



'''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


a							   =				logging.get_logger(__name__)

a							   =				{
    '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_		(     snake_case    ):
							UpperCAmelCase			:		List[Any]        =				"""marian"""
							UpperCAmelCase			:		Dict        =				["""past_key_values"""]
							UpperCAmelCase			:		List[str]        =				{"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}


							def __init__(				self   :						List[str]      ,						a_   :						Optional[int]=5_8_1_0_1      ,						a_   :						Tuple=None      ,						a_   :						Optional[Any]=1_0_2_4      ,						a_   :						str=1_2      ,						a_   :						Tuple=4_0_9_6      ,						a_   :						Optional[int]=1_6      ,						a_   :						Tuple=1_2      ,						a_   :						Tuple=4_0_9_6      ,						a_   :						Union[str, Any]=1_6      ,						a_   :						Tuple=0.0      ,						a_   :						Any=0.0      ,						a_   :						Optional[int]=True      ,						a_   :						Optional[Any]=True      ,						a_   :						Any="gelu"      ,						a_   :						Any=1_0_2_4      ,						a_   :						int=0.1      ,						a_   :						str=0.0      ,						a_   :						List[Any]=0.0      ,						a_   :						Any=0.0_2      ,						a_   :						str=5_8_1_0_0      ,						a_   :						Any=False      ,						a_   :						str=5_8_1_0_0      ,						a_   :						List[str]=0      ,						a_   :						Any=0      ,						a_   :						int=True      ,						**a_   :						Any      ,						)			->					Optional[Any]:
										snake_case:    Any      							=vocab_size
										snake_case:    List[Any]      							=decoder_vocab_size or vocab_size
										snake_case:    str      							=max_position_embeddings
										snake_case:    List[Any]      							=d_model
										snake_case:    Tuple      							=encoder_ffn_dim
										snake_case:    List[str]      							=encoder_layers
										snake_case:    Dict      							=encoder_attention_heads
										snake_case:    List[Any]      							=decoder_ffn_dim
										snake_case:    Optional[Any]      							=decoder_layers
										snake_case:    Optional[Any]      							=decoder_attention_heads
										snake_case:    Any      							=dropout
										snake_case:    Tuple      							=attention_dropout
										snake_case:    int      							=activation_dropout
										snake_case:    Optional[int]      							=activation_function
										snake_case:    Any      							=init_std
										snake_case:    int      							=encoder_layerdrop
										snake_case:    Union[str, Any]      							=decoder_layerdrop
										snake_case:    int      							=use_cache
										snake_case:    int      							=encoder_layers
										snake_case:    Optional[int]      							=scale_embedding  # scale factor will be sqrt(d_model) if True
										snake_case:    Union[str, Any]      							=share_encoder_decoder_embeddings
										super().__init__(
										    pad_token_id=a_      ,						eos_token_id=a_      ,						is_encoder_decoder=a_      ,						decoder_start_token_id=a_      ,						forced_eos_token_id=a_      ,						**a_      ,						)






class 						a_		(     snake_case    ):


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

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

													if self.use_past:
																self.fill_with_past_key_values_(a_      ,						direction='inputs'			)
										elif self.task == "causal-lm":
													# TODO: figure this case out.
													snake_case:    Union[str, Any]      							=OrderedDict(
													    [
													        ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
													        ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
													    ]			)
													if self.use_past:
																snake_case							,  snake_case:    List[Any]      							=self.num_layers
																for i in range(a_			):
																			snake_case:    Union[str, Any]      							={0: 'batch', 2: 'past_sequence + sequence'}
																			snake_case:    Dict      							={0: 'batch', 2: 'past_sequence + sequence'}
										else:
													snake_case:    Optional[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    UpperCamelCase     (				self   :						Optional[int]			)			->					Mapping[str, Mapping[int, str]]:
										if self.task in ["default", "seq2seq-lm"]:
													snake_case:    Union[str, Any]      							=super().outputs
										else:
													snake_case:    Optional[int]      							=super(a_      ,						self			).outputs
													if self.use_past:
																snake_case							,  snake_case:    List[Any]      							=self.num_layers
																for i in range(a_			):
																			snake_case:    List[str]      							={0: 'batch', 2: 'past_sequence + sequence'}
																			snake_case:    Optional[Any]      							={0: 'batch', 2: 'past_sequence + sequence'}
										return common_outputs


							def    UpperCamelCase     (				self   :						List[Any]      ,						a_   :						PreTrainedTokenizer      ,						a_   :						int = -1      ,						a_   :						int = -1      ,						a_   :						bool = False      ,						a_   :						Optional[TensorType] = None      ,						)			->					Mapping[str, Any]:
										snake_case:    Any      							=self._generate_dummy_inputs_for_encoder_and_decoder(
										    a_      ,						a_      ,						a_      ,						a_      ,						a_			)

										# Generate decoder inputs
										snake_case:    Optional[int]      							=seq_length if not self.use_past else 1
										snake_case:    List[Any]      							=self._generate_dummy_inputs_for_encoder_and_decoder(
										    a_      ,						a_      ,						a_      ,						a_      ,						a_			)
										snake_case:    Optional[int]      							={F'''decoder_{name}''': tensor for name, tensor in decoder_inputs.items()}
										snake_case:    Optional[int]      							=dict(**a_      ,						**a_			)

										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:    Optional[int]      							=common_inputs['input_ids'].shape
													snake_case:    List[Any]      							=common_inputs['decoder_input_ids'].shape[1]
													snake_case							,  snake_case:    List[str]      							=self.num_attention_heads
													snake_case:    int      							=(
													    batch,
													    num_encoder_attention_heads,
													    encoder_seq_length,
													    self._config.hidden_size // num_encoder_attention_heads,
													)
													snake_case:    Optional[int]      							=decoder_seq_length + 3
													snake_case:    int      							=(
													    batch,
													    num_decoder_attention_heads,
													    decoder_past_length,
													    self._config.hidden_size // num_decoder_attention_heads,
													)

													snake_case:    int      							=torch.cat(
													    [common_inputs['decoder_attention_mask'], torch.ones(a_      ,						a_			)]      ,						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:    str      							=self.num_layers
													snake_case:    str      							=min(a_      ,						a_			)
													snake_case:    str      							=max(a_      ,						a_			) - min_num_layers
													snake_case:    int      							='encoder' if num_encoder_layers > num_decoder_layers else 'decoder'

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


							def    UpperCamelCase     (				self   :						int      ,						a_   :						PreTrainedTokenizer      ,						a_   :						int = -1      ,						a_   :						int = -1      ,						a_   :						bool = False      ,						a_   :						Optional[TensorType] = None      ,						)			->					Mapping[str, Any]:
										snake_case:    List[Any]      							=self._generate_dummy_inputs_for_encoder_and_decoder(
										    a_      ,						a_      ,						a_      ,						a_      ,						a_			)

										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:    Optional[int]      							=common_inputs['input_ids'].shape
													# Not using the same length for past_key_values
													snake_case:    Tuple      							=seqlen + 2
													snake_case							,  snake_case:    List[str]      							=self.num_layers
													snake_case							,  snake_case:    str      							=self.num_attention_heads
													snake_case:    Any      							=(
													    batch,
													    num_encoder_attention_heads,
													    past_key_values_length,
													    self._config.hidden_size // num_encoder_attention_heads,
													)

													snake_case:    List[str]      							=common_inputs['attention_mask'].dtype
													snake_case:    int      							=torch.cat(
													    [common_inputs['attention_mask'], torch.ones(a_      ,						a_      ,						dtype=a_			)]      ,						dim=1			)
													snake_case:    Union[str, Any]      							=[
													    (torch.zeros(a_			), torch.zeros(a_			)) for _ in range(a_			)
													]
										return common_inputs


							def    UpperCamelCase     (				self   :						int      ,						a_   :						PreTrainedTokenizer      ,						a_   :						int = -1      ,						a_   :						int = -1      ,						a_   :						bool = False      ,						a_   :						Optional[TensorType] = 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
										snake_case:    Union[str, Any]      							=compute_effective_axis_dimension(
										    a_      ,						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:    str      							=tokenizer.num_special_tokens_to_add(a_			)
										snake_case:    Any      							=compute_effective_axis_dimension(
										    a_      ,						fixed_dimension=OnnxConfig.default_fixed_sequence      ,						num_token_to_add=a_			)

										# Generate dummy inputs according to compute batch and sequence
										snake_case:    Dict      							=[' '.join([tokenizer.unk_token]			) * seq_length] * batch_size
										snake_case:    Optional[int]      							=dict(tokenizer(a_      ,						return_tensors=a_			)			)
										return common_inputs


							def    UpperCamelCase     (				self   :						Tuple      ,						a_   :						PreTrainedTokenizer      ,						a_   :						int = -1      ,						a_   :						int = -1      ,						a_   :						bool = False      ,						a_   :						Optional[TensorType] = None      ,						)			->					Mapping[str, Any]:
										if self.task in ["default", "seq2seq-lm"]:
													snake_case:    str      							=self._generate_dummy_inputs_for_default_and_seqaseq_lm(
													    a_      ,						batch_size=a_      ,						seq_length=a_      ,						is_pair=a_      ,						framework=a_			)

										else:
													snake_case:    List[str]      							=self._generate_dummy_inputs_for_causal_lm(
													    a_      ,						batch_size=a_      ,						seq_length=a_      ,						is_pair=a_      ,						framework=a_			)

										return common_inputs


							def    UpperCamelCase     (				self   :						Optional[Any]      ,						a_   :						List[str]      ,						a_   :						Tuple      ,						a_   :						int      ,						a_   :						Dict			)			->					Union[str, Any]:
										if self.task in ["default", "seq2seq-lm"]:
													snake_case:    Optional[Any]      							=super()._flatten_past_key_values_(a_      ,						a_      ,						a_      ,						a_			)
										else:
													snake_case:    List[Any]      							=super(a_      ,						self			)._flatten_past_key_values_(
													    a_      ,						a_      ,						a_      ,						a_			)




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









'''simple docstring'''

import os
import unittest

from transformers import FunnelTokenizer, FunnelTokenizerFast
from transformers.models.funnel.tokenization_funnel import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin






@require_tokenizers
class 						a_		(     snake_case    ,   unittest.TestCase    ):
							UpperCAmelCase			:		int        =				FunnelTokenizer
							UpperCAmelCase			:		Tuple        =				FunnelTokenizerFast
							UpperCAmelCase			:		List[str]        =				True
							UpperCAmelCase			:		Tuple        =				True


							def    UpperCamelCase     (				self   :						Optional[Any]			)			->					Optional[Any]:
										super().setUp()

										snake_case:    Optional[Any]      							=[
										    '<unk>',
										    '<cls>',
										    '<sep>',
										    'want',
										    '##want',
										    '##ed',
										    'wa',
										    'un',
										    'runn',
										    '##ing',
										    ',',
										    'low',
										    'lowest',
										]
										snake_case:    Any      							=os.path.join(self.tmpdirname      ,						VOCAB_FILES_NAMES['vocab_file']			)
										with open(self.vocab_file      ,						'w'      ,						encoding='utf-8'			) as vocab_writer:
													vocab_writer.write(''.join([x + '\n' for x in vocab_tokens]			)			)


							def    UpperCamelCase     (				self   :						Tuple      ,						**a_   :						str			)			->					Any:
										return FunnelTokenizer.from_pretrained(self.tmpdirname      ,						**a_			)


							def    UpperCamelCase     (				self   :						List[Any]      ,						**a_   :						int			)			->					str:
										return FunnelTokenizerFast.from_pretrained(self.tmpdirname      ,						**a_			)


							def    UpperCamelCase     (				self   :						Any      ,						a_   :						Union[str, Any]			)			->					Union[str, Any]:
										snake_case:    List[str]      							='UNwant\u00E9d,running'
										snake_case:    Dict      							='unwanted, running'
										return input_text, output_text


							def    UpperCamelCase     (				self   :						Optional[int]			)			->					List[Any]:
										snake_case:    Union[str, Any]      							=self.tokenizer_class(self.vocab_file			)

										snake_case:    Dict      							=tokenizer.tokenize('UNwant\u00E9d,running'			)
										self.assertListEqual(a_      ,						['un', '##want', '##ed', ',', 'runn', '##ing']			)
										self.assertListEqual(tokenizer.convert_tokens_to_ids(a_			)      ,						[7, 4, 5, 1_0, 8, 9]			)




							def    UpperCamelCase     (				self   :						List[str]			)			->					Dict:
										snake_case:    Optional[Any]      							=self.get_tokenizers(do_lower_case=a_			)
										for tokenizer in tokenizers:
													snake_case:    str      							=tokenizer('UNwant\u00E9d,running'			)
													snake_case:    Union[str, Any]      							=len(inputs['input_ids']			) - 1
													self.assertListEqual(inputs['token_type_ids']      ,						[2] + [0] * sentence_len			)

													snake_case:    List[str]      							=tokenizer('UNwant\u00E9d,running'      ,						'UNwant\u00E9d,running'			)
													self.assertListEqual(inputs['token_type_ids']      ,						[2] + [0] * sentence_len + [1] * sentence_len			)




