"""simple docstring"""






import argparse
import os

import torch

from diffusers import (
    CMStochasticIterativeScheduler,
    ConsistencyModelPipeline,
    UNetaDModel,
)


__lowerCAmelCase					: Any        ={
    """sample_size""": 3_2,
    """in_channels""": 3,
    """out_channels""": 3,
    """layers_per_block""": 2,
    """num_class_embeds""": 1_0_0_0,
    """block_out_channels""": [3_2, 6_4],
    """attention_head_dim""": 8,
    """down_block_types""": [
        """ResnetDownsampleBlock2D""",
        """AttnDownBlock2D""",
    ],
    """up_block_types""": [
        """AttnUpBlock2D""",
        """ResnetUpsampleBlock2D""",
    ],
    """resnet_time_scale_shift""": """scale_shift""",
    """upsample_type""": """resnet""",
    """downsample_type""": """resnet""",
}

__lowerCAmelCase					: str        ={
    """sample_size""": 6_4,
    """in_channels""": 3,
    """out_channels""": 3,
    """layers_per_block""": 3,
    """num_class_embeds""": 1_0_0_0,
    """block_out_channels""": [1_9_2, 1_9_2 * 2, 1_9_2 * 3, 1_9_2 * 4],
    """attention_head_dim""": 6_4,
    """down_block_types""": [
        """ResnetDownsampleBlock2D""",
        """AttnDownBlock2D""",
        """AttnDownBlock2D""",
        """AttnDownBlock2D""",
    ],
    """up_block_types""": [
        """AttnUpBlock2D""",
        """AttnUpBlock2D""",
        """AttnUpBlock2D""",
        """ResnetUpsampleBlock2D""",
    ],
    """resnet_time_scale_shift""": """scale_shift""",
    """upsample_type""": """resnet""",
    """downsample_type""": """resnet""",
}

__lowerCAmelCase					: Tuple        ={
    """sample_size""": 2_5_6,
    """in_channels""": 3,
    """out_channels""": 3,
    """layers_per_block""": 2,
    """num_class_embeds""": None,
    """block_out_channels""": [2_5_6, 2_5_6, 2_5_6 * 2, 2_5_6 * 2, 2_5_6 * 4, 2_5_6 * 4],
    """attention_head_dim""": 6_4,
    """down_block_types""": [
        """ResnetDownsampleBlock2D""",
        """ResnetDownsampleBlock2D""",
        """ResnetDownsampleBlock2D""",
        """AttnDownBlock2D""",
        """AttnDownBlock2D""",
        """AttnDownBlock2D""",
    ],
    """up_block_types""": [
        """AttnUpBlock2D""",
        """AttnUpBlock2D""",
        """AttnUpBlock2D""",
        """ResnetUpsampleBlock2D""",
        """ResnetUpsampleBlock2D""",
        """ResnetUpsampleBlock2D""",
    ],
    """resnet_time_scale_shift""": """default""",
    """upsample_type""": """resnet""",
    """downsample_type""": """resnet""",
}

__lowerCAmelCase					: Any        ={
    """num_train_timesteps""": 4_0,
    """sigma_min""": 0.002,
    """sigma_max""": 80.0,
}

__lowerCAmelCase					: str        ={
    """num_train_timesteps""": 2_0_1,
    """sigma_min""": 0.002,
    """sigma_max""": 80.0,
}

__lowerCAmelCase					: List[str]        ={
    """num_train_timesteps""": 1_5_1,
    """sigma_min""": 0.002,
    """sigma_max""": 80.0,
}


def     UpperCAmelCase__ (       lowerCAmelCase__  :Union[str, Any]	)				->					str:
      '''simple docstring'''


      if isinstance(lowerCAmelCase__      ,	lowerCAmelCase__	):
            return v
      if v.lower() in ("yes", "true", "t", "y", "1"):
            return True
      elif v.lower() in ("no", "false", "f", "n", "0"):
            return False
      else:
            raise argparse.ArgumentTypeError("""boolean value expected"""	)


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


      lowercase														=			checkpoint[f'{old_prefix}.in_layers.0.weight']
      lowercase														=			checkpoint[f'{old_prefix}.in_layers.0.bias']
      lowercase														=			checkpoint[f'{old_prefix}.in_layers.2.weight']
      lowercase														=			checkpoint[f'{old_prefix}.in_layers.2.bias']
      lowercase														=			checkpoint[f'{old_prefix}.emb_layers.1.weight']
      lowercase														=			checkpoint[f'{old_prefix}.emb_layers.1.bias']
      lowercase														=			checkpoint[f'{old_prefix}.out_layers.0.weight']
      lowercase														=			checkpoint[f'{old_prefix}.out_layers.0.bias']
      lowercase														=			checkpoint[f'{old_prefix}.out_layers.3.weight']
      lowercase														=			checkpoint[f'{old_prefix}.out_layers.3.bias']

      if has_skip:
            lowercase														=			checkpoint[f'{old_prefix}.skip_connection.weight']
            lowercase														=			checkpoint[f'{old_prefix}.skip_connection.bias']

      return new_checkpoint


def     UpperCAmelCase__ (       lowerCAmelCase__  :List[Any]      ,	lowerCAmelCase__  :Optional[Any]      ,	lowerCAmelCase__  :Union[str, Any]      ,	lowerCAmelCase__  :List[Any]      ,	lowerCAmelCase__  :Optional[Any]=None	)				->					int:
      '''simple docstring'''


      lowercase							,							lowercase							,							lowercase														=			checkpoint[f'{old_prefix}.qkv.weight'].chunk(3      ,	dim=0	)
      lowercase							,							lowercase							,							lowercase														=			checkpoint[f'{old_prefix}.qkv.bias'].chunk(3      ,	dim=0	)

      lowercase														=			checkpoint[f'{old_prefix}.norm.weight']
      lowercase														=			checkpoint[f'{old_prefix}.norm.bias']

      lowercase														=			weight_q.squeeze(-1	).squeeze(-1	)
      lowercase														=			bias_q.squeeze(-1	).squeeze(-1	)
      lowercase														=			weight_k.squeeze(-1	).squeeze(-1	)
      lowercase														=			bias_k.squeeze(-1	).squeeze(-1	)
      lowercase														=			weight_v.squeeze(-1	).squeeze(-1	)
      lowercase														=			bias_v.squeeze(-1	).squeeze(-1	)

      lowercase														=			(
          checkpoint[f'{old_prefix}.proj_out.weight'].squeeze(-1	).squeeze(-1	)
      )
      lowercase														=			checkpoint[f'{old_prefix}.proj_out.bias'].squeeze(-1	).squeeze(-1	)

      return new_checkpoint


def     UpperCAmelCase__ (       lowerCAmelCase__  :str      ,	lowerCAmelCase__  :Dict	)				->					Dict:
      '''simple docstring'''


      lowercase														=			torch.load(lowerCAmelCase__      ,	map_location="""cpu"""	)
      lowercase														=			{}

      lowercase														=			checkpoint["""time_embed.0.weight"""]
      lowercase														=			checkpoint["""time_embed.0.bias"""]
      lowercase														=			checkpoint["""time_embed.2.weight"""]
      lowercase														=			checkpoint["""time_embed.2.bias"""]

      if unet_config["num_class_embeds"] is not None:
            lowercase														=			checkpoint["""label_emb.weight"""]

      lowercase														=			checkpoint["""input_blocks.0.0.weight"""]
      lowercase														=			checkpoint["""input_blocks.0.0.bias"""]

      lowercase														=			unet_config["""down_block_types"""]
      lowercase														=			unet_config["""layers_per_block"""]
      lowercase														=			unet_config["""attention_head_dim"""]
      lowercase														=			unet_config["""block_out_channels"""]
      lowercase														=			1
      lowercase														=			channels_list[0]

      for i, layer_type in enumerate(lowerCAmelCase__	):
            lowercase														=			channels_list[i]
            lowercase														=			current_channels != prev_channels
            if layer_type == "ResnetDownsampleBlock2D":
                  for j in range(lowerCAmelCase__	):
                        lowercase														=			f'down_blocks.{i}.resnets.{j}'
                        lowercase														=			f'input_blocks.{current_layer}.0'
                        lowercase														=			True if j == 0 and downsample_block_has_skip else False
                        lowercase														=			convert_resnet(lowerCAmelCase__      ,	lowerCAmelCase__      ,	lowerCAmelCase__      ,	lowerCAmelCase__      ,	has_skip=lowerCAmelCase__	)
                        current_layer += 1

            elif layer_type == "AttnDownBlock2D":
                  for j in range(lowerCAmelCase__	):
                        lowercase														=			f'down_blocks.{i}.resnets.{j}'
                        lowercase														=			f'input_blocks.{current_layer}.0'
                        lowercase														=			True if j == 0 and downsample_block_has_skip else False
                        lowercase														=			convert_resnet(lowerCAmelCase__      ,	lowerCAmelCase__      ,	lowerCAmelCase__      ,	lowerCAmelCase__      ,	has_skip=lowerCAmelCase__	)
                        lowercase														=			f'down_blocks.{i}.attentions.{j}'
                        lowercase														=			f'input_blocks.{current_layer}.1'
                        lowercase														=			convert_attention(
                            lowerCAmelCase__      ,	lowerCAmelCase__      ,	lowerCAmelCase__      ,	lowerCAmelCase__      ,	lowerCAmelCase__	)
                        current_layer += 1

            if i != len(lowerCAmelCase__	) - 1:
                  lowercase														=			f'down_blocks.{i}.downsamplers.0'
                  lowercase														=			f'input_blocks.{current_layer}.0'
                  lowercase														=			convert_resnet(lowerCAmelCase__      ,	lowerCAmelCase__      ,	lowerCAmelCase__      ,	lowerCAmelCase__	)
                  current_layer += 1

            lowercase														=			current_channels

      # hardcoded the mid-block for now
      lowercase														=			"""mid_block.resnets.0"""
      lowercase														=			"""middle_block.0"""
      lowercase														=			convert_resnet(lowerCAmelCase__      ,	lowerCAmelCase__      ,	lowerCAmelCase__      ,	lowerCAmelCase__	)
      lowercase														=			"""mid_block.attentions.0"""
      lowercase														=			"""middle_block.1"""
      lowercase														=			convert_attention(lowerCAmelCase__      ,	lowerCAmelCase__      ,	lowerCAmelCase__      ,	lowerCAmelCase__      ,	lowerCAmelCase__	)
      lowercase														=			"""mid_block.resnets.1"""
      lowercase														=			"""middle_block.2"""
      lowercase														=			convert_resnet(lowerCAmelCase__      ,	lowerCAmelCase__      ,	lowerCAmelCase__      ,	lowerCAmelCase__	)

      lowercase														=			0
      lowercase														=			unet_config["""up_block_types"""]

      for i, layer_type in enumerate(lowerCAmelCase__	):
            if layer_type == "ResnetUpsampleBlock2D":
                  for j in range(layers_per_block + 1	):
                        lowercase														=			f'up_blocks.{i}.resnets.{j}'
                        lowercase														=			f'output_blocks.{current_layer}.0'
                        lowercase														=			convert_resnet(lowerCAmelCase__      ,	lowerCAmelCase__      ,	lowerCAmelCase__      ,	lowerCAmelCase__      ,	has_skip=lowerCAmelCase__	)
                        current_layer += 1

                  if i != len(lowerCAmelCase__	) - 1:
                        lowercase														=			f'up_blocks.{i}.upsamplers.0'
                        lowercase														=			f'output_blocks.{current_layer-1}.1'
                        lowercase														=			convert_resnet(lowerCAmelCase__      ,	lowerCAmelCase__      ,	lowerCAmelCase__      ,	lowerCAmelCase__	)
            elif layer_type == "AttnUpBlock2D":
                  for j in range(layers_per_block + 1	):
                        lowercase														=			f'up_blocks.{i}.resnets.{j}'
                        lowercase														=			f'output_blocks.{current_layer}.0'
                        lowercase														=			convert_resnet(lowerCAmelCase__      ,	lowerCAmelCase__      ,	lowerCAmelCase__      ,	lowerCAmelCase__      ,	has_skip=lowerCAmelCase__	)
                        lowercase														=			f'up_blocks.{i}.attentions.{j}'
                        lowercase														=			f'output_blocks.{current_layer}.1'
                        lowercase														=			convert_attention(
                            lowerCAmelCase__      ,	lowerCAmelCase__      ,	lowerCAmelCase__      ,	lowerCAmelCase__      ,	lowerCAmelCase__	)
                        current_layer += 1

                  if i != len(lowerCAmelCase__	) - 1:
                        lowercase														=			f'up_blocks.{i}.upsamplers.0'
                        lowercase														=			f'output_blocks.{current_layer-1}.2'
                        lowercase														=			convert_resnet(lowerCAmelCase__      ,	lowerCAmelCase__      ,	lowerCAmelCase__      ,	lowerCAmelCase__	)

      lowercase														=			checkpoint["""out.0.weight"""]
      lowercase														=			checkpoint["""out.0.bias"""]
      lowercase														=			checkpoint["""out.2.weight"""]
      lowercase														=			checkpoint["""out.2.bias"""]

      return new_checkpoint


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

       parser.add_argument("""--unet_path""", default=None, type=str, required=True, help="""Path to the unet.pt to convert.""")
       parser.add_argument(
           """--dump_path""", default=None, type=str, required=True, help="""Path to output the converted UNet model."""
       )
       parser.add_argument("""--class_cond""", default=True, type=str, help="""Whether the model is class-conditional.""")

       __lowerCAmelCase					: List[Any]        =parser.parse_args()
       __lowerCAmelCase					: List[Any]        =strabool(args.class_cond)

       __lowerCAmelCase					: Union[str, Any]        =os.path.basename(args.unet_path)
       print(F"""Checkpoint: {ckpt_name}""")

       # Get U-Net config
       if "imagenet64" in ckpt_name:
              __lowerCAmelCase					: Optional[int]        =IMAGENET_64_UNET_CONFIG
       elif "256" in ckpt_name and (("bedroom" in ckpt_name) or ("cat" in ckpt_name)):
              __lowerCAmelCase					: Any        =LSUN_256_UNET_CONFIG
       elif "test" in ckpt_name:
              __lowerCAmelCase					: List[str]        =TEST_UNET_CONFIG
       else:
              raise ValueError(F"""Checkpoint type {ckpt_name} is not currently supported.""")

       if not args.class_cond:
              __lowerCAmelCase					: Optional[int]        =None

       __lowerCAmelCase					: Dict        =con_pt_to_diffuser(args.unet_path, unet_config)

       __lowerCAmelCase					: Union[str, Any]        =UNetaDModel(**unet_config)
       image_unet.load_state_dict(converted_unet_ckpt)

       # Get scheduler config
       if "cd" in ckpt_name or "test" in ckpt_name:
              __lowerCAmelCase					: List[Any]        =CD_SCHEDULER_CONFIG
       elif "ct" in ckpt_name and "imagenet64" in ckpt_name:
              __lowerCAmelCase					: List[str]        =CT_IMAGENET_64_SCHEDULER_CONFIG
       elif "ct" in ckpt_name and "256" in ckpt_name and (("bedroom" in ckpt_name) or ("cat" in ckpt_name)):
              __lowerCAmelCase					: List[Any]        =CT_LSUN_256_SCHEDULER_CONFIG
       else:
              raise ValueError(F"""Checkpoint type {ckpt_name} is not currently supported.""")

       __lowerCAmelCase					: Dict        =CMStochasticIterativeScheduler(**scheduler_config)

       __lowerCAmelCase					: Optional[int]        =ConsistencyModelPipeline(unet=image_unet, scheduler=cm_scheduler)
       consistency_model.save_pretrained(args.dump_path)






"""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


__lowerCAmelCase					: Tuple        =logging.get_logger(__name__)

__lowerCAmelCase					: Tuple        ={
    """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 				_A		( lowerCAmelCase			):
      snake_case__    :     int													=			'blenderbot-small'
      snake_case__    :     Optional[Any]													=			['past_key_values']
      snake_case__    :     Optional[Any]													=			{'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}

      def __init__(		self	, __lowerCAmelCase=5_0265	, __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=0.0_2	, __lowerCAmelCase=1	, __lowerCAmelCase=False	, __lowerCAmelCase=0	, __lowerCAmelCase=1	, __lowerCAmelCase=2	, __lowerCAmelCase=2	, **__lowerCAmelCase	, ):







            """simple docstring"""



            lowercase														=			vocab_size
            lowercase														=			max_position_embeddings
            lowercase														=			d_model
            lowercase														=			encoder_ffn_dim
            lowercase														=			encoder_layers
            lowercase														=			encoder_attention_heads
            lowercase														=			decoder_ffn_dim
            lowercase														=			decoder_layers
            lowercase														=			decoder_attention_heads
            lowercase														=			dropout
            lowercase														=			attention_dropout
            lowercase														=			activation_dropout
            lowercase														=			activation_function
            lowercase														=			init_std
            lowercase														=			encoder_layerdrop
            lowercase														=			decoder_layerdrop
            lowercase														=			use_cache
            lowercase														=			encoder_layers
            lowercase														=			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 				_A		( lowerCAmelCase			):

      @property
      def        A__ (		self		):







            """simple docstring"""



            if self.task in ["default", "seq2seq-lm"]:
                  lowercase														=			OrderedDict(
                      [
                          ("""input_ids""", {0: """batch""", 1: """encoder_sequence"""}),
                          ("""attention_mask""", {0: """batch""", 1: """encoder_sequence"""}),
                      ]		)

                  if self.use_past:
                        lowercase														=			{0: """batch"""}
                        lowercase														=			{0: """batch""", 1: """past_decoder_sequence + sequence"""}
                  else:
                        lowercase														=			{0: """batch""", 1: """decoder_sequence"""}
                        lowercase														=			{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.
                  lowercase														=			OrderedDict(
                      [
                          ("""input_ids""", {0: """batch""", 1: """encoder_sequence"""}),
                          ("""attention_mask""", {0: """batch""", 1: """encoder_sequence"""}),
                      ]		)
                  if self.use_past:
                        lowercase							,							lowercase														=			self.num_layers
                        for i in range(__lowerCAmelCase		):
                              lowercase														=			{0: """batch""", 2: """past_sequence + sequence"""}
                              lowercase														=			{0: """batch""", 2: """past_sequence + sequence"""}
            else:
                  lowercase														=			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        A__ (		self		):







            """simple docstring"""



            if self.task in ["default", "seq2seq-lm"]:
                  lowercase														=			super().outputs
            else:
                  lowercase														=			super(__lowerCAmelCase	, self		).outputs
                  if self.use_past:
                        lowercase							,							lowercase														=			self.num_layers
                        for i in range(__lowerCAmelCase		):
                              lowercase														=			{0: """batch""", 2: """past_sequence + sequence"""}
                              lowercase														=			{0: """batch""", 2: """past_sequence + sequence"""}
            return common_outputs

      def        A__ (		self	, __lowerCAmelCase	, __lowerCAmelCase = -1	, __lowerCAmelCase = -1	, __lowerCAmelCase = False	, __lowerCAmelCase = None	, ):







            """simple docstring"""



            lowercase														=			self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
                __lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase		)

            # Generate decoder inputs
            lowercase														=			seq_length if not self.use_past else 1
            lowercase														=			self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
                __lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase		)
            lowercase														=			{f'decoder_{name}': tensor for name, tensor in decoder_inputs.items()}
            lowercase														=			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
                  lowercase							,							lowercase														=			common_inputs["""input_ids"""].shape
                  lowercase														=			common_inputs["""decoder_input_ids"""].shape[1]
                  lowercase							,							lowercase														=			self.num_attention_heads
                  lowercase														=			(
                      batch,
                      num_encoder_attention_heads,
                      encoder_seq_length,
                      self._config.hidden_size // num_encoder_attention_heads,
                  )
                  lowercase														=			decoder_seq_length + 3
                  lowercase														=			(
                      batch,
                      num_decoder_attention_heads,
                      decoder_past_length,
                      self._config.hidden_size // num_decoder_attention_heads,
                  )

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

                  lowercase														=			[]
                  # If the number of encoder and decoder layers are present in the model configuration, both are considered
                  lowercase							,							lowercase														=			self.num_layers
                  lowercase														=			min(__lowerCAmelCase	, __lowerCAmelCase		)
                  lowercase														=			max(__lowerCAmelCase	, __lowerCAmelCase		) - min_num_layers
                  lowercase														=			"""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.
                  lowercase														=			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        A__ (		self	, __lowerCAmelCase	, __lowerCAmelCase = -1	, __lowerCAmelCase = -1	, __lowerCAmelCase = False	, __lowerCAmelCase = None	, ):







            """simple docstring"""



            lowercase														=			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
                  lowercase							,							lowercase														=			common_inputs["""input_ids"""].shape
                  # Not using the same length for past_key_values
                  lowercase														=			seqlen + 2
                  lowercase							,							lowercase														=			self.num_layers
                  lowercase							,							lowercase														=			self.num_attention_heads
                  lowercase														=			(
                      batch,
                      num_encoder_attention_heads,
                      past_key_values_length,
                      self._config.hidden_size // num_encoder_attention_heads,
                  )

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

      def        A__ (		self	, __lowerCAmelCase	, __lowerCAmelCase = -1	, __lowerCAmelCase = -1	, __lowerCAmelCase = False	, __lowerCAmelCase = None	, ):







            """simple docstring"""



            lowercase														=			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
            lowercase														=			tokenizer.num_special_tokens_to_add(__lowerCAmelCase		)
            lowercase														=			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
            lowercase														=			[""" """.join([tokenizer.unk_token]		) * seq_length] * batch_size
            lowercase														=			dict(tokenizer(__lowerCAmelCase	, return_tensors=__lowerCAmelCase		)		)
            return common_inputs

      def        A__ (		self	, __lowerCAmelCase	, __lowerCAmelCase = -1	, __lowerCAmelCase = -1	, __lowerCAmelCase = False	, __lowerCAmelCase = None	, ):







            """simple docstring"""



            if self.task in ["default", "seq2seq-lm"]:
                  lowercase														=			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":
                  lowercase														=			self._generate_dummy_inputs_for_causal_lm(
                      __lowerCAmelCase	, batch_size=__lowerCAmelCase	, seq_length=__lowerCAmelCase	, is_pair=__lowerCAmelCase	, framework=__lowerCAmelCase		)
            else:
                  lowercase														=			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        A__ (		self	, __lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase		):







            """simple docstring"""



            if self.task in ["default", "seq2seq-lm"]:
                  lowercase														=			super()._flatten_past_key_values_(__lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase		)
            else:
                  lowercase														=			super(__lowerCAmelCase	, self		)._flatten_past_key_values_(
                      __lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase		)



