




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

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


_snake_case										=       logging.get_logger(__name__)

_snake_case										=       {
    'Helsinki-NLP/opus-mt-en-de': 'https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/config.json',
    # See all Marian models at https://huggingface.co/models?filter=marian
}







class 						lowercase							(   _lowerCamelCase		):
     _a							 =						"marian"
     _a							 =						["past_key_values"]
     _a							 =						{"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}

     def __init__(  self		,     _a=5_8101		,     _a=None		,     _a=1024		,     _a=12		,     _a=4096		,     _a=16		,     _a=12		,     _a=4096		,     _a=16		,     _a=0.0		,     _a=0.0		,     _a=True		,     _a=True		,     _a="gelu"		,     _a=1024		,     _a=0.1		,     _a=0.0		,     _a=0.0		,     _a=0.02		,     _a=5_8100		,     _a=False		,     _a=5_8100		,     _a=0		,     _a=0		,     _a=True		,     **_a		,     )						->							int:
           _A							:     Any			=						vocab_size
           _A							:     int			=						decoder_vocab_size or vocab_size
           _A							:     Optional[int]			=						max_position_embeddings
           _A							:     Tuple			=						d_model
           _A							:     Any			=						encoder_ffn_dim
           _A							:     List[str]			=						encoder_layers
           _A							:     Union[str, Any]			=						encoder_attention_heads
           _A							:     Tuple			=						decoder_ffn_dim
           _A							:     Optional[int]			=						decoder_layers
           _A							:     Any			=						decoder_attention_heads
           _A							:     Dict			=						dropout
           _A							:     Union[str, Any]			=						attention_dropout
           _A							:     Optional[Any]			=						activation_dropout
           _A							:     List[str]			=						activation_function
           _A							:     int			=						init_std
           _A							:     Union[str, Any]			=						encoder_layerdrop
           _A							:     Dict			=						decoder_layerdrop
           _A							:     List[Any]			=						use_cache
           _A							:     Optional[Any]			=						encoder_layers
           _A							:     Dict			=						scale_embedding  # scale factor will be sqrt(d_model) if True
           _A							:     Union[str, Any]			=						share_encoder_decoder_embeddings
           super().__init__(
               pad_token_id=lowercase_		,     eos_token_id=lowercase_		,     is_encoder_decoder=lowercase_		,     decoder_start_token_id=lowercase_		,     forced_eos_token_id=lowercase_		,     **lowercase_		,     )







class 						lowercase							(   _lowerCamelCase		):

     @property
     # Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.inputs
     def 					a__	(  self				)						->							Union[str, Any]:
           if self.task in ["default", "seq2seq-lm"]:
                 _A							:     Dict			=						OrderedDict(
                     [
                         ("""input_ids""", {0: """batch""", 1: """encoder_sequence"""}),
                         ("""attention_mask""", {0: """batch""", 1: """encoder_sequence"""}),
                     ]				)

                 if self.use_past:
                       _A							:     List[Any]			=						{0: """batch"""}
                       _A							:     Dict			=						{0: """batch""", 1: """past_decoder_sequence + sequence"""}
                 else:
                       _A							:     str			=						{0: """batch""", 1: """decoder_sequence"""}
                       _A							:     int			=						{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.
                 _A							:     List[Any]			=						OrderedDict(
                     [
                         ("""input_ids""", {0: """batch""", 1: """encoder_sequence"""}),
                         ("""attention_mask""", {0: """batch""", 1: """encoder_sequence"""}),
                     ]				)
                 if self.use_past:
                       _A			,   _A							:     List[str]			=						self.num_layers
                       for i in range(lowercase_				):
                             _A							:     List[Any]			=						{0: """batch""", 2: """past_sequence + sequence"""}
                             _A							:     int			=						{0: """batch""", 2: """past_sequence + sequence"""}
           else:
                 _A							:     int			=						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 					a__	(  self				)						->							Union[str, Any]:
           if self.task in ["default", "seq2seq-lm"]:
                 _A							:     Optional[Any]			=						super().outputs
           else:
                 _A							:     List[Any]			=						super(lowercase_		,     self				).outputs
                 if self.use_past:
                       _A			,   _A							:     List[str]			=						self.num_layers
                       for i in range(lowercase_				):
                             _A							:     List[Any]			=						{0: """batch""", 2: """past_sequence + sequence"""}
                             _A							:     int			=						{0: """batch""", 2: """past_sequence + sequence"""}
           return common_outputs

     def 					a__	(  self		,     _a		,     _a = -1		,     _a = -1		,     _a = False		,     _a = None		,     )						->							Dict:
           _A							:     Tuple			=						self._generate_dummy_inputs_for_encoder_and_decoder(
               lowercase_		,     lowercase_		,     lowercase_		,     lowercase_		,     lowercase_				)

           # Generate decoder inputs
           _A							:     List[str]			=						seq_length if not self.use_past else 1
           _A							:     Optional[Any]			=						self._generate_dummy_inputs_for_encoder_and_decoder(
               lowercase_		,     lowercase_		,     lowercase_		,     lowercase_		,     lowercase_				)
           _A							:     List[Any]			=						{F'''decoder_{name}''': tensor for name, tensor in decoder_inputs.items()}
           _A							:     str			=						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
                 _A			,   _A							:     str			=						common_inputs["""input_ids"""].shape
                 _A							:     List[str]			=						common_inputs["""decoder_input_ids"""].shape[1]
                 _A			,   _A							:     Union[str, Any]			=						self.num_attention_heads
                 _A							:     List[Any]			=						(
                     batch,
                     num_encoder_attention_heads,
                     encoder_seq_length,
                     self._config.hidden_size // num_encoder_attention_heads,
                 )
                 _A							:     str			=						decoder_seq_length + 3
                 _A							:     str			=						(
                     batch,
                     num_decoder_attention_heads,
                     decoder_past_length,
                     self._config.hidden_size // num_decoder_attention_heads,
                 )

                 _A							:     Tuple			=						torch.cat(
                     [common_inputs["""decoder_attention_mask"""], torch.ones(lowercase_		,     lowercase_				)]		,     dim=1				)

                 _A							:     List[str]			=						[]
                 # If the number of encoder and decoder layers are present in the model configuration, both are considered
                 _A			,   _A							:     Optional[Any]			=						self.num_layers
                 _A							:     List[str]			=						min(lowercase_		,     lowercase_				)
                 _A							:     int			=						max(lowercase_		,     lowercase_				) - min_num_layers
                 _A							:     str			=						"""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.
                 _A							:     List[str]			=						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 					a__	(  self		,     _a		,     _a = -1		,     _a = -1		,     _a = False		,     _a = None		,     )						->							int:
           _A							:     str			=						self._generate_dummy_inputs_for_encoder_and_decoder(
               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
                 _A			,   _A							:     Optional[int]			=						common_inputs["""input_ids"""].shape
                 # Not using the same length for past_key_values
                 _A							:     Any			=						seqlen + 2
                 _A			,   _A							:     List[str]			=						self.num_layers
                 _A			,   _A							:     int			=						self.num_attention_heads
                 _A							:     Dict			=						(
                     batch,
                     num_encoder_attention_heads,
                     past_key_values_length,
                     self._config.hidden_size // num_encoder_attention_heads,
                 )

                 _A							:     Union[str, Any]			=						common_inputs["""attention_mask"""].dtype
                 _A							:     List[Any]			=						torch.cat(
                     [common_inputs["""attention_mask"""], torch.ones(lowercase_		,     lowercase_		,     dtype=lowercase_				)]		,     dim=1				)
                 _A							:     List[Any]			=						[
                     (torch.zeros(lowercase_				), torch.zeros(lowercase_				)) for _ in range(lowercase_				)
                 ]
           return common_inputs

     def 					a__	(  self		,     _a		,     _a = -1		,     _a = -1		,     _a = False		,     _a = None		,     )						->							Dict:
           # 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
           _A							:     Any			=						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
           _A							:     Dict			=						tokenizer.num_special_tokens_to_add(lowercase_				)
           _A							:     str			=						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
           _A							:     Tuple			=						[""" """.join([tokenizer.unk_token]				) * seq_length] * batch_size
           _A							:     Optional[Any]			=						dict(tokenizer(lowercase_		,     return_tensors=lowercase_				)				)
           return common_inputs

     def 					a__	(  self		,     _a		,     _a = -1		,     _a = -1		,     _a = False		,     _a = None		,     )						->							Any:
           if self.task in ["default", "seq2seq-lm"]:
                 _A							:     List[str]			=						self._generate_dummy_inputs_for_default_and_seqaseq_lm(
                     lowercase_		,     batch_size=lowercase_		,     seq_length=lowercase_		,     is_pair=lowercase_		,     framework=lowercase_				)

           else:
                 _A							:     Union[str, Any]			=						self._generate_dummy_inputs_for_causal_lm(
                     lowercase_		,     batch_size=lowercase_		,     seq_length=lowercase_		,     is_pair=lowercase_		,     framework=lowercase_				)

           return common_inputs

     def 					a__	(  self		,     _a		,     _a		,     _a		,     _a				)						->							Tuple:
           if self.task in ["default", "seq2seq-lm"]:
                 _A							:     List[str]			=						super()._flatten_past_key_values_(lowercase_		,     lowercase_		,     lowercase_		,     lowercase_				)
           else:
                 _A							:     Any			=						super(lowercase_		,     self				)._flatten_past_key_values_(
                     lowercase_		,     lowercase_		,     lowercase_		,     lowercase_				)



     @property
     def 					a__	(  self				)						->							str:
           return 1e-4







'''simple docstring'''

from collections import defaultdict


def 		__magic_name__    (	__UpperCAmelCase	)		->					int:

					'''simple docstring'''

					snake_case_									=				1
					snake_case_									=				True
					for v in tree[start]:
										if v not in visited:
															ret += dfs(__UpperCAmelCase	)
					if ret % 2 == 0:
										cuts.append(__UpperCAmelCase	)
					return ret


def 		__magic_name__    (	)		->					Union[str, Any]:

					'''simple docstring'''

					dfs(1	)


if __name__ == "__main__":
							a		,a  :				Dict									=  10, 9
							a  :				Dict									=  defaultdict(list)
							a  :				dict[int, bool]									=  {}
							a  :				list[int]									=  []
							a  :				Tuple									=  0
							a  :				str									=  [(2, 1), (3, 1), (4, 3), (5, 2), (6, 1), (7, 2), (8, 6), (9, 8), (10, 8)]
							for u, v in edges:
														tree[u].append(v)
														tree[v].append(u)
							even_tree()
							print(len(cuts) - 1)

