




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_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				(		SCREAMING_SNAKE_CASE__     ):
		__a					    =       """blenderbot-small"""
		__a					    =       ["""past_key_values"""]
		__a					    =       {"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}






		def __init__(    self						,		_SCREAMING_SNAKE_CASE=5_0265						,		_SCREAMING_SNAKE_CASE=512						,		_SCREAMING_SNAKE_CASE=8						,		_SCREAMING_SNAKE_CASE=2048						,		_SCREAMING_SNAKE_CASE=16						,		_SCREAMING_SNAKE_CASE=8						,		_SCREAMING_SNAKE_CASE=2048						,		_SCREAMING_SNAKE_CASE=16						,		_SCREAMING_SNAKE_CASE=0.0						,		_SCREAMING_SNAKE_CASE=0.0						,		_SCREAMING_SNAKE_CASE=True						,		_SCREAMING_SNAKE_CASE=True						,		_SCREAMING_SNAKE_CASE="gelu"						,		_SCREAMING_SNAKE_CASE=512						,		_SCREAMING_SNAKE_CASE=0.1						,		_SCREAMING_SNAKE_CASE=0.0						,		_SCREAMING_SNAKE_CASE=0.0						,		_SCREAMING_SNAKE_CASE=0.02						,		_SCREAMING_SNAKE_CASE=1						,		_SCREAMING_SNAKE_CASE=False						,		_SCREAMING_SNAKE_CASE=0						,		_SCREAMING_SNAKE_CASE=1						,		_SCREAMING_SNAKE_CASE=2						,		_SCREAMING_SNAKE_CASE=2						,		**_SCREAMING_SNAKE_CASE						,		):
								_UpperCAmelCase      =			vocab_size
								_UpperCAmelCase      =			max_position_embeddings
								_UpperCAmelCase      =			d_model
								_UpperCAmelCase      =			encoder_ffn_dim
								_UpperCAmelCase      =			encoder_layers
								_UpperCAmelCase      =			encoder_attention_heads
								_UpperCAmelCase      =			decoder_ffn_dim
								_UpperCAmelCase      =			decoder_layers
								_UpperCAmelCase      =			decoder_attention_heads
								_UpperCAmelCase      =			dropout
								_UpperCAmelCase      =			attention_dropout
								_UpperCAmelCase      =			activation_dropout
								_UpperCAmelCase      =			activation_function
								_UpperCAmelCase      =			init_std
								_UpperCAmelCase      =			encoder_layerdrop
								_UpperCAmelCase      =			decoder_layerdrop
								_UpperCAmelCase      =			use_cache
								_UpperCAmelCase      =			encoder_layers
								_UpperCAmelCase      =			scale_embedding  # scale factor will be sqrt(d_model) if True

								super().__init__(
								    pad_token_id=_lowercase						,		bos_token_id=_lowercase						,		eos_token_id=_lowercase						,		is_encoder_decoder=_lowercase						,		decoder_start_token_id=_lowercase						,		forced_eos_token_id=_lowercase						,		**_lowercase						,		)




class       _A				(		SCREAMING_SNAKE_CASE__     ):






		@property
		def 				UpperCAmelCase (    self			):

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

														if self.use_past:
																				_UpperCAmelCase      =			{0: """batch"""}
																				_UpperCAmelCase      =			{0: """batch""", 1: """past_decoder_sequence + sequence"""}
														else:
																				_UpperCAmelCase      =			{0: """batch""", 1: """decoder_sequence"""}
																				_UpperCAmelCase      =			{0: """batch""", 1: """decoder_sequence"""}

														if self.use_past:
																				self.fill_with_past_key_values_(_lowercase						,		direction="""inputs"""			)
								elif self.task == "causal-lm":
														# TODO: figure this case out.
														_UpperCAmelCase      =			OrderedDict(
														    [
														        ("""input_ids""", {0: """batch""", 1: """encoder_sequence"""}),
														        ("""attention_mask""", {0: """batch""", 1: """encoder_sequence"""}),
														    ]			)
														if self.use_past:
																				_UpperCAmelCase      =			self.num_layers
																				for i in range(_lowercase			):
																										_UpperCAmelCase      =			{0: """batch""", 2: """past_sequence + sequence"""}
																										_UpperCAmelCase      =			{0: """batch""", 2: """past_sequence + sequence"""}
								else:
														_UpperCAmelCase      =			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 				UpperCAmelCase (    self			):

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






		def 				UpperCAmelCase (    self						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE = -1						,		_SCREAMING_SNAKE_CASE = -1						,		_SCREAMING_SNAKE_CASE = False						,		_SCREAMING_SNAKE_CASE = None						,		):
								_UpperCAmelCase      =			self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
								    _lowercase						,		_lowercase						,		_lowercase						,		_lowercase						,		_lowercase			)

								# Generate decoder inputs
								_UpperCAmelCase      =			seq_length if not self.use_past else 1
								_UpperCAmelCase      =			self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
								    _lowercase						,		_lowercase						,		_lowercase						,		_lowercase						,		_lowercase			)
								_UpperCAmelCase      =			{F"decoder_{name}": tensor for name, tensor in decoder_inputs.items()}
								_UpperCAmelCase      =			dict(**_lowercase						,		**_lowercase			)

								if self.use_past:
														if not is_torch_available():
																				raise ValueError("""Cannot generate dummy past_keys inputs without PyTorch installed."""			)
														else:
																				import torch
														_UpperCAmelCase      =			common_inputs["""input_ids"""].shape
														_UpperCAmelCase      =			common_inputs["""decoder_input_ids"""].shape[1]
														_UpperCAmelCase      =			self.num_attention_heads
														_UpperCAmelCase      =			(
														    batch,
														    num_encoder_attention_heads,
														    encoder_seq_length,
														    self._config.hidden_size // num_encoder_attention_heads,
														)
														_UpperCAmelCase      =			decoder_seq_length + 3
														_UpperCAmelCase      =			(
														    batch,
														    num_decoder_attention_heads,
														    decoder_past_length,
														    self._config.hidden_size // num_decoder_attention_heads,
														)

														_UpperCAmelCase      =			torch.cat(
														    [common_inputs["""decoder_attention_mask"""], torch.ones(_lowercase						,		_lowercase			)]						,		dim=1			)

														_UpperCAmelCase      =			[]
														# If the number of encoder and decoder layers are present in the model configuration, both are considered
														_UpperCAmelCase      =			self.num_layers
														_UpperCAmelCase      =			min(_lowercase						,		_lowercase			)
														_UpperCAmelCase      =			max(_lowercase						,		_lowercase			) - min_num_layers
														_UpperCAmelCase      =			"""encoder""" if num_encoder_layers > num_decoder_layers else """decoder"""

														for _ in range(_lowercase			):
																				common_inputs["past_key_values"].append(
																				    (
																				        torch.zeros(_lowercase			),
																				        torch.zeros(_lowercase			),
																				        torch.zeros(_lowercase			),
																				        torch.zeros(_lowercase			),
																				    )			)
														# TODO: test this.
														_UpperCAmelCase      =			encoder_shape if remaining_side_name == """encoder""" else decoder_shape
														for _ in range(_lowercase						,		_lowercase			):
																				common_inputs["past_key_values"].append((torch.zeros(_lowercase			), torch.zeros(_lowercase			))			)
								return common_inputs






		def 				UpperCAmelCase (    self						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE = -1						,		_SCREAMING_SNAKE_CASE = -1						,		_SCREAMING_SNAKE_CASE = False						,		_SCREAMING_SNAKE_CASE = None						,		):
								_UpperCAmelCase      =			self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
								    _lowercase						,		_lowercase						,		_lowercase						,		_lowercase						,		_lowercase			)

								if self.use_past:
														if not is_torch_available():
																				raise ValueError("""Cannot generate dummy past_keys inputs without PyTorch installed."""			)
														else:
																				import torch
														_UpperCAmelCase      =			common_inputs["""input_ids"""].shape
														# Not using the same length for past_key_values
														_UpperCAmelCase      =			seqlen + 2
														_UpperCAmelCase      =			self.num_layers
														_UpperCAmelCase      =			self.num_attention_heads
														_UpperCAmelCase      =			(
														    batch,
														    num_encoder_attention_heads,
														    past_key_values_length,
														    self._config.hidden_size // num_encoder_attention_heads,
														)

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






		def 				UpperCAmelCase (    self						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE = -1						,		_SCREAMING_SNAKE_CASE = -1						,		_SCREAMING_SNAKE_CASE = False						,		_SCREAMING_SNAKE_CASE = None						,		):
								_UpperCAmelCase      =			compute_effective_axis_dimension(
								    _lowercase						,		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      =			tokenizer.num_special_tokens_to_add(_lowercase			)
								_UpperCAmelCase      =			compute_effective_axis_dimension(
								    _lowercase						,		fixed_dimension=OnnxConfig.default_fixed_sequence						,		num_token_to_add=_lowercase			)

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






		def 				UpperCAmelCase (    self						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE = -1						,		_SCREAMING_SNAKE_CASE = -1						,		_SCREAMING_SNAKE_CASE = False						,		_SCREAMING_SNAKE_CASE = None						,		):

								if self.task in ["default", "seq2seq-lm"]:
														_UpperCAmelCase      =			self._generate_dummy_inputs_for_default_and_seqaseq_lm(
														    _lowercase						,		batch_size=_lowercase						,		seq_length=_lowercase						,		is_pair=_lowercase						,		framework=_lowercase			)

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

								return common_inputs






		def 				UpperCAmelCase (    self						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE			):

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





def        _SCREAMING_SNAKE_CASE      (		snake_case			,     snake_case       )						->				bool:
						_UpperCAmelCase      =			len(snake_case       ) + 1
						_UpperCAmelCase      =			len(snake_case       ) + 1

						# dp is a 2d matrix where dp[i][j] denotes whether prefix string of
						# length i of input_string matches with prefix string of length j of
						# given pattern.
						# "dp" stands for dynamic programming.
						_UpperCAmelCase      =			[[0 for i in range(snake_case       )] for j in range(snake_case       )]

						# since string of zero length match pattern of zero length
						_UpperCAmelCase      =			1

						# since pattern of zero length will never match with string of non-zero length
						for i in range(1			,     snake_case       ):
												_UpperCAmelCase      =			0

						# since string of zero length will match with pattern where there
						# is at least one * alternatively
						for j in range(1			,     snake_case       ):
												_UpperCAmelCase      =			dp[0][j - 2] if pattern[j - 1] == """*""" else 0

						# now using bottom-up approach to find for all remaining lengths
						for i in range(1			,     snake_case       ):
												for j in range(1			,     snake_case       ):
																		if input_string[i - 1] == pattern[j - 1] or pattern[j - 1] == ".":
																								_UpperCAmelCase      =			dp[i - 1][j - 1]

																		elif pattern[j - 1] == "*":
																								if dp[i][j - 2] == 1:
																														_UpperCAmelCase      =			1
																								elif pattern[j - 2] in (input_string[i - 1], "."):
																														_UpperCAmelCase      =			dp[i - 1][j]
																								else:
																														_UpperCAmelCase      =			0
																		else:
																								_UpperCAmelCase      =			0

						return bool(dp[-1][-1]       )


if __name__ == "__main__":
				import doctest

				doctest.testmod()
				# inputing the strings
				# input_string = input("input a string :")
				# pattern = input("input a pattern :")

				a													=			"aab"
				a													=			"c*a*b"

				# using function to check whether given string matches the given pattern
				if match_pattern(input_string, pattern):
								print(F'{input_string} matches the given pattern {pattern}')
				else:
								print(F'{input_string} does not match with the given pattern {pattern}')