

import unittest

import torch
from torch import nn

from diffusers.models.activations import get_activation





class 			_SCREAMING_SNAKE_CASE (   unittest.TestCase		):







   '''simple docstring'''




   def      _snake_case		(       self					:			Optional[int]			):
       SCREAMING_SNAKE_CASE										=   get_activation("swish"			)

       self.assertIsInstance(__lowerCamelCase     , nn.SiLU			)

       self.assertEqual(act(torch.tensor(-100     , dtype=torch.floataa			)			).item()     , 0			)
       self.assertNotEqual(act(torch.tensor(-1     , dtype=torch.floataa			)			).item()     , 0			)
       self.assertEqual(act(torch.tensor(0     , dtype=torch.floataa			)			).item()     , 0			)
       self.assertEqual(act(torch.tensor(20     , dtype=torch.floataa			)			).item()     , 20			)





   def      _snake_case		(       self					:			List[str]			):
       SCREAMING_SNAKE_CASE										=   get_activation("silu"			)

       self.assertIsInstance(__lowerCamelCase     , nn.SiLU			)

       self.assertEqual(act(torch.tensor(-100     , dtype=torch.floataa			)			).item()     , 0			)
       self.assertNotEqual(act(torch.tensor(-1     , dtype=torch.floataa			)			).item()     , 0			)
       self.assertEqual(act(torch.tensor(0     , dtype=torch.floataa			)			).item()     , 0			)
       self.assertEqual(act(torch.tensor(20     , dtype=torch.floataa			)			).item()     , 20			)





   def      _snake_case		(       self					:			Optional[Any]			):
       SCREAMING_SNAKE_CASE										=   get_activation("mish"			)

       self.assertIsInstance(__lowerCamelCase     , nn.Mish			)

       self.assertEqual(act(torch.tensor(-200     , dtype=torch.floataa			)			).item()     , 0			)
       self.assertNotEqual(act(torch.tensor(-1     , dtype=torch.floataa			)			).item()     , 0			)
       self.assertEqual(act(torch.tensor(0     , dtype=torch.floataa			)			).item()     , 0			)
       self.assertEqual(act(torch.tensor(20     , dtype=torch.floataa			)			).item()     , 20			)





   def      _snake_case		(       self					:			Union[str, Any]			):
       SCREAMING_SNAKE_CASE										=   get_activation("gelu"			)

       self.assertIsInstance(__lowerCamelCase     , nn.GELU			)

       self.assertEqual(act(torch.tensor(-100     , dtype=torch.floataa			)			).item()     , 0			)
       self.assertNotEqual(act(torch.tensor(-1     , dtype=torch.floataa			)			).item()     , 0			)
       self.assertEqual(act(torch.tensor(0     , dtype=torch.floataa			)			).item()     , 0			)
       self.assertEqual(act(torch.tensor(20     , dtype=torch.floataa			)			).item()     , 20			)






'''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


_SCREAMING_SNAKE_CASE									=				logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE									=				{
    "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  lowerCAmelCase_		(		__magic_name__		):
 __lowerCamelCase    :				Union[str, Any]								=       "blenderbot-small"
 __lowerCamelCase    :				Optional[Any]								=       ["past_key_values"]
 __lowerCamelCase    :				str								=       {"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}




 def __init__(						self      ,  _lowerCAmelCase=50265      ,  _lowerCAmelCase=512      ,  _lowerCAmelCase=8      ,  _lowerCAmelCase=2048      ,  _lowerCAmelCase=16      ,  _lowerCAmelCase=8      ,  _lowerCAmelCase=2048      ,  _lowerCAmelCase=16      ,  _lowerCAmelCase=0.0      ,  _lowerCAmelCase=0.0      ,  _lowerCAmelCase=True      ,  _lowerCAmelCase=True      ,  _lowerCAmelCase="gelu"      ,  _lowerCAmelCase=512      ,  _lowerCAmelCase=0.1      ,  _lowerCAmelCase=0.0      ,  _lowerCAmelCase=0.0      ,  _lowerCAmelCase=0.02      ,  _lowerCAmelCase=1      ,  _lowerCAmelCase=False      ,  _lowerCAmelCase=0      ,  _lowerCAmelCase=1      ,  _lowerCAmelCase=2      ,  _lowerCAmelCase=2      ,  **_lowerCAmelCase      ,  )     ->				Dict:
     _lowerCAmelCase						      =   vocab_size
     _lowerCAmelCase						      =   max_position_embeddings
     _lowerCAmelCase						      =   d_model
     _lowerCAmelCase						      =   encoder_ffn_dim
     _lowerCAmelCase						      =   encoder_layers
     _lowerCAmelCase						      =   encoder_attention_heads
     _lowerCAmelCase						      =   decoder_ffn_dim
     _lowerCAmelCase						      =   decoder_layers
     _lowerCAmelCase						      =   decoder_attention_heads
     _lowerCAmelCase						      =   dropout
     _lowerCAmelCase						      =   attention_dropout
     _lowerCAmelCase						      =   activation_dropout
     _lowerCAmelCase						      =   activation_function
     _lowerCAmelCase						      =   init_std
     _lowerCAmelCase						      =   encoder_layerdrop
     _lowerCAmelCase						      =   decoder_layerdrop
     _lowerCAmelCase						      =   use_cache
     _lowerCAmelCase						      =   encoder_layers
     _lowerCAmelCase						      =   scale_embedding  # scale factor will be sqrt(d_model) if True

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







class  lowerCAmelCase_		(		__magic_name__		):




 @property
 def 				_snake_case (						self		)     ->				Mapping[str, Mapping[int, str]]:
     if self.task in ["default", "seq2seq-lm"]:
         _lowerCAmelCase						      =   OrderedDict(
             [
                 ("input_ids", {0: "batch", 1: "encoder_sequence"}),
                 ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
             ]		)

         if self.use_past:
             _lowerCAmelCase						      =   {0: "batch"}
             _lowerCAmelCase						      =   {0: "batch", 1: "past_decoder_sequence + sequence"}
         else:
             _lowerCAmelCase						      =   {0: "batch", 1: "decoder_sequence"}
             _lowerCAmelCase						      =   {0: "batch", 1: "decoder_sequence"}

         if self.use_past:
             self.fill_with_past_key_values_(_lowerCAmelCase      ,  direction="inputs"		)
     elif self.task == "causal-lm":
         # TODO: figure this case out.
         _lowerCAmelCase						      =   OrderedDict(
             [
                 ("input_ids", {0: "batch", 1: "encoder_sequence"}),
                 ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
             ]		)
         if self.use_past:
             _lowerCAmelCase      ,      _lowerCAmelCase						      =   self.num_layers
             for i in range(_lowerCAmelCase		):
                 _lowerCAmelCase						      =   {0: "batch", 2: "past_sequence + sequence"}
                 _lowerCAmelCase						      =   {0: "batch", 2: "past_sequence + sequence"}
     else:
         _lowerCAmelCase						      =   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 				_snake_case (						self		)     ->				Mapping[str, Mapping[int, str]]:
     if self.task in ["default", "seq2seq-lm"]:
         _lowerCAmelCase						      =   super().outputs
     else:
         _lowerCAmelCase						      =   super(_lowerCAmelCase      ,  self		).outputs
         if self.use_past:
             _lowerCAmelCase      ,      _lowerCAmelCase						      =   self.num_layers
             for i in range(_lowerCAmelCase		):
                 _lowerCAmelCase						      =   {0: "batch", 2: "past_sequence + sequence"}
                 _lowerCAmelCase						      =   {0: "batch", 2: "past_sequence + sequence"}
     return common_outputs




 def 				_snake_case (						self      ,  _lowerCAmelCase      ,  _lowerCAmelCase = -1      ,  _lowerCAmelCase = -1      ,  _lowerCAmelCase = False      ,  _lowerCAmelCase = None      ,  )     ->				Mapping[str, Any]:
     _lowerCAmelCase						      =   self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
         _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase		)

     # Generate decoder inputs
     _lowerCAmelCase						      =   seq_length if not self.use_past else 1
     _lowerCAmelCase						      =   self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
         _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase		)
     _lowerCAmelCase						      =   {f'''decoder_{name}''': tensor for name, tensor in decoder_inputs.items()}
     _lowerCAmelCase						      =   dict(**_lowerCAmelCase      ,  **_lowerCAmelCase		)

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

         _lowerCAmelCase						      =   torch.cat(
             [common_inputs["decoder_attention_mask"], torch.ones(_lowerCAmelCase      ,  _lowerCAmelCase		)]      ,  dim=1		)

         _lowerCAmelCase						      =   []
         # If the number of encoder and decoder layers are present in the model configuration, both are considered
         _lowerCAmelCase      ,      _lowerCAmelCase						      =   self.num_layers
         _lowerCAmelCase						      =   min(_lowerCAmelCase      ,  _lowerCAmelCase		)
         _lowerCAmelCase						      =   max(_lowerCAmelCase      ,  _lowerCAmelCase		) - min_num_layers
         _lowerCAmelCase						      =   "encoder" if num_encoder_layers > num_decoder_layers else "decoder"

         for _ in range(_lowerCAmelCase		):
             common_inputs["past_key_values"].append(
                 (
                     torch.zeros(_lowerCAmelCase		),
                     torch.zeros(_lowerCAmelCase		),
                     torch.zeros(_lowerCAmelCase		),
                     torch.zeros(_lowerCAmelCase		),
                 )		)
         # TODO: test this.
         _lowerCAmelCase						      =   encoder_shape if remaining_side_name == "encoder" else decoder_shape
         for _ in range(_lowerCAmelCase      ,  _lowerCAmelCase		):
             common_inputs["past_key_values"].append((torch.zeros(_lowerCAmelCase		), torch.zeros(_lowerCAmelCase		))		)
     return common_inputs




 def 				_snake_case (						self      ,  _lowerCAmelCase      ,  _lowerCAmelCase = -1      ,  _lowerCAmelCase = -1      ,  _lowerCAmelCase = False      ,  _lowerCAmelCase = None      ,  )     ->				Mapping[str, Any]:
     _lowerCAmelCase						      =   self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
         _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase		)

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

         _lowerCAmelCase						      =   common_inputs["attention_mask"].dtype
         _lowerCAmelCase						      =   torch.cat(
             [common_inputs["attention_mask"], torch.ones(_lowerCAmelCase      ,  _lowerCAmelCase      ,  dtype=_lowerCAmelCase		)]      ,  dim=1		)
         _lowerCAmelCase						      =   [
             (torch.zeros(_lowerCAmelCase		), torch.zeros(_lowerCAmelCase		)) for _ in range(_lowerCAmelCase		)
         ]
     return common_inputs




 def 				_snake_case (						self      ,  _lowerCAmelCase      ,  _lowerCAmelCase = -1      ,  _lowerCAmelCase = -1      ,  _lowerCAmelCase = False      ,  _lowerCAmelCase = 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
     _lowerCAmelCase						      =   compute_effective_axis_dimension(
         _lowerCAmelCase      ,  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
     _lowerCAmelCase						      =   tokenizer.num_special_tokens_to_add(_lowerCAmelCase		)
     _lowerCAmelCase						      =   compute_effective_axis_dimension(
         _lowerCAmelCase      ,  fixed_dimension=OnnxConfig.default_fixed_sequence      ,  num_token_to_add=_lowerCAmelCase		)

     # Generate dummy inputs according to compute batch and sequence
     _lowerCAmelCase						      =   [" ".join([tokenizer.unk_token]		) * seq_length] * batch_size
     _lowerCAmelCase						      =   dict(tokenizer(_lowerCAmelCase      ,  return_tensors=_lowerCAmelCase		)		)
     return common_inputs




 def 				_snake_case (						self      ,  _lowerCAmelCase      ,  _lowerCAmelCase = -1      ,  _lowerCAmelCase = -1      ,  _lowerCAmelCase = False      ,  _lowerCAmelCase = None      ,  )     ->				Mapping[str, Any]:
     if self.task in ["default", "seq2seq-lm"]:
         _lowerCAmelCase						      =   self._generate_dummy_inputs_for_default_and_seqaseq_lm(
             _lowerCAmelCase      ,  batch_size=_lowerCAmelCase      ,  seq_length=_lowerCAmelCase      ,  is_pair=_lowerCAmelCase      ,  framework=_lowerCAmelCase		)

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

     return common_inputs





 def 				_snake_case (						self      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase		)     ->				Dict:
     if self.task in ["default", "seq2seq-lm"]:
         _lowerCAmelCase						      =   super()._flatten_past_key_values_(_lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase		)
     else:
         _lowerCAmelCase						      =   super(_lowerCAmelCase      ,  self		)._flatten_past_key_values_(
             _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase		)


