





'''simple docstring'''


from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union

import numpy as np
import PIL
from PIL import Image

from ...utils import BaseOutput, is_torch_available, is_transformers_available

@dataclass
class  lowerCAmelCase_		(		__magic_name__		):
 __lowerCamelCase    :				Union[List[PIL.Image.Image], np.ndarray]
 __lowerCamelCase    :				Optional[List[bool]]


if is_transformers_available() and is_torch_available():
       from .pipeline_semantic_stable_diffusion import SemanticStableDiffusionPipeline









'''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		)


