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


UpperCAmelCase							=       logging.get_logger(__name__)

UpperCAmelCase							=       {
    '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 					snake_case__       (				UpperCAmelCase_							):
   _SCREAMING_SNAKE_CASE	: Optional[int]						    =      "m2m_100"
   _SCREAMING_SNAKE_CASE	: Dict						    =      ["past_key_values"]
   _SCREAMING_SNAKE_CASE	: Dict						    =      {"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}



   def __init__(				self   :   List[str]			,	A__   :   Tuple=12_81_12			,	A__   :   Tuple=10_24			,	A__   :   Any=12			,	A__   :   int=40_96			,	A__   :   List[str]=16			,	A__   :   List[Any]=12			,	A__   :   Dict=40_96			,	A__   :   int=16			,	A__   :   int=0.05			,	A__   :   Optional[int]=0.05			,	A__   :   List[str]=True			,	A__   :   Any=True			,	A__   :   Optional[int]="relu"			,	A__   :   List[str]=10_24			,	A__   :   Union[str, Any]=0.1			,	A__   :   Optional[Any]=0.1			,	A__   :   Optional[int]=0.0			,	A__   :   Union[str, Any]=0.02			,	A__   :   int=2			,	A__   :   Dict=True			,	A__   :   int=1			,	A__   :   Optional[Any]=0			,	A__   :   List[Any]=2			,	**A__   :   List[Any]			,	)							->     Optional[Any]:




         '''simple docstring'''




         snake_case_     :			Optional[Any]       =       vocab_size
         snake_case_     :			str       =       max_position_embeddings
         snake_case_     :			Optional[Any]       =       d_model
         snake_case_     :			Any       =       encoder_ffn_dim
         snake_case_     :			Any       =       encoder_layers
         snake_case_     :			str       =       encoder_attention_heads
         snake_case_     :			Optional[Any]       =       decoder_ffn_dim
         snake_case_     :			str       =       decoder_layers
         snake_case_     :			Any       =       decoder_attention_heads
         snake_case_     :			Any       =       dropout
         snake_case_     :			Optional[int]       =       attention_dropout
         snake_case_     :			Any       =       activation_dropout
         snake_case_     :			str       =       activation_function
         snake_case_     :			List[str]       =       init_std
         snake_case_     :			Optional[int]       =       encoder_layerdrop
         snake_case_     :			List[Any]       =       decoder_layerdrop
         snake_case_     :			Any       =       use_cache
         snake_case_     :			Any       =       encoder_layers
         snake_case_     :			str       =       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			,	**__UpperCamelCase			,	)



class 					snake_case__       (				UpperCAmelCase_							):



   @property
   def 					UpperCAmelCase__       (				self   :   Any   )							->     str:




         '''simple docstring'''




         snake_case_     :			Optional[int]       =       OrderedDict(
             [
                 ("input_ids", {0: "batch", 1: "encoder_sequence"}),
                 ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
             ]   )

         if self.use_past:
               snake_case_     :			Dict       =       {0: "batch"}
               snake_case_     :			List[Any]       =       {0: "batch", 1: "past_decoder_sequence + sequence"}
         else:
               snake_case_     :			List[str]       =       {0: "batch", 1: "decoder_sequence"}
               snake_case_     :			Optional[Any]       =       {0: "batch", 1: "decoder_sequence"}

         if self.use_past:
               self.fill_with_past_key_values_(__UpperCamelCase			,	direction="inputs"   )
         return common_inputs



   def 					UpperCAmelCase__       (				self   :   List[Any]			,	A__   :   List[Any]			,	A__   :   Dict = -1			,	A__   :   List[Any] = -1			,	A__   :   List[str] = False			,	A__   :   int = None			,	)							->     List[str]:




         '''simple docstring'''




         snake_case_     :			Union[str, Any]       =       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
         snake_case_     :			Optional[int]       =       tokenizer.num_special_tokens_to_add(__UpperCamelCase   )
         snake_case_     :			Any       =       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
         snake_case_     :			Optional[Any]       =       [" ".join([tokenizer.unk_token]   ) * seq_length] * batch_size
         snake_case_     :			int       =       dict(tokenizer(__UpperCamelCase			,	return_tensors=__UpperCamelCase   )   )
         return common_inputs



   def 					UpperCAmelCase__       (				self   :   Union[str, Any]			,	A__   :   Dict			,	A__   :   Union[str, Any] = -1			,	A__   :   Dict = -1			,	A__   :   Optional[Any] = False			,	A__   :   Dict = None			,	)							->     List[Any]:




         '''simple docstring'''




         snake_case_     :			Optional[int]       =       self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
             __UpperCamelCase			,	__UpperCamelCase			,	__UpperCamelCase			,	__UpperCamelCase			,	__UpperCamelCase   )

         # Generate decoder inputs
         snake_case_     :			List[str]       =       seq_length if not self.use_past else 1
         snake_case_     :			int       =       self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
             __UpperCamelCase			,	__UpperCamelCase			,	__UpperCamelCase			,	__UpperCamelCase			,	__UpperCamelCase   )
         snake_case_     :			Tuple       =       {f"decoder_{name}": tensor for name, tensor in decoder_inputs.items()}
         snake_case_     :			Dict       =       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
               snake_case_  ,snake_case_     :			List[str]       =       common_inputs["input_ids"].shape
               snake_case_     :			Union[str, Any]       =       common_inputs["decoder_input_ids"].shape[1]
               snake_case_  ,snake_case_     :			List[Any]       =       self.num_attention_heads
               snake_case_     :			List[Any]       =       (
                   batch,
                   num_encoder_attention_heads,
                   encoder_seq_length,
                   self._config.hidden_size // num_encoder_attention_heads,
               )
               snake_case_     :			List[Any]       =       decoder_seq_length + 3
               snake_case_     :			Any       =       (
                   batch,
                   num_decoder_attention_heads,
                   decoder_past_length,
                   self._config.hidden_size // num_decoder_attention_heads,
               )

               snake_case_     :			Union[str, Any]       =       torch.cat(
                   [common_inputs["decoder_attention_mask"], torch.ones(__UpperCamelCase			,	__UpperCamelCase   )]			,	dim=1   )

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

               for _ in range(__UpperCamelCase   ):
                     common_inputs["past_key_values"].append(
                         (
                             torch.zeros(__UpperCamelCase   ),
                             torch.zeros(__UpperCamelCase   ),
                             torch.zeros(__UpperCamelCase   ),
                             torch.zeros(__UpperCamelCase   ),
                         )   )
               # TODO: test this.
               snake_case_     :			Optional[Any]       =       encoder_shape if remaining_side_name == "encoder" else decoder_shape
               for _ in range(__UpperCamelCase			,	__UpperCamelCase   ):
                     common_inputs["past_key_values"].append((torch.zeros(__UpperCamelCase   ), torch.zeros(__UpperCamelCase   ))   )
         return common_inputs
   _SCREAMING_SNAKE_CASE	: Tuple						    =      _generate_dummy_inputs_for_default_and_seqaseq_lm




import math



class 					snake_case__       :



   def __init__(				self   :   int			,	A__   :   str   )							->     int:




         '''simple docstring'''




         snake_case_     :			Tuple       =       len(A__   )
         snake_case_     :			Union[str, Any]       =       [0] * (
             4 * self.N
         )  # approximate the overall size of segment tree with array N
         self.build(1			,	0			,	self.N - 1   )



   def 					UpperCAmelCase__       (				self   :   Optional[Any]			,	A__   :   Tuple   )							->     Optional[int]:




         '''simple docstring'''




         return idx * 2



   def 					UpperCAmelCase__       (				self   :   Union[str, Any]			,	A__   :   List[str]   )							->     Dict:




         '''simple docstring'''




         return idx * 2 + 1



   def 					UpperCAmelCase__       (				self   :   str			,	A__   :   Any			,	A__   :   Optional[int]			,	A__   :   Tuple   )							->     List[str]:  # noqa: E741




         '''simple docstring'''




         if l == r:
               snake_case_     :			str       =       A[l]
         else:
               snake_case_     :			List[str]       =       (l + r) // 2
               self.build(self.left(A__   )			,	A__			,	A__   )
               self.build(self.right(A__   )			,	mid + 1			,	A__   )
               snake_case_     :			Union[str, Any]       =       max(self.st[self.left(A__   )]			,	self.st[self.right(A__   )]   )



   def 					UpperCAmelCase__       (				self   :   Optional[Any]			,	A__   :   List[str]			,	A__   :   Union[str, Any]			,	A__   :   str   )							->     int:




         '''simple docstring'''




         return self.update_recursive(1			,	0			,	self.N - 1			,	a - 1			,	b - 1			,	A__   )



   def 					UpperCAmelCase__       (				self   :   Optional[int]			,	A__   :   Optional[Any]			,	A__   :   List[str]			,	A__   :   Optional[Any]			,	A__   :   Union[str, Any]			,	A__   :   List[Any]			,	A__   :   str   )							->     Any:  # noqa: E741




         '''simple docstring'''




         if r < a or l > b:
               return True
         if l == r:
               snake_case_     :			List[str]       =       val
               return True
         snake_case_     :			Dict       =       (l + r) // 2
         self.update_recursive(self.left(A__   )			,	A__			,	A__			,	A__			,	A__			,	A__   )
         self.update_recursive(self.right(A__   )			,	mid + 1			,	A__			,	A__			,	A__			,	A__   )
         snake_case_     :			Tuple       =       max(self.st[self.left(A__   )]			,	self.st[self.right(A__   )]   )
         return True



   def 					UpperCAmelCase__       (				self   :   Optional[int]			,	A__   :   List[Any]			,	A__   :   Optional[Any]   )							->     str:




         '''simple docstring'''




         return self.query_recursive(1			,	0			,	self.N - 1			,	a - 1			,	b - 1   )



   def 					UpperCAmelCase__       (				self   :   Union[str, Any]			,	A__   :   Any			,	A__   :   int			,	A__   :   Tuple			,	A__   :   Tuple			,	A__   :   Dict   )							->     str:  # noqa: E741




         '''simple docstring'''




         if r < a or l > b:
               return -math.inf
         if l >= a and r <= b:
               return self.st[idx]
         snake_case_     :			Dict       =       (l + r) // 2
         snake_case_     :			Optional[Any]       =       self.query_recursive(self.left(A__   )			,	A__			,	A__			,	A__			,	A__   )
         snake_case_     :			Any       =       self.query_recursive(self.right(A__   )			,	mid + 1			,	A__			,	A__			,	A__   )
         return max(A__			,	A__   )



   def 					UpperCAmelCase__       (				self   :   Dict   )							->     Optional[Any]:




         '''simple docstring'''




         snake_case_     :			int       =       []
         for i in range(1			,	N + 1   ):
               show_list += [self.query(A__			,	A__   )]
         print(A__   )


if __name__ == "__main__":
     UpperCAmelCase							=       [1, 2, -4, 7, 3, -5, 6, 1_1, -2_0, 9, 1_4, 1_5, 5, 2, -8]
     UpperCAmelCase							=       1_5
     UpperCAmelCase							=       SegmentTree(A)
     print(segt.query(4, 6))
     print(segt.query(7, 1_1))
     print(segt.query(7, 1_2))
     segt.update(1, 3, 1_1_1)
     print(segt.query(1, 1_5))
     segt.update(7, 8, 2_3_5)
     segt.show_data()



