


'''simple docstring'''




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      :      List[str]       				=      logging.get_logger(__name__)

_a      :      List[str]       				=      {
    "facebook/blenderbot-3B": "https://huggingface.co/facebook/blenderbot-3B/resolve/main/config.json",
    # See all Blenderbot models at https://huggingface.co/models?filter=blenderbot
}




class  _lowercase   (  __lowercase       ):
   _SCREAMING_SNAKE_CASE     :     Dict									=  "blenderbot"
   _SCREAMING_SNAKE_CASE     :     int									=  ["past_key_values"]
   _SCREAMING_SNAKE_CASE     :     Tuple									=  {"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}
   def __init__(	self					:			Optional[Any]					,      SCREAMING_SNAKE_CASE_					:			int=8008					,      SCREAMING_SNAKE_CASE_					:			Optional[Any]=128					,      SCREAMING_SNAKE_CASE_					:			int=2					,      SCREAMING_SNAKE_CASE_					:			List[str]=1_0240					,      SCREAMING_SNAKE_CASE_					:			List[Any]=32					,      SCREAMING_SNAKE_CASE_					:			int=24					,      SCREAMING_SNAKE_CASE_					:			List[Any]=1_0240					,      SCREAMING_SNAKE_CASE_					:			Optional[int]=32					,      SCREAMING_SNAKE_CASE_					:			List[str]=0.0					,      SCREAMING_SNAKE_CASE_					:			Tuple=0.0					,      SCREAMING_SNAKE_CASE_					:			Any=True					,      SCREAMING_SNAKE_CASE_					:			Optional[Any]=True					,      SCREAMING_SNAKE_CASE_					:			Union[str, Any]="gelu"					,      SCREAMING_SNAKE_CASE_					:			List[str]=2560					,      SCREAMING_SNAKE_CASE_					:			Optional[int]=0.1					,      SCREAMING_SNAKE_CASE_					:			Dict=0.0					,      SCREAMING_SNAKE_CASE_					:			Any=0.0					,      SCREAMING_SNAKE_CASE_					:			Union[str, Any]=0.0_2					,      SCREAMING_SNAKE_CASE_					:			Dict=1					,      SCREAMING_SNAKE_CASE_					:			Optional[Any]=False					,      SCREAMING_SNAKE_CASE_					:			int=0					,      SCREAMING_SNAKE_CASE_					:			Tuple=1					,      SCREAMING_SNAKE_CASE_					:			str=2					,      SCREAMING_SNAKE_CASE_					:			List[Any]=3					,      SCREAMING_SNAKE_CASE_					:			Optional[int]=2					,      **SCREAMING_SNAKE_CASE_					:			int					,      )   ->       Optional[int]:
        __snake_case    			=	vocab_size
        __snake_case    			=	max_position_embeddings
        __snake_case    			=	d_model
        __snake_case    			=	encoder_ffn_dim
        __snake_case    			=	encoder_layers
        __snake_case    			=	encoder_attention_heads
        __snake_case    			=	decoder_ffn_dim
        __snake_case    			=	decoder_layers
        __snake_case    			=	decoder_attention_heads
        __snake_case    			=	dropout
        __snake_case    			=	attention_dropout
        __snake_case    			=	activation_dropout
        __snake_case    			=	activation_function
        __snake_case    			=	init_std
        __snake_case    			=	encoder_layerdrop
        __snake_case    			=	decoder_layerdrop
        __snake_case    			=	use_cache
        __snake_case    			=	encoder_layers
        __snake_case    			=	scale_embedding  # scale factor will be sqrt(d_model) if True

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




class  _lowercase   (  __lowercase       ):
   @property
   def a     (	self					:			Optional[int]			)   ->       Mapping[str, Mapping[int, str]]:
        if self.task in ["default", "seq2seq-lm"]:
             __snake_case    			=	OrderedDict(
                 [
                     ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
                     ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
                 ]			)
             if self.use_past:
                  __snake_case    			=	{0: 'batch'}
                  __snake_case    			=	{0: 'batch', 1: 'past_decoder_sequence + sequence'}
             else:
                  __snake_case    			=	{0: 'batch', 1: 'decoder_sequence'}
                  __snake_case    			=	{0: 'batch', 1: 'decoder_sequence'}
             if self.use_past:
                  self.fill_with_past_key_values_(SCREAMING_SNAKE_CASE_					,      direction='inputs'			)
        elif self.task == "causal-lm":
             __snake_case    			=	OrderedDict(
                 [
                     ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
                     ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
                 ]			)
             if self.use_past:
                  __snake_case			,					__snake_case    			=	self.num_layers
                  for i in range(SCREAMING_SNAKE_CASE_			):
                       __snake_case    			=	{0: 'batch', 2: 'past_sequence + sequence'}
                       __snake_case    			=	{0: 'batch', 2: 'past_sequence + sequence'}
        else:
             __snake_case    			=	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
   # Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.outputs
   def a     (	self					:			Optional[int]			)   ->       Mapping[str, Mapping[int, str]]:
        if self.task in ["default", "seq2seq-lm"]:
             __snake_case    			=	super().outputs
        else:
             __snake_case    			=	super(SCREAMING_SNAKE_CASE_					,      self			).outputs
             if self.use_past:
                  __snake_case			,					__snake_case    			=	self.num_layers
                  for i in range(SCREAMING_SNAKE_CASE_			):
                       __snake_case    			=	{0: 'batch', 2: 'past_sequence + sequence'}
                       __snake_case    			=	{0: 'batch', 2: 'past_sequence + sequence'}
        return common_outputs
   def a     (	self					:			List[str]					,      SCREAMING_SNAKE_CASE_					:			PreTrainedTokenizer					,      SCREAMING_SNAKE_CASE_					:			int = -1					,      SCREAMING_SNAKE_CASE_					:			int = -1					,      SCREAMING_SNAKE_CASE_					:			bool = False					,      SCREAMING_SNAKE_CASE_					:			Optional[TensorType] = None					,      )   ->       Mapping[str, Any]:
        __snake_case    			=	self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
            SCREAMING_SNAKE_CASE_					,      SCREAMING_SNAKE_CASE_					,      SCREAMING_SNAKE_CASE_					,      SCREAMING_SNAKE_CASE_					,      SCREAMING_SNAKE_CASE_			)
        # Generate decoder inputs
        __snake_case    			=	seq_length if not self.use_past else 1
        __snake_case    			=	self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
            SCREAMING_SNAKE_CASE_					,      SCREAMING_SNAKE_CASE_					,      SCREAMING_SNAKE_CASE_					,      SCREAMING_SNAKE_CASE_					,      SCREAMING_SNAKE_CASE_			)
        __snake_case    			=	{f'decoder_{name}': tensor for name, tensor in decoder_inputs.items()}
        __snake_case    			=	dict(**SCREAMING_SNAKE_CASE_					,      **SCREAMING_SNAKE_CASE_			)

        if self.use_past:
             if not is_torch_available():
                  raise ValueError('Cannot generate dummy past_keys inputs without PyTorch installed.'			)
             else:
                  import torch
             __snake_case			,					__snake_case    			=	common_inputs['input_ids'].shape
             __snake_case    			=	common_inputs['decoder_input_ids'].shape[1]
             __snake_case			,					__snake_case    			=	self.num_attention_heads
             __snake_case    			=	(
                 batch,
                 num_encoder_attention_heads,
                 encoder_seq_length,
                 self._config.hidden_size // num_encoder_attention_heads,
             )
             __snake_case    			=	decoder_seq_length
             __snake_case    			=	(
                 batch,
                 num_decoder_attention_heads,
                 decoder_past_length,
                 self._config.hidden_size // num_decoder_attention_heads,
             )
             __snake_case    			=	torch.cat(
                 [common_inputs['decoder_attention_mask'], torch.ones(SCREAMING_SNAKE_CASE_					,      SCREAMING_SNAKE_CASE_			)]					,      dim=1			)
             __snake_case    			=	[]
             __snake_case			,					__snake_case    			=	self.num_layers

             for _ in range(SCREAMING_SNAKE_CASE_			):
                  common_inputs["past_key_values"].append(
                      (
                          torch.zeros(SCREAMING_SNAKE_CASE_			),
                          torch.zeros(SCREAMING_SNAKE_CASE_			),
                          torch.zeros(SCREAMING_SNAKE_CASE_			),
                          torch.zeros(SCREAMING_SNAKE_CASE_			),
                      )			)
        return common_inputs
   def a     (	self					:			List[Any]					,      SCREAMING_SNAKE_CASE_					:			PreTrainedTokenizer					,      SCREAMING_SNAKE_CASE_					:			int = -1					,      SCREAMING_SNAKE_CASE_					:			int = -1					,      SCREAMING_SNAKE_CASE_					:			bool = False					,      SCREAMING_SNAKE_CASE_					:			Optional[TensorType] = None					,      )   ->       Mapping[str, Any]:
        __snake_case    			=	self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
            SCREAMING_SNAKE_CASE_					,      SCREAMING_SNAKE_CASE_					,      SCREAMING_SNAKE_CASE_					,      SCREAMING_SNAKE_CASE_					,      SCREAMING_SNAKE_CASE_			)

        if self.use_past:
             if not is_torch_available():
                  raise ValueError('Cannot generate dummy past_keys inputs without PyTorch installed.'			)
             else:
                  import torch
             __snake_case			,					__snake_case    			=	common_inputs['input_ids'].shape
             __snake_case    			=	seqlen
             __snake_case			,					__snake_case    			=	self.num_layers
             __snake_case			,					__snake_case    			=	self.num_attention_heads
             __snake_case    			=	(
                 batch,
                 num_encoder_attention_heads,
                 past_key_values_length,
                 self._config.hidden_size // num_encoder_attention_heads,
             )
             __snake_case    			=	common_inputs['attention_mask'].dtype
             __snake_case    			=	torch.cat(
                 [common_inputs['attention_mask'], torch.ones(SCREAMING_SNAKE_CASE_					,      SCREAMING_SNAKE_CASE_					,      dtype=SCREAMING_SNAKE_CASE_			)]					,      dim=1			)
             __snake_case    			=	[
                 (torch.zeros(SCREAMING_SNAKE_CASE_			), torch.zeros(SCREAMING_SNAKE_CASE_			)) for _ in range(SCREAMING_SNAKE_CASE_			)
             ]
        return common_inputs
   def a     (	self					:			Dict					,      SCREAMING_SNAKE_CASE_					:			PreTrainedTokenizer					,      SCREAMING_SNAKE_CASE_					:			int = -1					,      SCREAMING_SNAKE_CASE_					:			int = -1					,      SCREAMING_SNAKE_CASE_					:			bool = False					,      SCREAMING_SNAKE_CASE_					:			Optional[TensorType] = None					,      )   ->       Mapping[str, Any]:
        # Copied from OnnxConfig.generate_dummy_inputs
        # Did not use super(OnnxConfigWithPast, self).generate_dummy_inputs for code clarity.
        # If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
        __snake_case    			=	compute_effective_axis_dimension(
            SCREAMING_SNAKE_CASE_					,      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
        __snake_case    			=	tokenizer.num_special_tokens_to_add(SCREAMING_SNAKE_CASE_			)
        __snake_case    			=	compute_effective_axis_dimension(
            SCREAMING_SNAKE_CASE_					,      fixed_dimension=OnnxConfig.default_fixed_sequence					,      num_token_to_add=SCREAMING_SNAKE_CASE_			)

        # Generate dummy inputs according to compute batch and sequence
        __snake_case    			=	[' '.join([tokenizer.unk_token]			) * seq_length] * batch_size
        __snake_case    			=	dict(tokenizer(SCREAMING_SNAKE_CASE_					,      return_tensors=SCREAMING_SNAKE_CASE_			)			)
        return common_inputs
   def a     (	self					:			List[Any]					,      SCREAMING_SNAKE_CASE_					:			PreTrainedTokenizer					,      SCREAMING_SNAKE_CASE_					:			int = -1					,      SCREAMING_SNAKE_CASE_					:			int = -1					,      SCREAMING_SNAKE_CASE_					:			bool = False					,      SCREAMING_SNAKE_CASE_					:			Optional[TensorType] = None					,      )   ->       Mapping[str, Any]:
        if self.task in ["default", "seq2seq-lm"]:
             __snake_case    			=	self._generate_dummy_inputs_for_default_and_seqaseq_lm(
                 SCREAMING_SNAKE_CASE_					,      batch_size=SCREAMING_SNAKE_CASE_					,      seq_length=SCREAMING_SNAKE_CASE_					,      is_pair=SCREAMING_SNAKE_CASE_					,      framework=SCREAMING_SNAKE_CASE_			)

        elif self.task == "causal-lm":
             __snake_case    			=	self._generate_dummy_inputs_for_causal_lm(
                 SCREAMING_SNAKE_CASE_					,      batch_size=SCREAMING_SNAKE_CASE_					,      seq_length=SCREAMING_SNAKE_CASE_					,      is_pair=SCREAMING_SNAKE_CASE_					,      framework=SCREAMING_SNAKE_CASE_			)
        else:
             __snake_case    			=	self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
                 SCREAMING_SNAKE_CASE_					,      batch_size=SCREAMING_SNAKE_CASE_					,      seq_length=SCREAMING_SNAKE_CASE_					,      is_pair=SCREAMING_SNAKE_CASE_					,      framework=SCREAMING_SNAKE_CASE_			)

        return common_inputs
   def a     (	self					:			Union[str, Any]					,      SCREAMING_SNAKE_CASE_					:			Union[str, Any]					,      SCREAMING_SNAKE_CASE_					:			Any					,      SCREAMING_SNAKE_CASE_					:			List[str]					,      SCREAMING_SNAKE_CASE_					:			str			)   ->       Tuple:
        if self.task in ["default", "seq2seq-lm"]:
             __snake_case    			=	super()._flatten_past_key_values_(SCREAMING_SNAKE_CASE_					,      SCREAMING_SNAKE_CASE_					,      SCREAMING_SNAKE_CASE_					,      SCREAMING_SNAKE_CASE_			)
        else:
             __snake_case    			=	super(SCREAMING_SNAKE_CASE_					,      self			)._flatten_past_key_values_(
                 SCREAMING_SNAKE_CASE_					,      SCREAMING_SNAKE_CASE_					,      SCREAMING_SNAKE_CASE_					,      SCREAMING_SNAKE_CASE_			)





   def a     (	self					:			Optional[int]					,      SCREAMING_SNAKE_CASE_					:			Mapping[str, Mapping[int, str]]					,      SCREAMING_SNAKE_CASE_					:			str			)   ->       Optional[int]:
        if direction not in ["inputs", "outputs"]:
             raise ValueError(f'direction must either be "inputs" or "outputs", but {direction} was given'			)

        __snake_case    			=	'past_key_values' if direction == 'inputs' else 'present'
        __snake_case			,					__snake_case    			=	self.num_layers

        __snake_case    			=	'past_encoder_sequence'
        __snake_case    			=	'past_decoder_sequence' if direction == 'inputs' else 'past_decoder_sequence + sequence'

        for i in range(SCREAMING_SNAKE_CASE_			):
             __snake_case    			=	{0: 'batch', 2: decoder_sequence}
             __snake_case    			=	{0: 'batch', 2: decoder_sequence}
             __snake_case    			=	{0: 'batch', 2: encoder_sequence}
             __snake_case    			=	{0: 'batch', 2: encoder_sequence}








'''simple docstring'''




import torch

from diffusers import EulerAncestralDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest




class  _lowercase   (  __lowercase       ):
   _SCREAMING_SNAKE_CASE     :     List[str]									=  (EulerAncestralDiscreteScheduler,)
   _SCREAMING_SNAKE_CASE     :     Dict									=  1_0
   def a     (	self					:			List[str]					,      **SCREAMING_SNAKE_CASE_					:			Union[str, Any]			)   ->       List[str]:
        __snake_case    			=	{
            'num_train_timesteps': 1100,
            'beta_start': 0.0_0_0_1,
            'beta_end': 0.0_2,
            'beta_schedule': 'linear',
        }

        config.update(**SCREAMING_SNAKE_CASE_			)
        return config
   def a     (	self					:			Optional[int]			)   ->       int:
        for timesteps in [10, 50, 100, 1000]:
             self.check_over_configs(num_train_timesteps=SCREAMING_SNAKE_CASE_			)
   def a     (	self					:			Any			)   ->       List[Any]:
        for beta_start, beta_end in zip([0.0_0_0_0_1, 0.0_0_0_1, 0.0_0_1]					,      [0.0_0_0_2, 0.0_0_2, 0.0_2]			):
             self.check_over_configs(beta_start=SCREAMING_SNAKE_CASE_					,      beta_end=SCREAMING_SNAKE_CASE_			)
   def a     (	self					:			List[str]			)   ->       int:
        for schedule in ["linear", "scaled_linear"]:
             self.check_over_configs(beta_schedule=SCREAMING_SNAKE_CASE_			)
   def a     (	self					:			Dict			)   ->       Optional[Any]:
        for prediction_type in ["epsilon", "v_prediction"]:
             self.check_over_configs(prediction_type=SCREAMING_SNAKE_CASE_			)
   def a     (	self					:			List[Any]			)   ->       str:
        __snake_case    			=	self.scheduler_classes[0]
        __snake_case    			=	self.get_scheduler_config()
        __snake_case    			=	scheduler_class(**SCREAMING_SNAKE_CASE_			)

        scheduler.set_timesteps(self.num_inference_steps			)

        __snake_case    			=	torch.manual_seed(0			)

        __snake_case    			=	self.dummy_model()
        __snake_case    			=	self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
        __snake_case    			=	sample.to(SCREAMING_SNAKE_CASE_			)

        for i, t in enumerate(scheduler.timesteps			):
             __snake_case    			=	scheduler.scale_model_input(SCREAMING_SNAKE_CASE_					,      SCREAMING_SNAKE_CASE_			)

             __snake_case    			=	model(SCREAMING_SNAKE_CASE_					,      SCREAMING_SNAKE_CASE_			)

             __snake_case    			=	scheduler.step(SCREAMING_SNAKE_CASE_					,      SCREAMING_SNAKE_CASE_					,      SCREAMING_SNAKE_CASE_					,      generator=SCREAMING_SNAKE_CASE_			)
             __snake_case    			=	output.prev_sample

        __snake_case    			=	torch.sum(torch.abs(SCREAMING_SNAKE_CASE_			)			)
        __snake_case    			=	torch.mean(torch.abs(SCREAMING_SNAKE_CASE_			)			)

        assert abs(result_sum.item() - 1_5_2.3_1_9_2			) < 1e-2
        assert abs(result_mean.item() - 0.1_9_8_3			) < 1e-3
   def a     (	self					:			List[Any]			)   ->       Optional[Any]:
        __snake_case    			=	self.scheduler_classes[0]
        __snake_case    			=	self.get_scheduler_config(prediction_type='v_prediction'			)
        __snake_case    			=	scheduler_class(**SCREAMING_SNAKE_CASE_			)

        scheduler.set_timesteps(self.num_inference_steps			)

        __snake_case    			=	torch.manual_seed(0			)

        __snake_case    			=	self.dummy_model()
        __snake_case    			=	self.dummy_sample_deter * scheduler.init_noise_sigma
        __snake_case    			=	sample.to(SCREAMING_SNAKE_CASE_			)

        for i, t in enumerate(scheduler.timesteps			):
             __snake_case    			=	scheduler.scale_model_input(SCREAMING_SNAKE_CASE_					,      SCREAMING_SNAKE_CASE_			)

             __snake_case    			=	model(SCREAMING_SNAKE_CASE_					,      SCREAMING_SNAKE_CASE_			)

             __snake_case    			=	scheduler.step(SCREAMING_SNAKE_CASE_					,      SCREAMING_SNAKE_CASE_					,      SCREAMING_SNAKE_CASE_					,      generator=SCREAMING_SNAKE_CASE_			)
             __snake_case    			=	output.prev_sample

        __snake_case    			=	torch.sum(torch.abs(SCREAMING_SNAKE_CASE_			)			)
        __snake_case    			=	torch.mean(torch.abs(SCREAMING_SNAKE_CASE_			)			)

        assert abs(result_sum.item() - 1_0_8.4_4_3_9			) < 1e-2
        assert abs(result_mean.item() - 0.1_4_1_2			) < 1e-3





   def a     (	self					:			Optional[int]			)   ->       Dict:
        __snake_case    			=	self.scheduler_classes[0]
        __snake_case    			=	self.get_scheduler_config()
        __snake_case    			=	scheduler_class(**SCREAMING_SNAKE_CASE_			)

        scheduler.set_timesteps(self.num_inference_steps					,      device=SCREAMING_SNAKE_CASE_			)
        __snake_case    			=	torch.manual_seed(0			)

        __snake_case    			=	self.dummy_model()
        __snake_case    			=	self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
        __snake_case    			=	sample.to(SCREAMING_SNAKE_CASE_			)

        for t in scheduler.timesteps:
             __snake_case    			=	scheduler.scale_model_input(SCREAMING_SNAKE_CASE_					,      SCREAMING_SNAKE_CASE_			)

             __snake_case    			=	model(SCREAMING_SNAKE_CASE_					,      SCREAMING_SNAKE_CASE_			)

             __snake_case    			=	scheduler.step(SCREAMING_SNAKE_CASE_					,      SCREAMING_SNAKE_CASE_					,      SCREAMING_SNAKE_CASE_					,      generator=SCREAMING_SNAKE_CASE_			)
             __snake_case    			=	output.prev_sample

        __snake_case    			=	torch.sum(torch.abs(SCREAMING_SNAKE_CASE_			)			)
        __snake_case    			=	torch.mean(torch.abs(SCREAMING_SNAKE_CASE_			)			)

        assert abs(result_sum.item() - 1_5_2.3_1_9_2			) < 1e-2
        assert abs(result_mean.item() - 0.1_9_8_3			) < 1e-3




