

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  A						(							_UpperCAmelCase ):







    """simple docstring"""





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


    def __init__(      self :       str,lowercase_ :       Dict=5_8_1_0_1,lowercase_ :       List[str]=None,lowercase_ :       int=1_0_2_4,lowercase_ :       Optional[Any]=1_2,lowercase_ :       List[Any]=4_0_9_6,lowercase_ :       Optional[Any]=1_6,lowercase_ :       Dict=1_2,lowercase_ :       Any=4_0_9_6,lowercase_ :       Dict=1_6,lowercase_ :       str=0.0,lowercase_ :       int=0.0,lowercase_ :       int=True,lowercase_ :       List[str]=True,lowercase_ :       str="gelu",lowercase_ :       Optional[int]=1_0_2_4,lowercase_ :       Dict=0.1,lowercase_ :       List[Any]=0.0,lowercase_ :       List[Any]=0.0,lowercase_ :       int=0.02,lowercase_ :       Any=5_8_1_0_0,lowercase_ :       str=False,lowercase_ :       Optional[Any]=5_8_1_0_0,lowercase_ :       Optional[Any]=0,lowercase_ :       Union[str, Any]=0,lowercase_ :       int=True,**lowercase_ :       Tuple,)->    List[Any]:





        '''simple docstring'''







        A__          =   vocab_size
        A__          =   decoder_vocab_size or vocab_size
        A__          =   max_position_embeddings
        A__          =   d_model
        A__          =   encoder_ffn_dim
        A__          =   encoder_layers
        A__          =   encoder_attention_heads
        A__          =   decoder_ffn_dim
        A__          =   decoder_layers
        A__          =   decoder_attention_heads
        A__          =   dropout
        A__          =   attention_dropout
        A__          =   activation_dropout
        A__          =   activation_function
        A__          =   init_std
        A__          =   encoder_layerdrop
        A__          =   decoder_layerdrop
        A__          =   use_cache
        A__          =   encoder_layers
        A__          =   scale_embedding  # scale factor will be sqrt(d_model) if True
        A__          =   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  A						(							_UpperCAmelCase ):







    """simple docstring"""





    @property
    # Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.inputs
    def 						snake_case__     (      self :       int		)->    Mapping[str, Mapping[int, str]]:





        '''simple docstring'''







        if self.task in ["default", "seq2seq-lm"]:
            A__          =   OrderedDict(
                [
                    ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
                    ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
                ]		)

            if self.use_past:
                A__          =   {0: 'batch'}
                A__          =   {0: 'batch', 1: 'past_decoder_sequence + sequence'}
            else:
                A__          =   {0: 'batch', 1: 'decoder_sequence'}
                A__          =   {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__          =   OrderedDict(
                [
                    ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
                    ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
                ]		)
            if self.use_past:
                A__      ,						A__          =   self.num_layers
                for i in range(lowercase_		):
                    A__          =   {0: 'batch', 2: 'past_sequence + sequence'}
                    A__          =   {0: 'batch', 2: 'past_sequence + sequence'}
        else:
            A__          =   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 						snake_case__     (      self :       str		)->    Mapping[str, Mapping[int, str]]:





        '''simple docstring'''







        if self.task in ["default", "seq2seq-lm"]:
            A__          =   super().outputs
        else:
            A__          =   super(lowercase_,self		).outputs
            if self.use_past:
                A__      ,						A__          =   self.num_layers
                for i in range(lowercase_		):
                    A__          =   {0: 'batch', 2: 'past_sequence + sequence'}
                    A__          =   {0: 'batch', 2: 'past_sequence + sequence'}
        return common_outputs


    def 						snake_case__     (      self :       List[Any],lowercase_ :       PreTrainedTokenizer,lowercase_ :       int = -1,lowercase_ :       int = -1,lowercase_ :       bool = False,lowercase_ :       Optional[TensorType] = None,)->    Mapping[str, Any]:





        '''simple docstring'''







        A__          =   self._generate_dummy_inputs_for_encoder_and_decoder(
            lowercase_,lowercase_,lowercase_,lowercase_,lowercase_		)

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

            A__          =   torch.cat(
                [common_inputs['decoder_attention_mask'], torch.ones(lowercase_,lowercase_		)],dim=1		)

            A__          =   []
            # If the number of encoder and decoder layers are present in the model configuration, both are considered
            A__      ,						A__          =   self.num_layers
            A__          =   min(lowercase_,lowercase_		)
            A__          =   max(lowercase_,lowercase_		) - min_num_layers
            A__          =   '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__          =   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 						snake_case__     (      self :       Dict,lowercase_ :       PreTrainedTokenizer,lowercase_ :       int = -1,lowercase_ :       int = -1,lowercase_ :       bool = False,lowercase_ :       Optional[TensorType] = None,)->    Mapping[str, Any]:





        '''simple docstring'''







        A__          =   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__          =   common_inputs['input_ids'].shape
            # Not using the same length for past_key_values
            A__          =   seqlen + 2
            A__      ,						A__          =   self.num_layers
            A__      ,						A__          =   self.num_attention_heads
            A__          =   (
                batch,
                num_encoder_attention_heads,
                past_key_values_length,
                self._config.hidden_size // num_encoder_attention_heads,
            )

            A__          =   common_inputs['attention_mask'].dtype
            A__          =   torch.cat(
                [common_inputs['attention_mask'], torch.ones(lowercase_,lowercase_,dtype=lowercase_		)],dim=1		)
            A__          =   [
                (torch.zeros(lowercase_		), torch.zeros(lowercase_		)) for _ in range(lowercase_		)
            ]
        return common_inputs


    def 						snake_case__     (      self :       Tuple,lowercase_ :       PreTrainedTokenizer,lowercase_ :       int = -1,lowercase_ :       int = -1,lowercase_ :       bool = False,lowercase_ :       Optional[TensorType] = None,)->    Mapping[str, Any]:





        '''simple docstring'''







        A__          =   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__          =   tokenizer.num_special_tokens_to_add(lowercase_		)
        A__          =   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__          =   [' '.join([tokenizer.unk_token]		) * seq_length] * batch_size
        A__          =   dict(tokenizer(lowercase_,return_tensors=lowercase_		)		)
        return common_inputs


    def 						snake_case__     (      self :       Optional[int],lowercase_ :       PreTrainedTokenizer,lowercase_ :       int = -1,lowercase_ :       int = -1,lowercase_ :       bool = False,lowercase_ :       Optional[TensorType] = None,)->    Mapping[str, Any]:





        '''simple docstring'''







        if self.task in ["default", "seq2seq-lm"]:
            A__          =   self._generate_dummy_inputs_for_default_and_seqaseq_lm(
                lowercase_,batch_size=lowercase_,seq_length=lowercase_,is_pair=lowercase_,framework=lowercase_		)

        else:
            A__          =   self._generate_dummy_inputs_for_causal_lm(
                lowercase_,batch_size=lowercase_,seq_length=lowercase_,is_pair=lowercase_,framework=lowercase_		)

        return common_inputs


    def 						snake_case__     (      self :       str,lowercase_ :       Any,lowercase_ :       Union[str, Any],lowercase_ :       List[str],lowercase_ :       Optional[Any]		)->    Any:





        '''simple docstring'''







        if self.task in ["default", "seq2seq-lm"]:
            A__          =   super()._flatten_past_key_values_(lowercase_,lowercase_,lowercase_,lowercase_		)
        else:
            A__          =   super(lowercase_,self		)._flatten_past_key_values_(
                lowercase_,lowercase_,lowercase_,lowercase_		)


    @property
    def 						snake_case__     (      self :       str		)->    float:





        '''simple docstring'''







        return 1E-4








from __future__ import annotations

from collections.abc import Callable





def 						__SCREAMING_SNAKE_CASE  (				UpperCamelCase__			,			UpperCamelCase__			,			UpperCamelCase__			,			UpperCamelCase__ = 100			,			)  ->				float:







			'''simple docstring'''

			UpperCAmelCase   		=				x_start
			UpperCAmelCase   		=				fnc(UpperCamelCase__ )
			UpperCAmelCase   		=				0.0
			for _ in range(UpperCamelCase__ ):
						# Approximates small segments of curve as linear and solve
						# for trapezoidal area
						UpperCAmelCase   		=				(x_end - x_start) / steps + xa
						UpperCAmelCase   		=				fnc(UpperCamelCase__ )
						area += abs(fxa + fxa ) * (xa - xa) / 2
						# Increment step
						UpperCAmelCase   		=				xa
						UpperCAmelCase   		=				fxa
			return area


if __name__ == "__main__":





				def 						__SCREAMING_SNAKE_CASE  (				UpperCamelCase__ )  ->				str:







							'''simple docstring'''

							return x**3 + x**2

				print("f(x) = x^3 + x^2")
				print("The area between the curve, x = -5, x = 5 and the x axis is:")
				__A				:    List[Any]       =   10
				while i <= 100_000:
								print(F'with {i} steps: {trapezoidal_area(f, -5, 5, i)}')
								i *= 10



