





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


lowerCAmelCase_    =    logging.get_logger(__name__)

lowerCAmelCase_    =    {
    '''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  __lowerCAmelCase	(      _a					):
			lowerCamelCase_					:					List[str]	      =				'''marian'''
			lowerCamelCase_					:					List[Any]	      =				['''past_key_values''']
			lowerCamelCase_					:					Tuple	      =				{'''num_attention_heads''': '''encoder_attention_heads''', '''hidden_size''': '''d_model'''}






			def __init__(self ,							__magic_name__=5_8101 ,							__magic_name__=None ,							__magic_name__=1024 ,							__magic_name__=12 ,							__magic_name__=4096 ,							__magic_name__=16 ,							__magic_name__=12 ,							__magic_name__=4096 ,							__magic_name__=16 ,							__magic_name__=0.0 ,							__magic_name__=0.0 ,							__magic_name__=True ,							__magic_name__=True ,							__magic_name__="gelu" ,							__magic_name__=1024 ,							__magic_name__=0.1 ,							__magic_name__=0.0 ,							__magic_name__=0.0 ,							__magic_name__=0.02 ,							__magic_name__=5_8100 ,							__magic_name__=False ,							__magic_name__=5_8100 ,							__magic_name__=0 ,							__magic_name__=0 ,							__magic_name__=True ,							**__magic_name__ ,							)					->       str:


							'''simple docstring'''



							snake_case_						:   List[str]           =  vocab_size
							snake_case_						:   str           =  decoder_vocab_size or vocab_size
							snake_case_						:   Union[str, Any]           =  max_position_embeddings
							snake_case_						:   Dict           =  d_model
							snake_case_						:   Optional[int]           =  encoder_ffn_dim
							snake_case_						:   List[Any]           =  encoder_layers
							snake_case_						:   Tuple           =  encoder_attention_heads
							snake_case_						:   List[str]           =  decoder_ffn_dim
							snake_case_						:   List[Any]           =  decoder_layers
							snake_case_						:   Tuple           =  decoder_attention_heads
							snake_case_						:   Optional[Any]           =  dropout
							snake_case_						:   Tuple           =  attention_dropout
							snake_case_						:   str           =  activation_dropout
							snake_case_						:   str           =  activation_function
							snake_case_						:   List[Any]           =  init_std
							snake_case_						:   Any           =  encoder_layerdrop
							snake_case_						:   Optional[Any]           =  decoder_layerdrop
							snake_case_						:   Union[str, Any]           =  use_cache
							snake_case_						:   Optional[int]           =  encoder_layers
							snake_case_						:   Optional[int]           =  scale_embedding  # scale factor will be sqrt(d_model) if True
							snake_case_						:   Optional[int]           =  share_encoder_decoder_embeddings
							super().__init__(
							    pad_token_id=__magic_name__ ,							eos_token_id=__magic_name__ ,							is_encoder_decoder=__magic_name__ ,							decoder_start_token_id=__magic_name__ ,							forced_eos_token_id=__magic_name__ ,							**__magic_name__ ,							)




class  __lowerCAmelCase	(      _a					):






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


							'''simple docstring'''



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

											if self.use_past:
															snake_case_						:   Dict           =  {0: '''batch'''}
															snake_case_						:   Optional[int]           =  {0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
											else:
															snake_case_						:   Optional[int]           =  {0: '''batch''', 1: '''decoder_sequence'''}
															snake_case_						:   Union[str, Any]           =  {0: '''batch''', 1: '''decoder_sequence'''}

											if self.use_past:
															self.fill_with_past_key_values_(__magic_name__ ,							direction='''inputs'''   )
							elif self.task == "causal-lm":
											# TODO: figure this case out.
											snake_case_						:   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[str]           =  self.num_layers
															for i in range(__magic_name__   ):
																			snake_case_						:   int           =  {0: '''batch''', 2: '''past_sequence + sequence'''}
																			snake_case_						:   Union[str, Any]           =  {0: '''batch''', 2: '''past_sequence + sequence'''}
							else:
											snake_case_						:   List[str]           =  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]]:


							'''simple docstring'''



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






			def       lowerCamelCase  (self ,							__magic_name__ ,							__magic_name__ = -1 ,							__magic_name__ = -1 ,							__magic_name__ = False ,							__magic_name__ = None ,							)					->       Mapping[str, Any]:


							'''simple docstring'''



							snake_case_						:   int           =  self._generate_dummy_inputs_for_encoder_and_decoder(
							    __magic_name__ ,							__magic_name__ ,							__magic_name__ ,							__magic_name__ ,							__magic_name__   )

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

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

											snake_case_						:   str           =  torch.cat(
											    [common_inputs['''decoder_attention_mask'''], torch.ones(__magic_name__ ,							__magic_name__   )] ,							dim=1   )

											snake_case_						:   Tuple           =  []
											# 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_						:   List[Any]           =  min(__magic_name__ ,							__magic_name__   )
											snake_case_						:   List[Any]           =  max(__magic_name__ ,							__magic_name__   ) - min_num_layers
											snake_case_						:   Tuple           =  '''encoder''' if num_encoder_layers > num_decoder_layers else '''decoder'''

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






			def       lowerCamelCase  (self ,							__magic_name__ ,							__magic_name__ = -1 ,							__magic_name__ = -1 ,							__magic_name__ = False ,							__magic_name__ = None ,							)					->       Mapping[str, Any]:


							'''simple docstring'''



							snake_case_						:   Dict           =  self._generate_dummy_inputs_for_encoder_and_decoder(
							    __magic_name__ ,							__magic_name__ ,							__magic_name__ ,							__magic_name__ ,							__magic_name__   )

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

											snake_case_						:   Dict           =  common_inputs['''attention_mask'''].dtype
											snake_case_						:   Any           =  torch.cat(
											    [common_inputs['''attention_mask'''], torch.ones(__magic_name__ ,							__magic_name__ ,							dtype=__magic_name__   )] ,							dim=1   )
											snake_case_						:   Any           =  [
											    (torch.zeros(__magic_name__   ), torch.zeros(__magic_name__   )) for _ in range(__magic_name__   )
											]
							return common_inputs






			def       lowerCamelCase  (self ,							__magic_name__ ,							__magic_name__ = -1 ,							__magic_name__ = -1 ,							__magic_name__ = False ,							__magic_name__ = None ,							)					->       Mapping[str, Any]:


							'''simple docstring'''



							snake_case_						:   Union[str, Any]           =  compute_effective_axis_dimension(
							    __magic_name__ ,							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_						:   Optional[Any]           =  tokenizer.num_special_tokens_to_add(__magic_name__   )
							snake_case_						:   Tuple           =  compute_effective_axis_dimension(
							    __magic_name__ ,							fixed_dimension=OnnxConfig.default_fixed_sequence ,							num_token_to_add=__magic_name__   )

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






			def       lowerCamelCase  (self ,							__magic_name__ ,							__magic_name__ = -1 ,							__magic_name__ = -1 ,							__magic_name__ = False ,							__magic_name__ = None ,							)					->       Mapping[str, Any]:


							'''simple docstring'''



							if self.task in ["default", "seq2seq-lm"]:
											snake_case_						:   List[str]           =  self._generate_dummy_inputs_for_default_and_seqaseq_lm(
											    __magic_name__ ,							batch_size=__magic_name__ ,							seq_length=__magic_name__ ,							is_pair=__magic_name__ ,							framework=__magic_name__   )

							else:
											snake_case_						:   List[Any]           =  self._generate_dummy_inputs_for_causal_lm(
											    __magic_name__ ,							batch_size=__magic_name__ ,							seq_length=__magic_name__ ,							is_pair=__magic_name__ ,							framework=__magic_name__   )

							return common_inputs






			def       lowerCamelCase  (self ,							__magic_name__ ,							__magic_name__ ,							__magic_name__ ,							__magic_name__   )					->       List[str]:


							'''simple docstring'''



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






			@property
			def       lowerCamelCase  (self   )					->       float:


							'''simple docstring'''



							return 1e-4







import unittest

from transformers import GPTSwaTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin


lowerCAmelCase_    =    get_tests_dir('''fixtures/test_sentencepiece_with_bytefallback.model''')



@require_sentencepiece
@require_tokenizers
class  __lowerCAmelCase	(      _a,					unittest.TestCase					):
			lowerCamelCase_					:					List[str]	      =				GPTSwaTokenizer
			lowerCamelCase_					:					str	      =				False
			lowerCamelCase_					:					str	      =				True
			lowerCamelCase_					:					List[Any]	      =				False






			def       lowerCamelCase  (self   )					->       List[str]:


							'''simple docstring'''



							super().setUp()

							# We have a SentencePiece fixture for testing
							snake_case_						:   Union[str, Any]           =  GPTSwaTokenizer(__magic_name__ ,							eos_token='''<unk>''' ,							bos_token='''<unk>''' ,							pad_token='''<unk>'''   )

							tokenizer.save_pretrained(self.tmpdirname   )






			def       lowerCamelCase  (self ,							__magic_name__   )					->       int:


							'''simple docstring'''



							snake_case_						:   Any           =  '''This is a test'''
							snake_case_						:   str           =  '''This is a test'''
							return input_text, output_text






			def       lowerCamelCase  (self   )					->       Any:


							'''simple docstring'''



							snake_case_						:   int           =  '''<s>'''
							snake_case_						:   Any           =  1

							self.assertEqual(self.get_tokenizer()._convert_token_to_id(__magic_name__   ) ,							__magic_name__   )
							self.assertEqual(self.get_tokenizer()._convert_id_to_token(__magic_name__   ) ,							__magic_name__   )






			def       lowerCamelCase  (self   )					->       List[Any]:


							'''simple docstring'''



							snake_case_						:   Union[str, Any]           =  list(self.get_tokenizer().get_vocab().keys()   )

							self.assertEqual(vocab_keys[0] ,							'''<unk>'''   )
							self.assertEqual(vocab_keys[1] ,							'''<s>'''   )
							self.assertEqual(vocab_keys[-1] ,							'''j'''   )
							self.assertEqual(len(__magic_name__   ) ,							2000   )






			def       lowerCamelCase  (self   )					->       List[str]:


							'''simple docstring'''



							self.assertEqual(self.get_tokenizer().vocab_size ,							2000   )






			def       lowerCamelCase  (self   )					->       List[str]:


							'''simple docstring'''



							snake_case_						:   str           =  GPTSwaTokenizer(__magic_name__   )

							snake_case_						:   List[str]           =  tokenizer.tokenize('''This is a test'''   )
							self.assertListEqual(__magic_name__ ,							['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est''']   )

							self.assertListEqual(tokenizer.convert_tokens_to_ids(__magic_name__   ) ,							[465, 287, 265, 631, 842]   )

							snake_case_						:   Optional[int]           =  tokenizer.tokenize('''I was born in 92000, and this is falsé.'''   )
							# fmt: off
							self.assertListEqual(
							    __magic_name__ ,							['''▁I''', '''▁was''', '''▁bor''', '''n''', '''▁in''', '''▁''', '''<0x39>''', '''2''', '''0''', '''0''', '''0''', ''',''', '''▁and''', '''▁this''', '''▁is''', '''▁f''', '''al''', '''s''', '''<0xC3>''', '''<0xA9>''', '''.'''] ,							)
							# fmt: on

							snake_case_						:   int           =  tokenizer.convert_tokens_to_ids(__magic_name__   )
							self.assertListEqual(
							    __magic_name__ ,							[262, 272, 1525, 286, 271, 268, 60, 916, 633, 633, 633, 259, 266, 301, 287, 384, 367, 263, 198, 172, 260] ,							)

							snake_case_						:   Dict           =  tokenizer.convert_ids_to_tokens(__magic_name__   )
							# fmt: off
							self.assertListEqual(
							    __magic_name__ ,							['''▁I''', '''▁was''', '''▁bor''', '''n''', '''▁in''', '''▁''', '''<0x39>''', '''2''', '''0''', '''0''', '''0''', ''',''', '''▁and''', '''▁this''', '''▁is''', '''▁f''', '''al''', '''s''', '''<0xC3>''', '''<0xA9>''', '''.''']   )
							# fmt: on






			def       lowerCamelCase  (self   )					->       Union[str, Any]:


							'''simple docstring'''



							snake_case_						:   Optional[Any]           =  GPTSwaTokenizer(__magic_name__   )
							snake_case_						:   Tuple           =  ['''This is a test''', '''I was born in 92000, and this is falsé.''']
							snake_case_						:   Optional[int]           =  [
							    [465, 287, 265, 631, 842],
							    [262, 272, 1525, 286, 271, 268, 60, 916, 633, 633, 633, 259, 266, 301, 287, 384, 367, 263, 198, 172, 260],
							]

							# Test that encode_fast returns the same as tokenize + convert_tokens_to_ids
							for text, expected_ids in zip(__magic_name__ ,							__magic_name__   ):
											self.assertListEqual(tokenizer.encode_fast(__magic_name__   ) ,							__magic_name__   )

							# Test that decode_fast returns the input text
							for text, token_ids in zip(__magic_name__ ,							__magic_name__   ):
											self.assertEqual(tokenizer.decode_fast(__magic_name__   ) ,							__magic_name__   )






			@slow
			def       lowerCamelCase  (self   )					->       Tuple:


							'''simple docstring'''



							snake_case_						:   str           =  [
							    '''<|python|>def fibonacci(n)\n    if n < 0:\n        print(\'Incorrect input\')''',
							    '''Hey there, how are you doing this fine day?''',
							    '''This is a text with a trailing spaces followed by a dot     .''',
							    '''Häj sväjs lillebrör! =)''',
							    '''Det är inget fel på Mr. Cool''',
							]

							# fmt: off
							snake_case_						:   str           =  {'''input_ids''': [[6_3423, 5, 6811, 1_4954, 282, 816, 3821, 6_3466, 6_3425, 6_3462, 18, 6_3978, 678, 301, 1320, 6_3423, 6_3455, 6_3458, 18, 6_3982, 4246, 3940, 1901, 4_7789, 5547, 1_8994], [1_9630, 1100, 6_3446, 1342, 633, 544, 4488, 593, 5102, 2416, 6_3495, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1652, 428, 268, 1936, 515, 268, 5_8593, 2_2413, 9106, 546, 268, 3_3213, 6_3979, 698, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [5_5130, 6_3450, 924, 6_3449, 2249, 4062, 1558, 318, 6_3504, 2_1498, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [509, 377, 2827, 2559, 332, 6575, 6_3443, 2_6801, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], '''token_type_ids''': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], '''attention_mask''': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}
							# fmt: on
							self.tokenizer_integration_test_util(
							    expected_encoding=__magic_name__ ,							model_name='''AI-Sweden/gpt-sw3-126m''' ,							sequences=__magic_name__ ,							)
