




import torch

from diffusers import DDIMScheduler

from .test_schedulers import SchedulerCommonTest



class 			a     (   _A			):

     '''simple docstring'''





     lowerCAmelCase				:       Optional[Any]   		=  (DDIMScheduler,)
     lowerCAmelCase				:       Optional[Any]   		=  (('eta', 0.0), ('num_inference_steps', 50))
     def   lowerCamelCase_   (	self   :			Dict					,    **__snake_case   :			Optional[Any]				):
         UpperCAmelCase_							 =     {
             '''num_train_timesteps''': 10_00,
             '''beta_start''': 0.0_001,
             '''beta_end''': 0.02,
             '''beta_schedule''': '''linear''',
             '''clip_sample''': True,
         }

         config.update(**__snake_case				)
         return config
     def   lowerCamelCase_   (	self   :			str					,    **__snake_case   :			Optional[Any]				):
         UpperCAmelCase_							 =     self.scheduler_classes[0]
         UpperCAmelCase_							 =     self.get_scheduler_config(**__snake_case				)
         UpperCAmelCase_							 =     scheduler_class(**__snake_case				)

         UpperCAmelCase_ ,							UpperCAmelCase_							 =     10, 0.0

         UpperCAmelCase_							 =     self.dummy_model()
         UpperCAmelCase_							 =     self.dummy_sample_deter

         scheduler.set_timesteps(__snake_case				)

         for t in scheduler.timesteps:
             UpperCAmelCase_							 =     model(__snake_case					,    __snake_case				)
             UpperCAmelCase_							 =     scheduler.step(__snake_case					,    __snake_case					,    __snake_case					,    __snake_case				).prev_sample

         return sample
     def   lowerCamelCase_   (	self   :			int				):
         for timesteps in [1_00, 5_00, 10_00]:
             self.check_over_configs(num_train_timesteps=__snake_case				)
     def   lowerCamelCase_   (	self   :			Tuple				):
         for steps_offset in [0, 1]:
             self.check_over_configs(steps_offset=__snake_case				)

         UpperCAmelCase_							 =     self.scheduler_classes[0]
         UpperCAmelCase_							 =     self.get_scheduler_config(steps_offset=1				)
         UpperCAmelCase_							 =     scheduler_class(**__snake_case				)
         scheduler.set_timesteps(5				)
         assert torch.equal(scheduler.timesteps					,    torch.LongTensor([8_01, 6_01, 4_01, 2_01, 1]				)				)
     def   lowerCamelCase_   (	self   :			int				):
         for beta_start, beta_end in zip([0.0_001, 0.001, 0.01, 0.1]					,    [0.002, 0.02, 0.2, 2]				):
             self.check_over_configs(beta_start=__snake_case					,    beta_end=__snake_case				)
     def   lowerCamelCase_   (	self   :			Any				):
         for schedule in ["linear", "squaredcos_cap_v2"]:
             self.check_over_configs(beta_schedule=__snake_case				)
     def   lowerCamelCase_   (	self   :			Tuple				):
         for prediction_type in ["epsilon", "v_prediction"]:
             self.check_over_configs(prediction_type=__snake_case				)
     def   lowerCamelCase_   (	self   :			List[str]				):
         for clip_sample in [True, False]:
             self.check_over_configs(clip_sample=__snake_case				)
     def   lowerCamelCase_   (	self   :			Any				):
         for timestep_spacing in ["trailing", "leading"]:
             self.check_over_configs(timestep_spacing=__snake_case				)
     def   lowerCamelCase_   (	self   :			List[str]				):
         for rescale_betas_zero_snr in [True, False]:
             self.check_over_configs(rescale_betas_zero_snr=__snake_case				)
     def   lowerCamelCase_   (	self   :			str				):
         self.check_over_configs(thresholding=__snake_case				)
         for threshold in [0.5, 1.0, 2.0]:
             for prediction_type in ["epsilon", "v_prediction"]:
                 self.check_over_configs(
                     thresholding=__snake_case					,    prediction_type=__snake_case					,    sample_max_value=__snake_case					,    )
     def   lowerCamelCase_   (	self   :			Tuple				):
         for t in [1, 10, 49]:
             self.check_over_forward(time_step=__snake_case				)
     def   lowerCamelCase_   (	self   :			List[Any]				):
         for t, num_inference_steps in zip([1, 10, 50]					,    [10, 50, 5_00]				):
             self.check_over_forward(time_step=__snake_case					,    num_inference_steps=__snake_case				)
     def   lowerCamelCase_   (	self   :			Any				):
         for t, eta in zip([1, 10, 49]					,    [0.0, 0.5, 1.0]				):
             self.check_over_forward(time_step=__snake_case					,    eta=__snake_case				)
     def   lowerCamelCase_   (	self   :			List[Any]				):
         UpperCAmelCase_							 =     self.scheduler_classes[0]
         UpperCAmelCase_							 =     self.get_scheduler_config()
         UpperCAmelCase_							 =     scheduler_class(**__snake_case				)

         assert torch.sum(torch.abs(scheduler._get_variance(0					,    0				) - 0.0				)				) < 1E-5
         assert torch.sum(torch.abs(scheduler._get_variance(4_20					,    4_00				) - 0.14_771				)				) < 1E-5
         assert torch.sum(torch.abs(scheduler._get_variance(9_80					,    9_60				) - 0.32_460				)				) < 1E-5
         assert torch.sum(torch.abs(scheduler._get_variance(0					,    0				) - 0.0				)				) < 1E-5
         assert torch.sum(torch.abs(scheduler._get_variance(4_87					,    4_86				) - 0.00_979				)				) < 1E-5
         assert torch.sum(torch.abs(scheduler._get_variance(9_99					,    9_98				) - 0.02				)				) < 1E-5
     def   lowerCamelCase_   (	self   :			Union[str, Any]				):
         UpperCAmelCase_							 =     self.full_loop()

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

         assert abs(result_sum.item() - 172.0_067				) < 1E-2
         assert abs(result_mean.item() - 0.223_967				) < 1E-3
     def   lowerCamelCase_   (	self   :			Optional[int]				):
         UpperCAmelCase_							 =     self.full_loop(prediction_type='''v_prediction'''				)

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

         assert abs(result_sum.item() - 52.5_302				) < 1E-2
         assert abs(result_mean.item() - 0.0_684				) < 1E-3
     def   lowerCamelCase_   (	self   :			str				):
         # We specify different beta, so that the first alpha is 0.99
         UpperCAmelCase_							 =     self.full_loop(set_alpha_to_one=__snake_case					,    beta_start=0.01				)
         UpperCAmelCase_							 =     torch.sum(torch.abs(__snake_case				)				)
         UpperCAmelCase_							 =     torch.mean(torch.abs(__snake_case				)				)

         assert abs(result_sum.item() - 149.8_295				) < 1E-2
         assert abs(result_mean.item() - 0.1_951				) < 1E-3




     def   lowerCamelCase_   (	self   :			List[Any]				):
         # We specify different beta, so that the first alpha is 0.99
         UpperCAmelCase_							 =     self.full_loop(set_alpha_to_one=__snake_case					,    beta_start=0.01				)
         UpperCAmelCase_							 =     torch.sum(torch.abs(__snake_case				)				)
         UpperCAmelCase_							 =     torch.mean(torch.abs(__snake_case				)				)

         assert abs(result_sum.item() - 149.0_784				) < 1E-2
         assert abs(result_mean.item() - 0.1_941				) < 1E-3











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


_lowerCamelCase			 =						logging.get_logger(__name__)

_lowerCamelCase			 =						{
    '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 			a     (   _A			):

     '''simple docstring'''





     lowerCAmelCase				:       List[Any]   		=  'blenderbot'
     lowerCAmelCase				:       int   		=  ['past_key_values']
     lowerCAmelCase				:       int   		=  {'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}
     def __init__(	self   :			int					,    __snake_case   :			Optional[Any]=80_08					,    __snake_case   :			List[str]=1_28					,    __snake_case   :			List[str]=2					,    __snake_case   :			Tuple=1_02_40					,    __snake_case   :			List[Any]=32					,    __snake_case   :			int=24					,    __snake_case   :			str=1_02_40					,    __snake_case   :			Optional[int]=32					,    __snake_case   :			Dict=0.0					,    __snake_case   :			int=0.0					,    __snake_case   :			str=True					,    __snake_case   :			Dict=True					,    __snake_case   :			Dict="gelu"					,    __snake_case   :			List[str]=25_60					,    __snake_case   :			List[Any]=0.1					,    __snake_case   :			str=0.0					,    __snake_case   :			Dict=0.0					,    __snake_case   :			Tuple=0.02					,    __snake_case   :			Any=1					,    __snake_case   :			int=False					,    __snake_case   :			Any=0					,    __snake_case   :			Optional[Any]=1					,    __snake_case   :			Optional[int]=2					,    __snake_case   :			List[Any]=3					,    __snake_case   :			int=2					,    **__snake_case   :			Tuple					,    ):
         UpperCAmelCase_							 =     vocab_size
         UpperCAmelCase_							 =     max_position_embeddings
         UpperCAmelCase_							 =     d_model
         UpperCAmelCase_							 =     encoder_ffn_dim
         UpperCAmelCase_							 =     encoder_layers
         UpperCAmelCase_							 =     encoder_attention_heads
         UpperCAmelCase_							 =     decoder_ffn_dim
         UpperCAmelCase_							 =     decoder_layers
         UpperCAmelCase_							 =     decoder_attention_heads
         UpperCAmelCase_							 =     dropout
         UpperCAmelCase_							 =     attention_dropout
         UpperCAmelCase_							 =     activation_dropout
         UpperCAmelCase_							 =     activation_function
         UpperCAmelCase_							 =     init_std
         UpperCAmelCase_							 =     encoder_layerdrop
         UpperCAmelCase_							 =     decoder_layerdrop
         UpperCAmelCase_							 =     use_cache
         UpperCAmelCase_							 =     encoder_layers
         UpperCAmelCase_							 =     scale_embedding  # scale factor will be sqrt(d_model) if True

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



class 			a     (   _A			):

     '''simple docstring'''





     @property
     def   lowerCamelCase_   (	self   :			Any				):
         if self.task in ["default", "seq2seq-lm"]:
             UpperCAmelCase_							 =     OrderedDict(
                 [
                     ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
                     ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
                 ]				)
             if self.use_past:
                 UpperCAmelCase_							 =     {0: '''batch'''}
                 UpperCAmelCase_							 =     {0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
             else:
                 UpperCAmelCase_							 =     {0: '''batch''', 1: '''decoder_sequence'''}
                 UpperCAmelCase_							 =     {0: '''batch''', 1: '''decoder_sequence'''}
             if self.use_past:
                 self.fill_with_past_key_values_(__snake_case					,    direction='''inputs'''				)
         elif self.task == "causal-lm":
             UpperCAmelCase_							 =     OrderedDict(
                 [
                     ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
                     ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
                 ]				)
             if self.use_past:
                 UpperCAmelCase_ ,							UpperCAmelCase_							 =     self.num_layers
                 for i in range(__snake_case				):
                     UpperCAmelCase_							 =     {0: '''batch''', 2: '''past_sequence + sequence'''}
                     UpperCAmelCase_							 =     {0: '''batch''', 2: '''past_sequence + sequence'''}
         else:
             UpperCAmelCase_							 =     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   lowerCamelCase_   (	self   :			Optional[Any]				):
         if self.task in ["default", "seq2seq-lm"]:
             UpperCAmelCase_							 =     super().outputs
         else:
             UpperCAmelCase_							 =     super(__snake_case					,    self				).outputs
             if self.use_past:
                 UpperCAmelCase_ ,							UpperCAmelCase_							 =     self.num_layers
                 for i in range(__snake_case				):
                     UpperCAmelCase_							 =     {0: '''batch''', 2: '''past_sequence + sequence'''}
                     UpperCAmelCase_							 =     {0: '''batch''', 2: '''past_sequence + sequence'''}
         return common_outputs
     def   lowerCamelCase_   (	self   :			Optional[Any]					,    __snake_case   :			PreTrainedTokenizer					,    __snake_case   :			int = -1					,    __snake_case   :			int = -1					,    __snake_case   :			bool = False					,    __snake_case   :			Optional[TensorType] = None					,    ):
         UpperCAmelCase_							 =     self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
             __snake_case					,    __snake_case					,    __snake_case					,    __snake_case					,    __snake_case				)
         # Generate decoder inputs
         UpperCAmelCase_							 =     seq_length if not self.use_past else 1
         UpperCAmelCase_							 =     self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
             __snake_case					,    __snake_case					,    __snake_case					,    __snake_case					,    __snake_case				)
         UpperCAmelCase_							 =     {F'decoder_{name}': tensor for name, tensor in decoder_inputs.items()}
         UpperCAmelCase_							 =     dict(**__snake_case					,    **__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
             UpperCAmelCase_ ,							UpperCAmelCase_							 =     common_inputs['''input_ids'''].shape
             UpperCAmelCase_							 =     common_inputs['''decoder_input_ids'''].shape[1]
             UpperCAmelCase_ ,							UpperCAmelCase_							 =     self.num_attention_heads
             UpperCAmelCase_							 =     (
                 batch,
                 num_encoder_attention_heads,
                 encoder_seq_length,
                 self._config.hidden_size // num_encoder_attention_heads,
             )
             UpperCAmelCase_							 =     decoder_seq_length
             UpperCAmelCase_							 =     (
                 batch,
                 num_decoder_attention_heads,
                 decoder_past_length,
                 self._config.hidden_size // num_decoder_attention_heads,
             )
             UpperCAmelCase_							 =     torch.cat(
                 [common_inputs['''decoder_attention_mask'''], torch.ones(__snake_case					,    __snake_case				)]					,    dim=1				)
             UpperCAmelCase_							 =     []
             UpperCAmelCase_ ,							UpperCAmelCase_							 =     self.num_layers

             for _ in range(__snake_case				):
                 common_inputs["past_key_values"].append(
                     (
                         torch.zeros(__snake_case				),
                         torch.zeros(__snake_case				),
                         torch.zeros(__snake_case				),
                         torch.zeros(__snake_case				),
                     )				)
         return common_inputs
     def   lowerCamelCase_   (	self   :			Optional[Any]					,    __snake_case   :			PreTrainedTokenizer					,    __snake_case   :			int = -1					,    __snake_case   :			int = -1					,    __snake_case   :			bool = False					,    __snake_case   :			Optional[TensorType] = None					,    ):
         UpperCAmelCase_							 =     self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
             __snake_case					,    __snake_case					,    __snake_case					,    __snake_case					,    __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
             UpperCAmelCase_ ,							UpperCAmelCase_							 =     common_inputs['''input_ids'''].shape
             UpperCAmelCase_							 =     seqlen
             UpperCAmelCase_ ,							UpperCAmelCase_							 =     self.num_layers
             UpperCAmelCase_ ,							UpperCAmelCase_							 =     self.num_attention_heads
             UpperCAmelCase_							 =     (
                 batch,
                 num_encoder_attention_heads,
                 past_key_values_length,
                 self._config.hidden_size // num_encoder_attention_heads,
             )
             UpperCAmelCase_							 =     common_inputs['''attention_mask'''].dtype
             UpperCAmelCase_							 =     torch.cat(
                 [common_inputs['''attention_mask'''], torch.ones(__snake_case					,    __snake_case					,    dtype=__snake_case				)]					,    dim=1				)
             UpperCAmelCase_							 =     [
                 (torch.zeros(__snake_case				), torch.zeros(__snake_case				)) for _ in range(__snake_case				)
             ]
         return common_inputs
     def   lowerCamelCase_   (	self   :			Any					,    __snake_case   :			PreTrainedTokenizer					,    __snake_case   :			int = -1					,    __snake_case   :			int = -1					,    __snake_case   :			bool = False					,    __snake_case   :			Optional[TensorType] = None					,    ):
         # 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
         UpperCAmelCase_							 =     compute_effective_axis_dimension(
             __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
         UpperCAmelCase_							 =     tokenizer.num_special_tokens_to_add(__snake_case				)
         UpperCAmelCase_							 =     compute_effective_axis_dimension(
             __snake_case					,    fixed_dimension=OnnxConfig.default_fixed_sequence					,    num_token_to_add=__snake_case				)

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

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

         return common_inputs
     def   lowerCamelCase_   (	self   :			Dict					,    __snake_case   :			Optional[int]					,    __snake_case   :			int					,    __snake_case   :			str					,    __snake_case   :			Optional[int]				):
         if self.task in ["default", "seq2seq-lm"]:
             UpperCAmelCase_							 =     super()._flatten_past_key_values_(__snake_case					,    __snake_case					,    __snake_case					,    __snake_case				)
         else:
             UpperCAmelCase_							 =     super(__snake_case					,    self				)._flatten_past_key_values_(
                 __snake_case					,    __snake_case					,    __snake_case					,    __snake_case				)




     def   lowerCamelCase_   (	self   :			Dict					,    __snake_case   :			Mapping[str, Mapping[int, str]]					,    __snake_case   :			str				):
         if direction not in ["inputs", "outputs"]:
             raise ValueError(F'direction must either be "inputs" or "outputs", but {direction} was given'				)

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

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

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





