
"""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


__A 		= logging.get_logger(__name__)

__A 		= {
    'facebook/blenderbot-3B': 'https://huggingface.co/facebook/blenderbot-3B/resolve/main/config.json',
    # See all Blenderbot models at https://huggingface.co/models?filter=blenderbot
}







class 	lowerCamelCase__      (      __magic_name__       ):





			'''simple docstring'''
			lowerCamelCase						      =	'''blenderbot'''
			lowerCamelCase						      =	['''past_key_values''']
			lowerCamelCase						      =	{'''num_attention_heads''': '''encoder_attention_heads''', '''hidden_size''': '''d_model'''}



			def __init__(							self  ,  __UpperCAmelCase=80_08  ,  __UpperCAmelCase=1_28  ,  __UpperCAmelCase=2  ,  __UpperCAmelCase=1_02_40  ,  __UpperCAmelCase=32  ,  __UpperCAmelCase=24  ,  __UpperCAmelCase=1_02_40  ,  __UpperCAmelCase=32  ,  __UpperCAmelCase=0.0  ,  __UpperCAmelCase=0.0  ,  __UpperCAmelCase=True  ,  __UpperCAmelCase=True  ,  __UpperCAmelCase="gelu"  ,  __UpperCAmelCase=25_60  ,  __UpperCAmelCase=0.1  ,  __UpperCAmelCase=0.0  ,  __UpperCAmelCase=0.0  ,  __UpperCAmelCase=0.0_2  ,  __UpperCAmelCase=1  ,  __UpperCAmelCase=False  ,  __UpperCAmelCase=0  ,  __UpperCAmelCase=1  ,  __UpperCAmelCase=2  ,  __UpperCAmelCase=3  ,  __UpperCAmelCase=2  ,  **__UpperCAmelCase  ,  )				->  Union[str, Any]:
				_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=__UpperCAmelCase  ,  bos_token_id=__UpperCAmelCase  ,  eos_token_id=__UpperCAmelCase  ,  is_encoder_decoder=__UpperCAmelCase  ,  decoder_start_token_id=__UpperCAmelCase  ,  encoder_no_repeat_ngram_size=__UpperCAmelCase  ,  forced_eos_token_id=__UpperCAmelCase  ,  **__UpperCAmelCase  ,  )







class 	lowerCamelCase__      (      __magic_name__       ):





			'''simple docstring'''



			@property
			def       _lowerCAmelCase	(							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_(__UpperCAmelCase  ,  direction="""inputs"""						)
				elif self.task == "causal-lm":
					_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(__UpperCAmelCase						):
							_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
			# Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.outputs
			def       _lowerCAmelCase	(							self						)				->  Mapping[str, Mapping[int, str]]:
				if self.task in ["default", "seq2seq-lm"]:
					_lowerCAmelCase			=super().outputs
				else:
					_lowerCAmelCase			=super(__UpperCAmelCase  ,  self						).outputs
					if self.use_past:
						_lowerCAmelCase		,							_lowerCAmelCase			=self.num_layers
						for i in range(__UpperCAmelCase						):
							_lowerCAmelCase			={0: """batch""", 2: """past_sequence + sequence"""}
							_lowerCAmelCase			={0: """batch""", 2: """past_sequence + sequence"""}
				return common_outputs



			def       _lowerCAmelCase	(							self  ,  __UpperCAmelCase  ,  __UpperCAmelCase = -1  ,  __UpperCAmelCase = -1  ,  __UpperCAmelCase = False  ,  __UpperCAmelCase = None  ,  )				->  Mapping[str, Any]:
				_lowerCAmelCase			=self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
				    __UpperCAmelCase  ,  __UpperCAmelCase  ,  __UpperCAmelCase  ,  __UpperCAmelCase  ,  __UpperCAmelCase						)
				# Generate decoder inputs
				_lowerCAmelCase			=seq_length if not self.use_past else 1
				_lowerCAmelCase			=self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
				    __UpperCAmelCase  ,  __UpperCAmelCase  ,  __UpperCAmelCase  ,  __UpperCAmelCase  ,  __UpperCAmelCase						)
				_lowerCAmelCase			={f'''decoder_{name}''': tensor for name, tensor in decoder_inputs.items()}
				_lowerCAmelCase			=dict(**__UpperCAmelCase  ,  **__UpperCAmelCase						)

				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
					_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(__UpperCAmelCase  ,  __UpperCAmelCase						)]  ,  dim=1						)
					_lowerCAmelCase			=[]
					_lowerCAmelCase		,							_lowerCAmelCase			=self.num_layers

					for _ in range(__UpperCAmelCase						):
						common_inputs["past_key_values"].append(
						    (
						        torch.zeros(__UpperCAmelCase						),
						        torch.zeros(__UpperCAmelCase						),
						        torch.zeros(__UpperCAmelCase						),
						        torch.zeros(__UpperCAmelCase						),
						    )						)
				return common_inputs



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

				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			=seqlen
					_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(__UpperCAmelCase  ,  __UpperCAmelCase  ,  dtype=__UpperCAmelCase						)]  ,  dim=1						)
					_lowerCAmelCase			=[
					    (torch.zeros(__UpperCAmelCase						), torch.zeros(__UpperCAmelCase						)) for _ in range(__UpperCAmelCase						)
					]
				return common_inputs



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

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



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

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

				return common_inputs



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



			def       _lowerCAmelCase	(							self  ,  __UpperCAmelCase  ,  __UpperCAmelCase						)				->  Optional[int]:
				if direction not in ["inputs", "outputs"]:
					raise ValueError(f'''direction must either be "inputs" or "outputs", but {direction} was given'''						)

				_lowerCAmelCase			="""past_key_values""" if direction == """inputs""" else """present"""
				_lowerCAmelCase		,							_lowerCAmelCase			=self.num_layers

				_lowerCAmelCase			="""past_encoder_sequence"""
				_lowerCAmelCase			="""past_decoder_sequence""" if direction == """inputs""" else """past_decoder_sequence + sequence"""

				for i in range(__UpperCAmelCase						):
					_lowerCAmelCase			={0: """batch""", 2: decoder_sequence}
					_lowerCAmelCase			={0: """batch""", 2: decoder_sequence}
					_lowerCAmelCase			={0: """batch""", 2: encoder_sequence}
					_lowerCAmelCase			={0: """batch""", 2: encoder_sequence}





"""simple docstring"""


def 					_lowerCamelCase(__UpperCamelCase					,				__UpperCamelCase					,				__UpperCamelCase )       ->				float:
	if mass < 0:
		raise ValueError("""The mass of the body cannot be negative""" )
	if radius <= 0:
		raise ValueError("""The radius is always a positive non zero integer""" )
	return (mass * (velocity) ** 2) / radius


if __name__ == "__main__":
			import doctest

			doctest.testmod(verbose=True)



