def 							__UpperCamelCase (			self							: int	)							->       Mapping[str, Mapping[int, str]]:

				"""simple docstring"""

				if self.task in ["default", "seq2seq-lm"]:
					SCREAMING_SNAKE_CASE   :      int				     =       super().outputs
				else:
					SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       super(a      ,      self	).outputs
					if self.use_past:
						SCREAMING_SNAKE_CASE   :      Dict				     =       self.num_layers
						for i in range(a	):
							SCREAMING_SNAKE_CASE   :      int				     =       {0: "batch", 2: "past_sequence + sequence"}
							SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       {0: "batch", 2: "past_sequence + sequence"}
				return common_outputs
			def 							__UpperCamelCase (			self							: Any      ,      a							: PreTrainedTokenizer      ,      a							: int = -1      ,      a							: int = -1      ,      a							: bool = False      ,      a							: Optional[TensorType] = None      ,      )							->       Mapping[str, Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Any				     =       self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
				    a      ,      a      ,      a      ,      a      ,      a	)

				# Generate decoder inputs
				SCREAMING_SNAKE_CASE   :      Tuple				     =       seq_length if not self.use_past else 1
				SCREAMING_SNAKE_CASE   :      List[str]				     =       self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
				    a      ,      a      ,      a      ,      a      ,      a	)
				SCREAMING_SNAKE_CASE   :      Optional[int]				     =       {F"decoder_{name}": tensor for name, tensor in decoder_inputs.items()}
				SCREAMING_SNAKE_CASE   :      List[Any]				     =       dict(**a      ,      **a	)

				if self.use_past:
					if not is_torch_available():
						raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed."	)
					else:
						import torch
					SCREAMING_SNAKE_CASE   :      Tuple				     =       common_inputs["input_ids"].shape
					SCREAMING_SNAKE_CASE   :      Any				     =       common_inputs["decoder_input_ids"].shape[1]
					SCREAMING_SNAKE_CASE   :      Dict				     =       self.num_attention_heads
					SCREAMING_SNAKE_CASE   :      List[str]				     =       (
					    batch,
					    num_encoder_attention_heads,
					    encoder_seq_length,
					    self._config.hidden_size // num_encoder_attention_heads,
					)
					SCREAMING_SNAKE_CASE   :      Dict				     =       decoder_seq_length + 3
					SCREAMING_SNAKE_CASE   :      List[Any]				     =       (
					    batch,
					    num_decoder_attention_heads,
					    decoder_past_length,
					    self._config.hidden_size // num_decoder_attention_heads,
					)

					SCREAMING_SNAKE_CASE   :      List[Any]				     =       torch.cat(
					    [common_inputs["decoder_attention_mask"], torch.ones(a      ,      a	)]      ,      dim=1	)

					SCREAMING_SNAKE_CASE   :      str				     =       []
					# If the number of encoder and decoder layers are present in the model configuration, both are considered
					SCREAMING_SNAKE_CASE   :      List[Any]				     =       self.num_layers
					SCREAMING_SNAKE_CASE   :      Tuple				     =       min(a      ,      a	)
					SCREAMING_SNAKE_CASE   :      List[Any]				     =       max(a      ,      a	) - min_num_layers
					SCREAMING_SNAKE_CASE   :      int				     =       "encoder" if num_encoder_layers > num_decoder_layers else "decoder"

					for _ in range(a	):
						common_inputs["past_key_values"].append(
						    (
						        torch.zeros(a	),
						        torch.zeros(a	),
						        torch.zeros(a	),
						        torch.zeros(a	),
						    )	)
					# TODO: test this.
					SCREAMING_SNAKE_CASE   :      Any				     =       encoder_shape if remaining_side_name == "encoder" else decoder_shape
					for _ in range(a      ,      a	):
						common_inputs["past_key_values"].append((torch.zeros(a	), torch.zeros(a	))	)
				return common_inputs
			def 							__UpperCamelCase (			self							: Optional[Any]      ,      a							: PreTrainedTokenizer      ,      a							: int = -1      ,      a							: int = -1      ,      a							: bool = False      ,      a							: Optional[TensorType] = None      ,      )							->       Mapping[str, Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
				    a      ,      a      ,      a      ,      a      ,      a	)

				if self.use_past:
					if not is_torch_available():
						raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed."	)
					else:
						import torch
					SCREAMING_SNAKE_CASE   :      Tuple				     =       common_inputs["input_ids"].shape
					# Not using the same length for past_key_values
					SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       seqlen + 2
					SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       self.num_layers
					SCREAMING_SNAKE_CASE   :      Optional[int]				     =       self.num_attention_heads
					SCREAMING_SNAKE_CASE   :      Optional[int]				     =       (
					    batch,
					    num_encoder_attention_heads,
					    past_key_values_length,
					    self._config.hidden_size // num_encoder_attention_heads,
					)

					SCREAMING_SNAKE_CASE   :      List[Any]				     =       common_inputs["attention_mask"].dtype
					SCREAMING_SNAKE_CASE   :      List[str]				     =       torch.cat(
					    [common_inputs["attention_mask"], torch.ones(a      ,      a      ,      dtype=a	)]      ,      dim=1	)
					SCREAMING_SNAKE_CASE   :      str				     =       [
					    (torch.zeros(a	), torch.zeros(a	)) for _ in range(a	)
					]
				return common_inputs
			def 							__UpperCamelCase (			self							: List[Any]      ,      a							: PreTrainedTokenizer      ,      a							: int = -1      ,      a							: int = -1      ,      a							: bool = False      ,      a							: Optional[TensorType] = None      ,      )							->       Mapping[str, Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      str				     =       compute_effective_axis_dimension(
				    a      ,      fixed_dimension=OnnxConfig.default_fixed_batch      ,      num_token_to_add=0	)

				# If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
				SCREAMING_SNAKE_CASE   :      List[Any]				     =       tokenizer.num_special_tokens_to_add(a	)
				SCREAMING_SNAKE_CASE   :      Any				     =       compute_effective_axis_dimension(
				    a      ,      fixed_dimension=OnnxConfig.default_fixed_sequence      ,      num_token_to_add=a	)

				# Generate dummy inputs according to compute batch and sequence
				SCREAMING_SNAKE_CASE   :      List[str]				     =       [" ".join([tokenizer.unk_token]	) * seq_length] * batch_size
				SCREAMING_SNAKE_CASE   :      List[str]				     =       dict(tokenizer(a      ,      return_tensors=a	)	)
				return common_inputs
			def 							__UpperCamelCase (			self							: Tuple      ,      a							: PreTrainedTokenizer      ,      a							: int = -1      ,      a							: int = -1      ,      a							: bool = False      ,      a							: Optional[TensorType] = None      ,      )							->       Mapping[str, Any]:

				"""simple docstring"""

				if self.task in ["default", "seq2seq-lm"]:
					SCREAMING_SNAKE_CASE   :      Tuple				     =       self._generate_dummy_inputs_for_default_and_seqaseq_lm(
					    a      ,      batch_size=a      ,      seq_length=a      ,      is_pair=a      ,      framework=a	)

				elif self.task == "causal-lm":
					SCREAMING_SNAKE_CASE   :      str				     =       self._generate_dummy_inputs_for_causal_lm(
					    a      ,      batch_size=a      ,      seq_length=a      ,      is_pair=a      ,      framework=a	)
				else:
					SCREAMING_SNAKE_CASE   :      Optional[int]				     =       self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
					    a      ,      batch_size=a      ,      seq_length=a      ,      is_pair=a      ,      framework=a	)

				return common_inputs






			def 							__UpperCamelCase (			self							: Union[str, Any]      ,      a							: Tuple      ,      a							: List[str]      ,      a							: int      ,      a							: List[str]	)							->       List[Any]:

				"""simple docstring"""

				if self.task in ["default", "seq2seq-lm"]:
					SCREAMING_SNAKE_CASE   :      Optional[int]				     =       super()._flatten_past_key_values_(a      ,      a      ,      a      ,      a	)
				else:
					SCREAMING_SNAKE_CASE   :      int				     =       super(a      ,      self	)._flatten_past_key_values_(
					    a      ,      a      ,      a      ,      a	)


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							:
			'''simple docstring'''







			lowerCamelCase__           =None
			lowerCamelCase__           =None
			lowerCamelCase__           =None  # sigma(t_i)
			@classmethod
			def 							__UpperCamelCase (			cls							: Tuple	)							->       int:

				"""simple docstring"""

				return cls()






@dataclass
class _UpperCamelCase							(							__A   ):
			'''simple docstring'''







			lowerCamelCase__           =42
			lowerCamelCase__           =42
			lowerCamelCase__           =42






class _UpperCamelCase							(							__A							,	__A   ):
			'''simple docstring'''







			@property
			def 							__UpperCamelCase (			self							: List[str]	)							->       Optional[int]:

				"""simple docstring"""

				return True
			@register_to_config
			def __init__(			self							: List[str]      ,      a							: float = 0.02      ,      a							: float = 100      ,      a							: float = 1.007      ,      a							: float = 80      ,      a							: float = 0.05      ,      a							: float = 50      ,      )							->       Optional[Any]:

				"""simple docstring"""

				pass
			def 							__UpperCamelCase (			self							: Dict	)							->       str:

				"""simple docstring"""

				return KarrasVeSchedulerState.create()
			def 							__UpperCamelCase (			self							: Tuple      ,      a							: KarrasVeSchedulerState      ,      a							: int      ,      a							: Tuple = ()	)							->       KarrasVeSchedulerState:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Any				     =       jnp.arange(0      ,      a	)[::-1].copy()
				SCREAMING_SNAKE_CASE   :      int				     =       [
				    (
				        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=a      ,      schedule=jnp.array(a      ,      dtype=jnp.floataa	)      ,      timesteps=a      ,      )
			def 							__UpperCamelCase (			self							: str      ,      a							: KarrasVeSchedulerState      ,      a							: jnp.ndarray      ,      a							: float      ,      a							: random.KeyArray      ,      )							->       Tuple[jnp.ndarray, float]:

				"""simple docstring"""

				if self.config.s_min <= sigma <= self.config.s_max:
					SCREAMING_SNAKE_CASE   :      Dict				     =       min(self.config.s_churn / state.num_inference_steps      ,      2**0.5 - 1	)
				else:
					SCREAMING_SNAKE_CASE   :      Any				     =       0

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

				return sample_hat, sigma_hat
			def 							__UpperCamelCase (			self							: Union[str, Any]      ,      a							: KarrasVeSchedulerState      ,      a							: jnp.ndarray      ,      a							: float      ,      a							: float      ,      a							: jnp.ndarray      ,      a							: bool = True      ,      )							->       Union[FlaxKarrasVeOutput, Tuple]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      int				     =       sample_hat + sigma_hat * model_output
				SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       (sample_hat - pred_original_sample) / sigma_hat
				SCREAMING_SNAKE_CASE   :      str				     =       sample_hat + (sigma_prev - sigma_hat) * derivative

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

				return FlaxKarrasVeOutput(prev_sample=a      ,      derivative=a      ,      state=a	)
			def 							__UpperCamelCase (			self							: Optional[int]      ,      a							: KarrasVeSchedulerState      ,      a							: jnp.ndarray      ,      a							: float      ,      a							: float      ,      a							: jnp.ndarray      ,      a							: jnp.ndarray      ,      a							: jnp.ndarray      ,      a							: bool = True      ,      )							->       Union[FlaxKarrasVeOutput, Tuple]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       sample_prev + sigma_prev * model_output
				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       (sample_prev - pred_original_sample) / sigma_prev
				SCREAMING_SNAKE_CASE   :      List[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=a      ,      derivative=a      ,      state=a	)






			def 							__UpperCamelCase (			self							: int      ,      a							: KarrasVeSchedulerState      ,      a							: Optional[Any]      ,      a							: int      ,      a							: Dict	)							->       List[str]:

				"""simple docstring"""

				raise NotImplementedError()