






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

from ...configuration_utils import PretrainedConfig
from ...feature_extraction_utils import FeatureExtractionMixin
from ...onnx import OnnxConfig
from ...onnx.utils import compute_effective_axis_dimension
from ...tokenization_utils_base import PreTrainedTokenizerBase
from ...utils import TensorType, logging


__snake_case												=  logging.get_logger(__name__)

__snake_case												=  {
    """deepmind/language-perceiver""": """https://huggingface.co/deepmind/language-perceiver/resolve/main/config.json""",
    # See all Perceiver models at https://huggingface.co/models?filter=perceiver
}







class 			UpperCAmelCase_						(					lowercase ):



      """simple docstring"""
      UpperCamelCase_				:					Dict           ='perceiver'
      def __init__(				self    ,				SCREAMING_SNAKE_CASE_=256    ,				SCREAMING_SNAKE_CASE_=1280    ,				SCREAMING_SNAKE_CASE_=768    ,				SCREAMING_SNAKE_CASE_=1    ,				SCREAMING_SNAKE_CASE_=26    ,				SCREAMING_SNAKE_CASE_=8    ,				SCREAMING_SNAKE_CASE_=8    ,				SCREAMING_SNAKE_CASE_=None    ,				SCREAMING_SNAKE_CASE_=None    ,				SCREAMING_SNAKE_CASE_="kv"    ,				SCREAMING_SNAKE_CASE_=1    ,				SCREAMING_SNAKE_CASE_=1    ,				SCREAMING_SNAKE_CASE_="gelu"    ,				SCREAMING_SNAKE_CASE_=0.1    ,				SCREAMING_SNAKE_CASE_=0.02    ,				SCREAMING_SNAKE_CASE_=1e-12    ,				SCREAMING_SNAKE_CASE_=True    ,				SCREAMING_SNAKE_CASE_=262    ,				SCREAMING_SNAKE_CASE_=2048    ,				SCREAMING_SNAKE_CASE_=56    ,				SCREAMING_SNAKE_CASE_=[368, 496]    ,				SCREAMING_SNAKE_CASE_=16    ,				SCREAMING_SNAKE_CASE_=1920    ,				SCREAMING_SNAKE_CASE_=16    ,				SCREAMING_SNAKE_CASE_=[1, 16, 224, 224]    ,				**SCREAMING_SNAKE_CASE_    ,				)				->      int:
          super().__init__(**__lowerCAmelCase )

          UpperCamelCase       :Dict								=   num_latents
          UpperCamelCase       :Any								=   d_latents
          UpperCamelCase       :str								=   d_model
          UpperCamelCase       :Dict								=   num_blocks
          UpperCamelCase       :List[str]								=   num_self_attends_per_block
          UpperCamelCase       :Union[str, Any]								=   num_self_attention_heads
          UpperCamelCase       :List[str]								=   num_cross_attention_heads
          UpperCamelCase       :Optional[int]								=   qk_channels
          UpperCamelCase       :int								=   v_channels
          UpperCamelCase       :Tuple								=   cross_attention_shape_for_attention
          UpperCamelCase       :Union[str, Any]								=   self_attention_widening_factor
          UpperCamelCase       :Dict								=   cross_attention_widening_factor
          UpperCamelCase       :Tuple								=   hidden_act
          UpperCamelCase       :Optional[Any]								=   attention_probs_dropout_prob
          UpperCamelCase       :Tuple								=   initializer_range
          UpperCamelCase       :Optional[int]								=   layer_norm_eps
          UpperCamelCase       :Union[str, Any]								=   use_query_residual
          # masked language modeling attributes
          UpperCamelCase       :Optional[int]								=   vocab_size
          UpperCamelCase       :Union[str, Any]								=   max_position_embeddings
          # image classification attributes
          UpperCamelCase       :Dict								=   image_size
          # flow attributes
          UpperCamelCase       :Any								=   train_size
          # multimodal autoencoding attributes
          UpperCamelCase       :Tuple								=   num_frames
          UpperCamelCase       :Tuple								=   audio_samples_per_frame
          UpperCamelCase       :Dict								=   samples_per_patch
          UpperCamelCase       :Optional[Any]								=   output_shape







class 			UpperCAmelCase_						(					lowercase ):



      """simple docstring"""
      @property
      def     UpperCAmelCase						(				self )				->      Union[str, Any]:



          if self.task == "multiple-choice":
              UpperCamelCase       :Optional[int]								=   {0: '''batch''', 1: '''choice''', 2: '''sequence'''}
          else:
              UpperCamelCase       :Optional[Any]								=   {0: '''batch''', 1: '''sequence'''}
          return OrderedDict(
              [
                  ('''inputs''', dynamic_axis),
                  ('''attention_mask''', dynamic_axis),
              ] )
      @property
      def     UpperCAmelCase						(				self )				->      int:
          return 1e-4




      def     UpperCAmelCase						(				self    ,				SCREAMING_SNAKE_CASE_    ,				SCREAMING_SNAKE_CASE_ = -1    ,				SCREAMING_SNAKE_CASE_ = -1    ,				SCREAMING_SNAKE_CASE_ = -1    ,				SCREAMING_SNAKE_CASE_ = False    ,				SCREAMING_SNAKE_CASE_ = None    ,				SCREAMING_SNAKE_CASE_ = 3    ,				SCREAMING_SNAKE_CASE_ = 40    ,				SCREAMING_SNAKE_CASE_ = 40    ,				)				->      Union[str, Any]:



          if isinstance(__lowerCAmelCase    ,				__lowerCAmelCase ):
              # If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
              UpperCamelCase       :Any								=   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
              UpperCamelCase       :List[Any]								=   preprocessor.num_special_tokens_to_add(__lowerCAmelCase )
              UpperCamelCase       :Optional[int]								=   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
              UpperCamelCase       :Union[str, Any]								=   [''' '''.join(['''a'''] ) * seq_length] * batch_size
              UpperCamelCase       :List[Any]								=   dict(preprocessor(__lowerCAmelCase    ,				return_tensors=__lowerCAmelCase ) )
              UpperCamelCase       :Dict								=   inputs.pop('''input_ids''' )
              return inputs
          elif isinstance(__lowerCAmelCase    ,				__lowerCAmelCase ) and preprocessor.model_input_names[0] == "pixel_values":
              # If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
              UpperCamelCase       :Optional[Any]								=   compute_effective_axis_dimension(__lowerCAmelCase    ,				fixed_dimension=OnnxConfig.default_fixed_batch )
              UpperCamelCase       :str								=   self._generate_dummy_images(__lowerCAmelCase    ,				__lowerCAmelCase    ,				__lowerCAmelCase    ,				__lowerCAmelCase )
              UpperCamelCase       :Optional[int]								=   dict(preprocessor(images=__lowerCAmelCase    ,				return_tensors=__lowerCAmelCase ) )
              UpperCamelCase       :List[str]								=   inputs.pop('''pixel_values''' )
              return inputs
          else:
              raise ValueError(
                  '''Unable to generate dummy inputs for the model. Please provide a tokenizer or a preprocessor.''' )






"""simple docstring"""






import argparse
from pathlib import Path

import torch
from packaging import version
from torch.onnx import export

from diffusers import AutoencoderKL


__lowerCAmelCase    :       Dict					     =version.parse(version.parse(torch.__version__).base_version) < version.parse("""1.11""")


def 			UpperCAmelCase__       (				lowerCAmelCase__     :int					,		lowerCAmelCase__     :tuple					,		lowerCAmelCase__     :Path					,		lowerCAmelCase__     :str					,		lowerCAmelCase__     :int					,		lowerCAmelCase__     :Union[str, Any]					,		lowerCAmelCase__     :Any					,		lowerCAmelCase__     :List[str]=False					,		) ->    Union[str, Any]:
      '''simple docstring'''


      output_path.parent.mkdir(parents=lowerCAmelCase__					,		exist_ok=lowerCAmelCase__		)
      # PyTorch deprecated the `enable_onnx_checker` and `use_external_data_format` arguments in v1.11,
      # so we check the torch version for backwards compatibility
      if is_torch_less_than_1_11:
            export(
                lowerCAmelCase__					,		lowerCAmelCase__					,		f=output_path.as_posix()					,		input_names=lowerCAmelCase__					,		output_names=lowerCAmelCase__					,		dynamic_axes=lowerCAmelCase__					,		do_constant_folding=lowerCAmelCase__					,		use_external_data_format=lowerCAmelCase__					,		enable_onnx_checker=lowerCAmelCase__					,		opset_version=lowerCAmelCase__					,		)
      else:
            export(
                lowerCAmelCase__					,		lowerCAmelCase__					,		f=output_path.as_posix()					,		input_names=lowerCAmelCase__					,		output_names=lowerCAmelCase__					,		dynamic_axes=lowerCAmelCase__					,		do_constant_folding=lowerCAmelCase__					,		opset_version=lowerCAmelCase__					,		)


@torch.no_grad()
def 			UpperCAmelCase__       (				lowerCAmelCase__     :str					,		lowerCAmelCase__     :str					,		lowerCAmelCase__     :int					,		lowerCAmelCase__     :bool = False		) ->    str:
      '''simple docstring'''


      lowercase														=	torch.floataa if fpaa else torch.floataa
      if fpaa and torch.cuda.is_available():
            lowercase														=	"""cuda"""
      elif fpaa and not torch.cuda.is_available():
            raise ValueError("""`float16` model export is only supported on GPUs with CUDA"""		)
      else:
            lowercase														=	"""cpu"""
      lowercase														=	Path(lowerCAmelCase__		)

      # VAE DECODER
      lowercase														=	AutoencoderKL.from_pretrained(model_path + """/vae"""		)
      lowercase														=	vae_decoder.config.latent_channels
      # forward only through the decoder part
      lowercase														=	vae_decoder.decode
      onnx_export(
          lowerCAmelCase__					,		model_args=(
              torch.randn(1					,		lowerCAmelCase__					,		2_5					,		2_5		).to(device=lowerCAmelCase__					,		dtype=lowerCAmelCase__		),
              False,
          )					,		output_path=output_path / """vae_decoder""" / """model.onnx"""					,		ordered_input_names=["""latent_sample""", """return_dict"""]					,		output_names=["""sample"""]					,		dynamic_axes={
              """latent_sample""": {0: """batch""", 1: """channels""", 2: """height""", 3: """width"""},
          }					,		opset=lowerCAmelCase__					,		)
      del vae_decoder


if __name__ == "__main__":
       __lowerCAmelCase    :       Tuple					     =argparse.ArgumentParser()

       parser.add_argument(
           """--model_path""",
           type=str,
           required=True,
           help="""Path to the `diffusers` checkpoint to convert (either a local directory or on the Hub).""",
       )

       parser.add_argument("""--output_path""", type=str, required=True, help="""Path to the output model.""")
       parser.add_argument(
           """--opset""",
           default=1_4,
           type=int,
           help="""The version of the ONNX operator set to use.""",
       )
       parser.add_argument("""--fp16""", action="""store_true""", default=False, help="""Export the models in `float16` mode""")

       __lowerCAmelCase    :       Dict					     =parser.parse_args()
       print(args.output_path)
       convert_models(args.model_path, args.output_path, args.opset, args.fpaa)
       print("""SD: Done: ONNX""")



