



"""simple docstring"""





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


lowercase  							=					logging.get_logger(__name__)

lowercase  							=					{
    '''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__			(      A    ):
       '''simple docstring'''







       _UpperCAmelCase      				=					'''marian'''
       _UpperCAmelCase      				=					['''past_key_values''']
       _UpperCAmelCase      				=					{'''num_attention_heads''': '''encoder_attention_heads''', '''hidden_size''': '''d_model'''}






       def __init__(  self	,       snake_case=58101	,       snake_case=None	,       snake_case=1024	,       snake_case=12	,       snake_case=4096	,       snake_case=16	,       snake_case=12	,       snake_case=4096	,       snake_case=16	,       snake_case=0.0	,       snake_case=0.0	,       snake_case=True	,       snake_case=True	,       snake_case="gelu"	,       snake_case=1024	,       snake_case=0.1	,       snake_case=0.0	,       snake_case=0.0	,       snake_case=0.02	,       snake_case=58100	,       snake_case=False	,       snake_case=58100	,       snake_case=0	,       snake_case=0	,       snake_case=True	,       **snake_case	,       )			->							Dict:
         _UpperCAmelCase							 =		vocab_size
         _UpperCAmelCase							 =		decoder_vocab_size or 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
         _UpperCAmelCase							 =		share_encoder_decoder_embeddings
         super().__init__(
             pad_token_id=snake_case	,       eos_token_id=snake_case	,       is_encoder_decoder=snake_case	,       decoder_start_token_id=snake_case	,       forced_eos_token_id=snake_case	,       **snake_case	,       )







class 			lowercase__			(      A    ):
       '''simple docstring'''







       @property
       # Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.inputs
       def        lowerCamelCase_   (  self						)			->							Mapping[str, Mapping[int, str]]:
         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":
           # 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 ,					_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						)			->							Mapping[str, Mapping[int, str]]:
         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	,       snake_case	,       snake_case = -1	,       snake_case = -1	,       snake_case = False	,       snake_case = None	,       )			->							Mapping[str, Any]:
         _UpperCAmelCase							 =		self._generate_dummy_inputs_for_encoder_and_decoder(
             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_encoder_and_decoder(
             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 + 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(snake_case	,       snake_case						)]	,       dim=1						)

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

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






       def        lowerCamelCase_   (  self	,       snake_case	,       snake_case = -1	,       snake_case = -1	,       snake_case = False	,       snake_case = None	,       )			->							Mapping[str, Any]:
         _UpperCAmelCase							 =		self._generate_dummy_inputs_for_encoder_and_decoder(
             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
           # Not using the same length for past_key_values
           _UpperCAmelCase							 =		seqlen + 2
           _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	,       snake_case	,       snake_case = -1	,       snake_case = -1	,       snake_case = False	,       snake_case = None	,       )			->							Mapping[str, Any]:
         # 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	,       snake_case	,       snake_case = -1	,       snake_case = -1	,       snake_case = False	,       snake_case = None	,       )			->							Mapping[str, Any]:
         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						)

         else:
           _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						)

         return common_inputs






       def        lowerCamelCase_   (  self	,       snake_case	,       snake_case	,       snake_case	,       snake_case						)			->							str:
         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						)






       @property
       def        lowerCamelCase_   (  self						)			->							float:
         return 1E-4







"""simple docstring"""





from __future__ import annotations

from cmath import sqrt




def 	UpperCAmelCase     (		A	:						int     ,				A	:						int     ,				A	:						int       ):

		'''simple docstring'''


		if a == 0:
				raise ValueError('Coefficient \'a\' must not be zero.'       )
		_UpperCAmelCase							 =		b * b - 4 * a * c

		_UpperCAmelCase							 =		(-b + sqrt(A       )) / (2 * a)
		_UpperCAmelCase							 =		(-b - sqrt(A       )) / (2 * a)

		return (
		    root_a.real if not root_a.imag else root_a,
		    root_a.real if not root_a.imag else root_a,
		)




def 	UpperCAmelCase     (		):

		'''simple docstring'''


		_UpperCAmelCase ,					_UpperCAmelCase							 =		quadratic_roots(a=5     ,				b=6     ,				c=1       )
		print(f'The solutions are: {solutiona} and {solutiona}'       )


if __name__ == "__main__":
					main()


