




"""simple docstring"""





from collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import TensorType, is_torch_available, logging


_lowercase	      =    logging.get_logger(__name__)

_lowercase	      =    {
    """facebook/m2m100_418M""": """https://huggingface.co/facebook/m2m100_418M/resolve/main/config.json""",
    # See all M2M100 models at https://huggingface.co/models?filter=m2m_100
}

class       __lowercase       (			snake_case_	):


				'''simple docstring'''


				_lowerCAmelCase				="m2m_100"
				_lowerCAmelCase				=["past_key_values"]
				_lowerCAmelCase				={"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}

				def __init__(   self   :     Tuple      ,SCREAMING_SNAKE_CASE_   :     Union[str, Any]=1_2_8_1_1_2      ,SCREAMING_SNAKE_CASE_   :     Any=1_0_2_4      ,SCREAMING_SNAKE_CASE_   :     List[Any]=1_2      ,SCREAMING_SNAKE_CASE_   :     Any=4_0_9_6      ,SCREAMING_SNAKE_CASE_   :     List[Any]=1_6      ,SCREAMING_SNAKE_CASE_   :     List[Any]=1_2      ,SCREAMING_SNAKE_CASE_   :     int=4_0_9_6      ,SCREAMING_SNAKE_CASE_   :     str=1_6      ,SCREAMING_SNAKE_CASE_   :     Dict=0.05      ,SCREAMING_SNAKE_CASE_   :     int=0.05      ,SCREAMING_SNAKE_CASE_   :     Optional[int]=True      ,SCREAMING_SNAKE_CASE_   :     List[str]=True      ,SCREAMING_SNAKE_CASE_   :     int="relu"      ,SCREAMING_SNAKE_CASE_   :     Tuple=1_0_2_4      ,SCREAMING_SNAKE_CASE_   :     Any=0.1      ,SCREAMING_SNAKE_CASE_   :     Dict=0.1      ,SCREAMING_SNAKE_CASE_   :     int=0.0      ,SCREAMING_SNAKE_CASE_   :     Union[str, Any]=0.02      ,SCREAMING_SNAKE_CASE_   :     List[str]=2      ,SCREAMING_SNAKE_CASE_   :     int=True      ,SCREAMING_SNAKE_CASE_   :     List[Any]=1      ,SCREAMING_SNAKE_CASE_   :     List[str]=0      ,SCREAMING_SNAKE_CASE_   :     Any=2      ,**SCREAMING_SNAKE_CASE_   :     Tuple      ,):
										snake_case__       :					Any										=					vocab_size
										snake_case__       :					Tuple										=					max_position_embeddings
										snake_case__       :					str										=					d_model
										snake_case__       :					Optional[Any]										=					encoder_ffn_dim
										snake_case__       :					int										=					encoder_layers
										snake_case__       :					List[Any]										=					encoder_attention_heads
										snake_case__       :					List[Any]										=					decoder_ffn_dim
										snake_case__       :					Union[str, Any]										=					decoder_layers
										snake_case__       :					Tuple										=					decoder_attention_heads
										snake_case__       :					Union[str, Any]										=					dropout
										snake_case__       :					Union[str, Any]										=					attention_dropout
										snake_case__       :					Optional[Any]										=					activation_dropout
										snake_case__       :					Optional[Any]										=					activation_function
										snake_case__       :					Union[str, Any]										=					init_std
										snake_case__       :					str										=					encoder_layerdrop
										snake_case__       :					Optional[int]										=					decoder_layerdrop
										snake_case__       :					List[Any]										=					use_cache
										snake_case__       :					Tuple										=					encoder_layers
										snake_case__       :					Dict										=					scale_embedding  # scale factor will be sqrt(d_model) if True

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







class       __lowercase       (			snake_case_	):


				'''simple docstring'''


				@property
				def       _lowerCamelCase					(   self   :     Union[str, Any]      ):
										snake_case__       :					int										=					OrderedDict(
										    [
										        ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
										        ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
										    ]      )

										if self.use_past:
																snake_case__       :					int										=					{0: '''batch'''}
																snake_case__       :					Optional[int]										=					{0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
										else:
																snake_case__       :					Dict										=					{0: '''batch''', 1: '''decoder_sequence'''}
																snake_case__       :					int										=					{0: '''batch''', 1: '''decoder_sequence'''}

										if self.use_past:
																self.fill_with_past_key_values_(SCREAMING_SNAKE_CASE_      ,direction='''inputs'''      )
										return common_inputs

				def       _lowerCamelCase					(   self   :     int      ,SCREAMING_SNAKE_CASE_   :     PreTrainedTokenizer      ,SCREAMING_SNAKE_CASE_   :     int = -1      ,SCREAMING_SNAKE_CASE_   :     int = -1      ,SCREAMING_SNAKE_CASE_   :     bool = False      ,SCREAMING_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
										snake_case__       :					Dict										=					compute_effective_axis_dimension(
										    SCREAMING_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
										snake_case__       :					int										=					tokenizer.num_special_tokens_to_add(SCREAMING_SNAKE_CASE_      )
										snake_case__       :					Tuple										=					compute_effective_axis_dimension(
										    SCREAMING_SNAKE_CASE_      ,fixed_dimension=OnnxConfig.default_fixed_sequence      ,num_token_to_add=SCREAMING_SNAKE_CASE_      )

										# Generate dummy inputs according to compute batch and sequence
										snake_case__       :					List[Any]										=					[''' '''.join([tokenizer.unk_token]      ) * seq_length] * batch_size
										snake_case__       :					Union[str, Any]										=					dict(tokenizer(SCREAMING_SNAKE_CASE_      ,return_tensors=SCREAMING_SNAKE_CASE_      )      )
										return common_inputs

				def       _lowerCamelCase					(   self   :     Optional[Any]      ,SCREAMING_SNAKE_CASE_   :     PreTrainedTokenizer      ,SCREAMING_SNAKE_CASE_   :     int = -1      ,SCREAMING_SNAKE_CASE_   :     int = -1      ,SCREAMING_SNAKE_CASE_   :     bool = False      ,SCREAMING_SNAKE_CASE_   :     Optional[TensorType] = None      ,):
										snake_case__       :					int										=					self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
										    SCREAMING_SNAKE_CASE_      ,SCREAMING_SNAKE_CASE_      ,SCREAMING_SNAKE_CASE_      ,SCREAMING_SNAKE_CASE_      ,SCREAMING_SNAKE_CASE_      )

										# Generate decoder inputs
										snake_case__       :					Dict										=					seq_length if not self.use_past else 1
										snake_case__       :					List[Any]										=					self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
										    SCREAMING_SNAKE_CASE_      ,SCREAMING_SNAKE_CASE_      ,SCREAMING_SNAKE_CASE_      ,SCREAMING_SNAKE_CASE_      ,SCREAMING_SNAKE_CASE_      )
										snake_case__       :					List[str]										=					{F'''decoder_{name}''': tensor for name, tensor in decoder_inputs.items()}
										snake_case__       :					Dict										=					dict(**SCREAMING_SNAKE_CASE_      ,**SCREAMING_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
																snake_case__			,	snake_case__       :					Any										=					common_inputs['''input_ids'''].shape
																snake_case__       :					str										=					common_inputs['''decoder_input_ids'''].shape[1]
																snake_case__			,	snake_case__       :					str										=					self.num_attention_heads
																snake_case__       :					Dict										=					(
																    batch,
																    num_encoder_attention_heads,
																    encoder_seq_length,
																    self._config.hidden_size // num_encoder_attention_heads,
																)
																snake_case__       :					Dict										=					decoder_seq_length + 3
																snake_case__       :					List[Any]										=					(
																    batch,
																    num_decoder_attention_heads,
																    decoder_past_length,
																    self._config.hidden_size // num_decoder_attention_heads,
																)

																snake_case__       :					Dict										=					torch.cat(
																    [common_inputs['''decoder_attention_mask'''], torch.ones(SCREAMING_SNAKE_CASE_      ,SCREAMING_SNAKE_CASE_      )]      ,dim=1      )

																snake_case__       :					Any										=					[]
																# If the number of encoder and decoder layers are present in the model configuration, both are considered
																snake_case__			,	snake_case__       :					Any										=					self.num_layers
																snake_case__       :					int										=					min(SCREAMING_SNAKE_CASE_      ,SCREAMING_SNAKE_CASE_      )
																snake_case__       :					Tuple										=					max(SCREAMING_SNAKE_CASE_      ,SCREAMING_SNAKE_CASE_      ) - min_num_layers
																snake_case__       :					List[str]										=					'''encoder''' if num_encoder_layers > num_decoder_layers else '''decoder'''

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





				_lowerCAmelCase				=_generate_dummy_inputs_for_default_and_seqaseq_lm








"""simple docstring"""





def 		lowerCAmelCase							( lowerCamelCase      ,			lowerCamelCase		):
						'''simple docstring'''


						snake_case__       :					Any										=					len(lowerCamelCase		)
						# Create a stack and push index of fist element to it
						snake_case__       :					str										=					[]
						st.append(0		)

						# Span value of first element is always 1
						snake_case__       :					Union[str, Any]										=					1

						# Calculate span values for rest of the elements
						for i in range(1      ,			lowerCamelCase		):
												# Pop elements from stack while stack is not
												# empty and top of stack is smaller than price[i]
												while len(lowerCamelCase		) > 0 and price[st[0]] <= price[i]:
																		st.pop()

												# If stack becomes empty, then price[i] is greater
												# than all elements on left of it, i.e. price[0],
												# price[1], ..price[i-1]. Else the price[i]  is
												# greater than elements after top of stack
												snake_case__       :					Dict										=					i + 1 if len(lowerCamelCase		) <= 0 else (i - st[0])

												# Push this element to stack
												st.append(lowerCamelCase		)

def 		lowerCAmelCase							( lowerCamelCase      ,			lowerCamelCase		):
						'''simple docstring'''


						for i in range(0      ,			lowerCamelCase		):
												print(arr[i]      ,			end=''' '''		)


# Driver program to test above function
_lowercase	      =    [10, 4, 5, 90, 1_20, 80]
_lowercase	      =    [0 for i in range(len(price) + 1)]

# Fill the span values in array S[]
calculation_span(price, S)

print_array(S, len(price))


