
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


SCREAMING_SNAKE_CASE				:int									=							logging.get_logger(__name__)

SCREAMING_SNAKE_CASE				:int									=							{
    "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 				UpperCAmelCase (    __SCREAMING_SNAKE_CASE    ):




	'''simple docstring'''





	snake_case_          =	"marian"
	snake_case_          =	["past_key_values"]
	snake_case_          =	{"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}






	def __init__(			self   :					Any				,A   :					List[Any]=5_81_01				,A   :					Optional[int]=None				,A   :					List[str]=10_24				,A   :					int=12				,A   :					Union[str, Any]=40_96				,A   :					int=16				,A   :					Dict=12				,A   :					Optional[int]=40_96				,A   :					str=16				,A   :					List[Any]=0.0				,A   :					Optional[Any]=0.0				,A   :					List[str]=True				,A   :					Union[str, Any]=True				,A   :					List[Any]="gelu"				,A   :					int=10_24				,A   :					Optional[int]=0.1				,A   :					Optional[int]=0.0				,A   :					Tuple=0.0				,A   :					List[Any]=0.02				,A   :					Union[str, Any]=5_81_00				,A   :					str=False				,A   :					Optional[Any]=5_81_00				,A   :					Union[str, Any]=0				,A   :					Optional[Any]=0				,A   :					int=True				,**A   :					List[str]				,):
					__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=A				,eos_token_id=A				,is_encoder_decoder=A				,decoder_start_token_id=A				,forced_eos_token_id=A				,**A				,)





class 				UpperCAmelCase (    __SCREAMING_SNAKE_CASE    ):




	'''simple docstring'''





	@property
	# Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.inputs
	def 							UpperCamelCase_    (			self   :					List[Any]					):

					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_(A				,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(A					):
																	__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 							UpperCamelCase_    (			self   :					Any					):

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






	def 							UpperCamelCase_    (			self   :					Optional[int]				,A   :					PreTrainedTokenizer				,A   :					int = -1				,A   :					int = -1				,A   :					bool = False				,A   :					Optional[TensorType] = None				,):
					__A       =					self._generate_dummy_inputs_for_encoder_and_decoder(
					    A				,A				,A				,A				,A					)

					# Generate decoder inputs
					__A       =					seq_length if not self.use_past else 1
					__A       =					self._generate_dummy_inputs_for_encoder_and_decoder(
					    A				,A				,A				,A				,A					)
					__A       =					{f'''decoder_{name}''': tensor for name, tensor in decoder_inputs.items()}
					__A       =					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
									__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(A				,A					)]				,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(A				,A					)
									__A       =					max(A				,A					) - min_num_layers
									__A       =					"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.
									__A       =					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   :					str				,A   :					PreTrainedTokenizer				,A   :					int = -1				,A   :					int = -1				,A   :					bool = False				,A   :					Optional[TensorType] = None				,):
					__A       =					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
									__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(A				,A				,dtype=A					)]				,dim=1					)
									__A       =					[
									    (torch.zeros(A					), torch.zeros(A					)) for _ in range(A					)
									]
					return common_inputs






	def 							UpperCamelCase_    (			self   :					Dict				,A   :					PreTrainedTokenizer				,A   :					int = -1				,A   :					int = -1				,A   :					bool = False				,A   :					Optional[TensorType] = None				,):
					__A       =					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
					__A       =					tokenizer.num_special_tokens_to_add(A					)
					__A       =					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
					__A       =					[" ".join([tokenizer.unk_token]					) * seq_length] * batch_size
					__A       =					dict(tokenizer(A				,return_tensors=A					)					)
					return common_inputs






	def 							UpperCamelCase_    (			self   :					str				,A   :					PreTrainedTokenizer				,A   :					int = -1				,A   :					int = -1				,A   :					bool = False				,A   :					Optional[TensorType] = None				,):

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

					else:
									__A       =					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   :					List[Any]				,A   :					Optional[int]				,A   :					Optional[Any]				,A   :					str				,A   :					str					):

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







	@property
	def 							UpperCamelCase_    (			self   :					Optional[int]					):
					return 1E-4





import json
import os
import unittest

from transformers.models.ctrl.tokenization_ctrl import VOCAB_FILES_NAMES, CTRLTokenizer

from ...test_tokenization_common import TokenizerTesterMixin



class    UpperCAmelCase__(				lowerCamelCase  ,				unittest.TestCase						):

   '''simple docstring'''




   A     :	int												=     CTRLTokenizer
   A     :	Tuple												=     False
   A     :	Any												=     False


   def       UpperCAmelCase		(    self	:      int)						->					Any:




         """simple docstring"""

         super().setUp()

         # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
         lowercase__  =	['adapt', 're@@', 'a@@', 'apt', 'c@@', 't', '<unk>']
         lowercase__  =	dict(zip(lowerCAmelCase      ,					range(len(lowerCAmelCase))))
         lowercase__  =	['#version: 0.2', 'a p', 'ap t</w>', 'r e', 'a d', 'ad apt</w>', '']
         lowercase__  =	{'unk_token': '<unk>'}

         lowercase__  =	os.path.join(self.tmpdirname      ,					VOCAB_FILES_NAMES['vocab_file'])
         lowercase__  =	os.path.join(self.tmpdirname      ,					VOCAB_FILES_NAMES['merges_file'])
         with open(self.vocab_file      ,					'w'      ,					encoding='utf-8') as fp:
               fp.write(json.dumps(lowerCAmelCase) + '\n')
         with open(self.merges_file      ,					'w'      ,					encoding='utf-8') as fp:
               fp.write('\n'.join(lowerCAmelCase))


   def       UpperCAmelCase		(    self	:      int      ,					**lowerCAmelCase	:      str)						->					Dict:




         """simple docstring"""

         kwargs.update(self.special_tokens_map)
         return CTRLTokenizer.from_pretrained(self.tmpdirname      ,					**lowerCAmelCase)


   def       UpperCAmelCase		(    self	:      List[str]      ,					lowerCAmelCase	:      Optional[int])						->					Optional[Any]:




         """simple docstring"""

         lowercase__  =	'adapt react readapt apt'
         lowercase__  =	'adapt react readapt apt'
         return input_text, output_text



   def       UpperCAmelCase		(    self	:      Optional[int])						->					str:




         """simple docstring"""

         lowercase__  =	CTRLTokenizer(self.vocab_file      ,					self.merges_file      ,					**self.special_tokens_map)
         lowercase__  =	'adapt react readapt apt'
         lowercase__  =	'adapt re@@ a@@ c@@ t re@@ adapt apt'.split()
         lowercase__  =	tokenizer.tokenize(lowerCAmelCase)
         self.assertListEqual(lowerCAmelCase      ,					lowerCAmelCase)

         lowercase__  =	tokens + [tokenizer.unk_token]

         lowercase__  =	[0, 1, 2, 4, 5, 1, 0, 3, 6]
         self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCAmelCase)      ,					lowerCAmelCase)
