"""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    :       Dict					     =logging.get_logger(__name__)

__lowerCAmelCase    :       List[Any]					     ={
    """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__			:   Any							  =	'blenderbot-small'
      snake_case__			:   List[Any]							  =	['past_key_values']
      snake_case__			:   List[str]							  =	{'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						)




"""simple docstring"""






from __future__ import annotations

from typing import Any





class  _A						:

      def __init__(						self		,       __lowerCAmelCase = 6						):







            """simple docstring"""



            lowercase														=	None
            lowercase														=	None
            self.create_linked_list(__lowerCAmelCase						)

      def 				A__       (						self		,       __lowerCAmelCase						):







            """simple docstring"""



            lowercase														=	Node()
            lowercase														=	current_node
            lowercase														=	current_node
            lowercase														=	current_node
            for _ in range(1		,       __lowerCAmelCase						):
                  lowercase														=	Node()
                  lowercase														=	current_node
                  lowercase														=	previous_node
                  lowercase														=	current_node
            lowercase														=	self.front
            lowercase														=	previous_node

      def 				A__       (						self						):







            """simple docstring"""



            return (
                self.front == self.rear
                and self.front is not None
                and self.front.data is None
            )

      def 				A__       (						self						):







            """simple docstring"""



            self.check_can_perform_operation()
            return self.front.data if self.front else None

      def 				A__       (						self		,       __lowerCAmelCase						):







            """simple docstring"""



            if self.rear is None:
                  return

            self.check_is_full()
            if not self.is_empty():
                  lowercase														=	self.rear.next
            if self.rear:
                  lowercase														=	data

      def 				A__       (						self						):







            """simple docstring"""



            self.check_can_perform_operation()
            if self.rear is None or self.front is None:
                  return None
            if self.front == self.rear:
                  lowercase														=	self.front.data
                  lowercase														=	None
                  return data

            lowercase														=	self.front
            lowercase														=	old_front.next
            lowercase														=	old_front.data
            lowercase														=	None
            return data

      def 				A__       (						self						):







            """simple docstring"""



            if self.is_empty():
                  raise Exception("""Empty Queue"""						)







      def 				A__       (						self						):







            """simple docstring"""



            if self.rear and self.rear.next == self.front:
                  raise Exception("""Full Queue"""						)






class  _A						:

      def __init__(						self						):







            """simple docstring"""



            lowercase														=	None
            lowercase														=	None
            lowercase														=	None


if __name__ == "__main__":
       import doctest

       doctest.testmod()



