



"""simple docstring"""







from __future__ import annotations

from typing import Generic, TypeVar

UpperCAmelCase:   str       			=							TypeVar("""T""")





class    UpperCamelCase     (			snake_case    ):




	"""simple docstring"""
	pass







class    UpperCamelCase     (			snake_case    ):




	"""simple docstring"""
	pass







class    UpperCamelCase     (			Generic[T]    ):




	"""simple docstring"""






	def __init__(			self							,UpperCAmelCase_ = 10					):
			_lowercase							:      list[T]			=					[]
			_lowercase							:      Optional[int]			=					limit






	def __bool__(			self					):
			return bool(self.stack					)






	def __str__(			self					):
			return str(self.stack					)






	def 					lowerCamelCase__ (			self							,UpperCAmelCase_					):
			if len(self.stack					) >= self.limit:
					raise StackOverflowError
			self.stack.append(UpperCAmelCase_					)






	def 					lowerCamelCase__ (			self					):
			if not self.stack:
					raise StackUnderflowError
			return self.stack.pop()






	def 					lowerCamelCase__ (			self					):
			if not self.stack:
					raise StackUnderflowError
			return self.stack[-1]






	def 					lowerCamelCase__ (			self					):
			return not bool(self.stack					)






	def 					lowerCamelCase__ (			self					):
			return self.size() == self.limit






	def 					lowerCamelCase__ (			self					):
			return len(self.stack					)






	def __contains__(			self							,UpperCAmelCase_					):
			return item in self.stack







def       __SCREAMING_SNAKE_CASE      (   ):
		_lowercase							:      Stack[int]			=					Stack(10	)
		assert bool(__UpperCAmelCase	) is False
		assert stack.is_empty() is True
		assert stack.is_full() is False
		assert str(__UpperCAmelCase	) == "[]"

		try:
				_lowercase							:      Optional[Any]			=					stack.pop()
				raise AssertionError  # This should not happen
		except StackUnderflowError:
				assert True  # This should happen

		try:
				_lowercase							:      Union[str, Any]			=					stack.peek()
				raise AssertionError  # This should not happen
		except StackUnderflowError:
				assert True  # This should happen

		for i in range(10	):
				assert stack.size() == i
				stack.push(__UpperCAmelCase	)

		assert bool(__UpperCAmelCase	)
		assert not stack.is_empty()
		assert stack.is_full()
		assert str(__UpperCAmelCase	) == str(list(range(10	)	)	)
		assert stack.pop() == 9
		assert stack.peek() == 8

		stack.push(100	)
		assert str(__UpperCAmelCase	) == str([0, 1, 2, 3, 4, 5, 6, 7, 8, 100]	)

		try:
				stack.push(200	)
				raise AssertionError  # This should not happen
		except StackOverflowError:
				assert True  # This should happen

		assert not stack.is_empty()
		assert stack.size() == 10

		assert 5 in stack
		assert 55 not in stack


if __name__ == "__main__":
		test_stack()









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			 =						logging.get_logger(__name__)

_lowerCamelCase			 =						{
    '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 			a     (   _A			):

     '''simple docstring'''





     lowerCAmelCase				:       List[Any]   		=  'blenderbot'
     lowerCAmelCase				:       int   		=  ['past_key_values']
     lowerCAmelCase				:       int   		=  {'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}
     def __init__(	self   :			int					,    __snake_case   :			Optional[Any]=80_08					,    __snake_case   :			List[str]=1_28					,    __snake_case   :			List[str]=2					,    __snake_case   :			Tuple=1_02_40					,    __snake_case   :			List[Any]=32					,    __snake_case   :			int=24					,    __snake_case   :			str=1_02_40					,    __snake_case   :			Optional[int]=32					,    __snake_case   :			Dict=0.0					,    __snake_case   :			int=0.0					,    __snake_case   :			str=True					,    __snake_case   :			Dict=True					,    __snake_case   :			Dict="gelu"					,    __snake_case   :			List[str]=25_60					,    __snake_case   :			List[Any]=0.1					,    __snake_case   :			str=0.0					,    __snake_case   :			Dict=0.0					,    __snake_case   :			Tuple=0.02					,    __snake_case   :			Any=1					,    __snake_case   :			int=False					,    __snake_case   :			Any=0					,    __snake_case   :			Optional[Any]=1					,    __snake_case   :			Optional[int]=2					,    __snake_case   :			List[Any]=3					,    __snake_case   :			int=2					,    **__snake_case   :			Tuple					,    ):
         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=__snake_case					,    bos_token_id=__snake_case					,    eos_token_id=__snake_case					,    is_encoder_decoder=__snake_case					,    decoder_start_token_id=__snake_case					,    encoder_no_repeat_ngram_size=__snake_case					,    forced_eos_token_id=__snake_case					,    **__snake_case					,    )



class 			a     (   _A			):

     '''simple docstring'''





     @property
     def   lowerCamelCase_   (	self   :			Any				):
         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_(__snake_case					,    direction='''inputs'''				)
         elif self.task == "causal-lm":
             UpperCAmelCase_							 =     OrderedDict(
                 [
                     ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
                     ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
                 ]				)
             if self.use_past:
                 UpperCAmelCase_ ,							UpperCAmelCase_							 =     self.num_layers
                 for i in range(__snake_case				):
                     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
     # Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.outputs
     def   lowerCamelCase_   (	self   :			Optional[Any]				):
         if self.task in ["default", "seq2seq-lm"]:
             UpperCAmelCase_							 =     super().outputs
         else:
             UpperCAmelCase_							 =     super(__snake_case					,    self				).outputs
             if self.use_past:
                 UpperCAmelCase_ ,							UpperCAmelCase_							 =     self.num_layers
                 for i in range(__snake_case				):
                     UpperCAmelCase_							 =     {0: '''batch''', 2: '''past_sequence + sequence'''}
                     UpperCAmelCase_							 =     {0: '''batch''', 2: '''past_sequence + sequence'''}
         return common_outputs
     def   lowerCamelCase_   (	self   :			Optional[Any]					,    __snake_case   :			PreTrainedTokenizer					,    __snake_case   :			int = -1					,    __snake_case   :			int = -1					,    __snake_case   :			bool = False					,    __snake_case   :			Optional[TensorType] = None					,    ):
         UpperCAmelCase_							 =     self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
             __snake_case					,    __snake_case					,    __snake_case					,    __snake_case					,    __snake_case				)
         # Generate decoder inputs
         UpperCAmelCase_							 =     seq_length if not self.use_past else 1
         UpperCAmelCase_							 =     self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
             __snake_case					,    __snake_case					,    __snake_case					,    __snake_case					,    __snake_case				)
         UpperCAmelCase_							 =     {F'decoder_{name}': tensor for name, tensor in decoder_inputs.items()}
         UpperCAmelCase_							 =     dict(**__snake_case					,    **__snake_case				)

         if self.use_past:
             if not is_torch_available():
                 raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.'''				)
             else:
                 import torch
             UpperCAmelCase_ ,							UpperCAmelCase_							 =     common_inputs['''input_ids'''].shape
             UpperCAmelCase_							 =     common_inputs['''decoder_input_ids'''].shape[1]
             UpperCAmelCase_ ,							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
             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(__snake_case					,    __snake_case				)]					,    dim=1				)
             UpperCAmelCase_							 =     []
             UpperCAmelCase_ ,							UpperCAmelCase_							 =     self.num_layers

             for _ in range(__snake_case				):
                 common_inputs["past_key_values"].append(
                     (
                         torch.zeros(__snake_case				),
                         torch.zeros(__snake_case				),
                         torch.zeros(__snake_case				),
                         torch.zeros(__snake_case				),
                     )				)
         return common_inputs
     def   lowerCamelCase_   (	self   :			Optional[Any]					,    __snake_case   :			PreTrainedTokenizer					,    __snake_case   :			int = -1					,    __snake_case   :			int = -1					,    __snake_case   :			bool = False					,    __snake_case   :			Optional[TensorType] = None					,    ):
         UpperCAmelCase_							 =     self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
             __snake_case					,    __snake_case					,    __snake_case					,    __snake_case					,    __snake_case				)

         if self.use_past:
             if not is_torch_available():
                 raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.'''				)
             else:
                 import torch
             UpperCAmelCase_ ,							UpperCAmelCase_							 =     common_inputs['''input_ids'''].shape
             UpperCAmelCase_							 =     seqlen
             UpperCAmelCase_ ,							UpperCAmelCase_							 =     self.num_layers
             UpperCAmelCase_ ,							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(__snake_case					,    __snake_case					,    dtype=__snake_case				)]					,    dim=1				)
             UpperCAmelCase_							 =     [
                 (torch.zeros(__snake_case				), torch.zeros(__snake_case				)) for _ in range(__snake_case				)
             ]
         return common_inputs
     def   lowerCamelCase_   (	self   :			Any					,    __snake_case   :			PreTrainedTokenizer					,    __snake_case   :			int = -1					,    __snake_case   :			int = -1					,    __snake_case   :			bool = False					,    __snake_case   :			Optional[TensorType] = None					,    ):
         # 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
         UpperCAmelCase_							 =     compute_effective_axis_dimension(
             __snake_case					,    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(__snake_case				)
         UpperCAmelCase_							 =     compute_effective_axis_dimension(
             __snake_case					,    fixed_dimension=OnnxConfig.default_fixed_sequence					,    num_token_to_add=__snake_case				)

         # Generate dummy inputs according to compute batch and sequence
         UpperCAmelCase_							 =     [''' '''.join([tokenizer.unk_token]				) * seq_length] * batch_size
         UpperCAmelCase_							 =     dict(tokenizer(__snake_case					,    return_tensors=__snake_case				)				)
         return common_inputs
     def   lowerCamelCase_   (	self   :			List[Any]					,    __snake_case   :			PreTrainedTokenizer					,    __snake_case   :			int = -1					,    __snake_case   :			int = -1					,    __snake_case   :			bool = False					,    __snake_case   :			Optional[TensorType] = None					,    ):
         if self.task in ["default", "seq2seq-lm"]:
             UpperCAmelCase_							 =     self._generate_dummy_inputs_for_default_and_seqaseq_lm(
                 __snake_case					,    batch_size=__snake_case					,    seq_length=__snake_case					,    is_pair=__snake_case					,    framework=__snake_case				)

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

         return common_inputs
     def   lowerCamelCase_   (	self   :			Dict					,    __snake_case   :			Optional[int]					,    __snake_case   :			int					,    __snake_case   :			str					,    __snake_case   :			Optional[int]				):
         if self.task in ["default", "seq2seq-lm"]:
             UpperCAmelCase_							 =     super()._flatten_past_key_values_(__snake_case					,    __snake_case					,    __snake_case					,    __snake_case				)
         else:
             UpperCAmelCase_							 =     super(__snake_case					,    self				)._flatten_past_key_values_(
                 __snake_case					,    __snake_case					,    __snake_case					,    __snake_case				)




     def   lowerCamelCase_   (	self   :			Dict					,    __snake_case   :			Mapping[str, Mapping[int, str]]					,    __snake_case   :			str				):
         if direction not in ["inputs", "outputs"]:
             raise ValueError(F'direction must either be "inputs" or "outputs", but {direction} was given'				)

         UpperCAmelCase_							 =     '''past_key_values''' if direction == '''inputs''' else '''present'''
         UpperCAmelCase_ ,							UpperCAmelCase_							 =     self.num_layers

         UpperCAmelCase_							 =     '''past_encoder_sequence'''
         UpperCAmelCase_							 =     '''past_decoder_sequence''' if direction == '''inputs''' else '''past_decoder_sequence + sequence'''

         for i in range(__snake_case				):
             UpperCAmelCase_							 =     {0: '''batch''', 2: decoder_sequence}
             UpperCAmelCase_							 =     {0: '''batch''', 2: decoder_sequence}
             UpperCAmelCase_							 =     {0: '''batch''', 2: encoder_sequence}
             UpperCAmelCase_							 =     {0: '''batch''', 2: encoder_sequence}





