






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 						SCREAMING_SNAKE_CASE				( snake_case					):





							"""simple docstring"""

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

							def __init__(  self:   Union[str, Any]		,     __A:   Any=5_81_01		,     __A:   Optional[Any]=None		,     __A:   List[str]=10_24		,     __A:   Any=12		,     __A:   Tuple=40_96		,     __A:   Union[str, Any]=16		,     __A:   Tuple=12		,     __A:   Optional[int]=40_96		,     __A:   List[Any]=16		,     __A:   Optional[Any]=0.0		,     __A:   Optional[Any]=0.0		,     __A:   Optional[int]=True		,     __A:   str=True		,     __A:   Tuple="gelu"		,     __A:   str=10_24		,     __A:   str=0.1		,     __A:   Dict=0.0		,     __A:   List[str]=0.0		,     __A:   List[Any]=0.02		,     __A:   int=5_81_00		,     __A:   Tuple=False		,     __A:   Optional[int]=5_81_00		,     __A:   int=0		,     __A:   Optional[int]=0		,     __A:   Optional[int]=True		,     **__A:   Dict		,     )    ->				Dict:
												_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 						SCREAMING_SNAKE_CASE				( snake_case					):





							"""simple docstring"""

							@property
							# Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.inputs
							def       __A (  self:   Any   )    ->				Mapping[str, Mapping[int, str]]:
												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       __A (  self:   int   )    ->				Mapping[str, Mapping[int, str]]:
												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       __A (  self:   Optional[Any]		,     __A:   PreTrainedTokenizer		,     __A:   int = -1		,     __A:   int = -1		,     __A:   bool = False		,     __A:   Optional[TensorType] = None		,     )    ->				Mapping[str, Any]:
												_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       __A (  self:   List[str]		,     __A:   PreTrainedTokenizer		,     __A:   int = -1		,     __A:   int = -1		,     __A:   bool = False		,     __A:   Optional[TensorType] = None		,     )    ->				Mapping[str, Any]:
												_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       __A (  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
												_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       __A (  self:   Union[str, Any]		,     __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"]:
																	_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       __A (  self:   Dict		,     __A:   Any		,     __A:   Any		,     __A:   Tuple		,     __A:   Dict   )    ->				Union[str, Any]:
												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       __A (  self:   Any   )    ->				float:
												return 1e-4











import json
import os
import unittest

from transformers.models.blenderbot_small.tokenization_blenderbot_small import (
    VOCAB_FILES_NAMES,
    BlenderbotSmallTokenizer,
)

from ...test_tokenization_common import TokenizerTesterMixin





class 						SCREAMING_SNAKE_CASE				( snake_case  ,	unittest.TestCase					):





							"""simple docstring"""

							A_						  =					BlenderbotSmallTokenizer
							A_						  =					False

							def       __A (  self:   List[str]   )    ->				int:
												super().setUp()

												_A								= ['''__start__''', '''adapt''', '''act''', '''ap@@''', '''te''', '''__end__''', '''__unk__''']
												_A								= dict(zip(__A		,     range(len(__A   )   )   )   )

												_A								= ['''#version: 0.2''', '''a p''', '''t e</w>''', '''ap t</w>''', '''a d''', '''ad apt</w>''', '''a c''', '''ac t</w>''', '''''']
												_A								= {'''unk_token''': '''__unk__''', '''bos_token''': '''__start__''', '''eos_token''': '''__end__'''}

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

							def       __A (  self:   str		,     **__A:   Optional[Any]   )    ->				Dict:
												kwargs.update(self.special_tokens_map   )
												return BlenderbotSmallTokenizer.from_pretrained(self.tmpdirname		,     **__A   )

							def       __A (  self:   str		,     __A:   List[str]   )    ->				int:
												_A								= '''adapt act apte'''
												_A								= '''adapt act apte'''
												return input_text, output_text

							def       __A (  self:   Union[str, Any]   )    ->				Any:
												_A								= BlenderbotSmallTokenizer(self.vocab_file		,     self.merges_file		,     **self.special_tokens_map   )
												_A								= '''adapt act apte'''
												_A								= ['''adapt''', '''act''', '''ap@@''', '''te''']
												_A								= tokenizer.tokenize(__A   )
												self.assertListEqual(__A		,     __A   )

												_A								= [tokenizer.bos_token] + tokens + [tokenizer.eos_token]

												_A								= [0, 1, 2, 3, 4, 5]
												self.assertListEqual(tokenizer.convert_tokens_to_ids(__A   )		,     __A   )

							def       __A (  self:   Any   )    ->				List[str]:
												_A								= BlenderbotSmallTokenizer.from_pretrained('''facebook/blenderbot-90M'''   )
												assert tok('''sam'''   ).input_ids == [13_84]
												_A								= '''I am a small frog.'''
												_A								= tok([src_text]		,     padding=__A		,     truncation=__A   )['''input_ids''']
												_A								= tok.batch_decode(__A		,     skip_special_tokens=__A		,     clean_up_tokenization_spaces=__A   )[0]
												assert src_text != decoded  # I wish it did!
												assert decoded == "i am a small frog ."



							def       __A (  self:   Any   )    ->				int:
												_A								= BlenderbotSmallTokenizer.from_pretrained('''facebook/blenderbot-90M'''   )
												_A								= '''I am a small frog .'''
												_A								= '''.'''
												_A								= tok(__A   )['''input_ids''']
												_A								= tok(__A   )['''input_ids''']

												assert encoded[-1] == encoded_dot[0]



