'''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


__lowercase          =  logging.get_logger(__name__)

__lowercase          =  {
    '''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__	):


			'''simple docstring'''






			UpperCAmelCase_			:     Union[str, Any]      =       '''blenderbot-small'''
			UpperCAmelCase_			:     Union[str, Any]      =       ['''past_key_values''']
			UpperCAmelCase_			:     Tuple      =       {'''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       ,    ):




					"""simple docstring"""


					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 				a__(					lowerCAmelCase__	):


			'''simple docstring'''






			@property
			def 							a_       (   self):




					"""simple docstring"""


					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 							a_       (   self):




					"""simple docstring"""


					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 							a_       (   self       ,    __lowerCAmelCase       ,    __lowerCAmelCase = -1       ,    __lowerCAmelCase = -1       ,    __lowerCAmelCase = False       ,    __lowerCAmelCase = None       ,    ):




					"""simple docstring"""


					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 							a_       (   self       ,    __lowerCAmelCase       ,    __lowerCAmelCase = -1       ,    __lowerCAmelCase = -1       ,    __lowerCAmelCase = False       ,    __lowerCAmelCase = None       ,    ):




					"""simple docstring"""


					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 							a_       (   self       ,    __lowerCAmelCase       ,    __lowerCAmelCase = -1       ,    __lowerCAmelCase = -1       ,    __lowerCAmelCase = False       ,    __lowerCAmelCase = None       ,    ):




					"""simple docstring"""


					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 							a_       (   self       ,    __lowerCAmelCase       ,    __lowerCAmelCase = -1       ,    __lowerCAmelCase = -1       ,    __lowerCAmelCase = False       ,    __lowerCAmelCase = None       ,    ):




					"""simple docstring"""


					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 							a_       (   self       ,    __lowerCAmelCase       ,    __lowerCAmelCase       ,    __lowerCAmelCase       ,    __lowerCAmelCase):




					"""simple docstring"""


					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)





'''simple docstring'''




from string import ascii_lowercase, ascii_uppercase






def 				snake_case__							(			_A:					str )    ->	str:

		'''simple docstring'''


		if not sentence:
				return ""
		lowerCAmelCase   					=  dict(zip(_A      ,   _A ) )
		return lower_to_upper.get(sentence[0]      ,   sentence[0] ) + sentence[1:]


if __name__ == "__main__":
						from doctest import testmod

						testmod()




