


import tempfile
import unittest

from transformers import TaConfig, is_torch_available
from transformers.testing_utils import (
    require_sentencepiece,
    require_tokenizers,
    require_torch,
    slow,
    torch_device,
)

from ...generation.test_utils import GenerationTesterMixin
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
 import torch

 from transformers import AutoTokenizer, UMTaForConditionalGeneration, UMTaForQuestionAnswering, UMTaModel






class     UpperCamelCase:


     def __init__(						self	:		List[Any]     ,    SCREAMING_SNAKE_CASE	:		List[Any]     ,    SCREAMING_SNAKE_CASE	:		List[str]=9_9     ,    SCREAMING_SNAKE_CASE	:		Any=1_3     ,    SCREAMING_SNAKE_CASE	:		Optional[int]=7     ,    SCREAMING_SNAKE_CASE	:		Dict=9     ,    SCREAMING_SNAKE_CASE	:		Dict=True     ,    SCREAMING_SNAKE_CASE	:		Optional[int]=True     ,    SCREAMING_SNAKE_CASE	:		Dict=False     ,    SCREAMING_SNAKE_CASE	:		Union[str, Any]=3_2     ,    SCREAMING_SNAKE_CASE	:		List[str]=5     ,    SCREAMING_SNAKE_CASE	:		int=4     ,    SCREAMING_SNAKE_CASE	:		Dict=3_7     ,    SCREAMING_SNAKE_CASE	:		Optional[int]=8     ,    SCREAMING_SNAKE_CASE	:		List[Any]=0.1     ,    SCREAMING_SNAKE_CASE	:		int=0.002     ,    SCREAMING_SNAKE_CASE	:		Optional[Any]=1     ,    SCREAMING_SNAKE_CASE	:		List[Any]=0     ,    SCREAMING_SNAKE_CASE	:		List[str]=0     ,    SCREAMING_SNAKE_CASE	:		List[Any]=None     ,    SCREAMING_SNAKE_CASE	:		Tuple=None     ,    )			->	str:




           '''simple docstring'''

           __snake_case				=		parent
           __snake_case				=		batch_size
           __snake_case				=		encoder_seq_length
           __snake_case				=		decoder_seq_length
           # For common tests
           __snake_case				=		self.decoder_seq_length
           __snake_case				=		is_training
           __snake_case				=		use_attention_mask
           __snake_case				=		use_labels
           __snake_case				=		vocab_size
           __snake_case				=		hidden_size
           __snake_case				=		num_hidden_layers
           __snake_case				=		num_attention_heads
           __snake_case				=		d_ff
           __snake_case				=		relative_attention_num_buckets
           __snake_case				=		dropout_rate
           __snake_case				=		initializer_factor
           __snake_case				=		eos_token_id
           __snake_case				=		pad_token_id
           __snake_case				=		decoder_start_token_id
           __snake_case				=		None
           __snake_case				=		decoder_layers


     def 							SCREAMING_SNAKE_CASE_  (						self	:		Any						)			->	List[str]:




           '''simple docstring'''

           return TaConfig.from_pretrained("google/umt5-base"						)


     def 							SCREAMING_SNAKE_CASE_  (						self	:		Tuple     ,    SCREAMING_SNAKE_CASE	:		str     ,    SCREAMING_SNAKE_CASE	:		Optional[int]     ,    SCREAMING_SNAKE_CASE	:		Dict     ,    SCREAMING_SNAKE_CASE	:		Optional[Any]=None     ,    SCREAMING_SNAKE_CASE	:		Optional[Any]=None     ,    SCREAMING_SNAKE_CASE	:		Union[str, Any]=None     ,    SCREAMING_SNAKE_CASE	:		Optional[int]=None     ,    SCREAMING_SNAKE_CASE	:		List[str]=None     ,    )			->	int:




           '''simple docstring'''

           if attention_mask is None:
                 __snake_case				=		input_ids.ne(config.pad_token_id						)
           if decoder_attention_mask is None:
                 __snake_case				=		decoder_input_ids.ne(config.pad_token_id						)
           if head_mask is None:
                 __snake_case				=		torch.ones(config.num_hidden_layers     ,    config.num_attention_heads     ,    device=SCREAMING_SNAKE_CASE						)
           if decoder_head_mask is None:
                 __snake_case				=		torch.ones(config.num_decoder_layers     ,    config.num_attention_heads     ,    device=SCREAMING_SNAKE_CASE						)
           if cross_attn_head_mask is None:
                 __snake_case				=		torch.ones(
                     config.num_decoder_layers     ,    config.num_attention_heads     ,    device=SCREAMING_SNAKE_CASE						)
           return {
               "input_ids": input_ids,
               "decoder_input_ids": decoder_input_ids,
               "attention_mask": attention_mask,
               "decoder_attention_mask": decoder_attention_mask,
               "head_mask": head_mask,
               "decoder_head_mask": decoder_head_mask,
               "cross_attn_head_mask": cross_attn_head_mask,
           }


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




           '''simple docstring'''

           __snake_case				=		ids_tensor([self.batch_size, self.encoder_seq_length]     ,    self.vocab_size						)
           __snake_case				=		ids_tensor([self.batch_size, self.decoder_seq_length]     ,    self.vocab_size						)

           # we need to clamp the input ids here to avoid having pad token in between
           # this is because for NllbMoe the position_ids are prepared such that
           # all pad tokens have pos id = 2 and rest are between 2..seq_length
           # and the seq_length here is seq_length - num_pad_tokens
           # but when using past, there is no way of knowing if the past input ids had
           # pad tokens in them, which results in incorrect seq_lenth and which in turn results in
           # position_ids being off by num_pad_tokens in past input
           __snake_case				=		input_ids.clamp(self.pad_token_id + 1						)
           __snake_case				=		decoder_input_ids.clamp(self.pad_token_id + 1						)

           __snake_case				=		self.get_config()
           __snake_case				=		config.num_attention_heads
           __snake_case				=		self.prepare_inputs_dict(SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE						)
           return config, input_dict


     def 							SCREAMING_SNAKE_CASE_  (						self	:		int						)			->	Tuple:




           '''simple docstring'''

           __snake_case		,					__snake_case				=		self.prepare_config_and_inputs()
           return config, inputs_dict


     def 							SCREAMING_SNAKE_CASE_  (						self	:		Any						)			->	List[str]:




           '''simple docstring'''

           return TaConfig(
               vocab_size=1_6_6     ,    d_model=self.hidden_size     ,    d_ff=self.d_ff     ,    d_kv=self.hidden_size // self.num_attention_heads     ,    num_layers=self.num_hidden_layers     ,    num_decoder_layers=self.decoder_layers     ,    num_heads=self.num_attention_heads     ,    relative_attention_num_buckets=self.relative_attention_num_buckets     ,    dropout_rate=self.dropout_rate     ,    initializer_factor=self.initializer_factor     ,    eos_token_id=self.eos_token_id     ,    bos_token_id=self.pad_token_id     ,    pad_token_id=self.pad_token_id     ,    decoder_start_token_id=self.decoder_start_token_id     ,    )


     def 							SCREAMING_SNAKE_CASE_  (						self	:		Optional[int]						)			->	List[Any]:




           '''simple docstring'''

           return TaConfig(
               vocab_size=self.vocab_size     ,    d_model=self.hidden_size     ,    d_ff=self.d_ff     ,    d_kv=self.hidden_size // self.num_attention_heads     ,    num_layers=self.num_hidden_layers     ,    num_decoder_layers=self.decoder_layers     ,    num_heads=self.num_attention_heads     ,    relative_attention_num_buckets=self.relative_attention_num_buckets     ,    dropout_rate=self.dropout_rate     ,    initializer_factor=self.initializer_factor     ,    eos_token_id=self.eos_token_id     ,    bos_token_id=self.pad_token_id     ,    pad_token_id=self.pad_token_id     ,    decoder_start_token_id=self.decoder_start_token_id     ,    )


     def 							SCREAMING_SNAKE_CASE_  (						self	:		List[Any]     ,    SCREAMING_SNAKE_CASE	:		Optional[int]     ,    SCREAMING_SNAKE_CASE	:		Any     ,    SCREAMING_SNAKE_CASE	:		List[Any]     ,    SCREAMING_SNAKE_CASE	:		Dict     ,    SCREAMING_SNAKE_CASE	:		Dict     ,    SCREAMING_SNAKE_CASE	:		List[Any]     ,    )			->	Optional[int]:




           '''simple docstring'''

           __snake_case				=		UMTaModel(config=SCREAMING_SNAKE_CASE						)
           model.to(SCREAMING_SNAKE_CASE						)
           model.eval()
           __snake_case				=		model(
               input_ids=SCREAMING_SNAKE_CASE     ,    decoder_input_ids=SCREAMING_SNAKE_CASE     ,    attention_mask=SCREAMING_SNAKE_CASE     ,    decoder_attention_mask=SCREAMING_SNAKE_CASE     ,    )
           __snake_case				=		model(input_ids=SCREAMING_SNAKE_CASE     ,    decoder_input_ids=SCREAMING_SNAKE_CASE						)
           __snake_case				=		result.last_hidden_state
           __snake_case				=		result.past_key_values
           __snake_case				=		result.encoder_last_hidden_state

           self.parent.assertEqual(encoder_output.size()     ,    (self.batch_size, self.encoder_seq_length, self.hidden_size)						)
           self.parent.assertEqual(decoder_output.size()     ,    (self.batch_size, self.decoder_seq_length, self.hidden_size)						)
           # There should be `num_layers` key value embeddings stored in decoder_past
           self.parent.assertEqual(len(SCREAMING_SNAKE_CASE						)     ,    config.num_layers						)
           # There should be a self attn key, a self attn value, a cross attn key and a cross attn value stored in each decoder_past tuple
           self.parent.assertEqual(len(decoder_past[0]						)     ,    4						)


     def 							SCREAMING_SNAKE_CASE_  (						self	:		Any     ,    SCREAMING_SNAKE_CASE	:		Optional[int]     ,    SCREAMING_SNAKE_CASE	:		Any     ,    SCREAMING_SNAKE_CASE	:		List[str]     ,    SCREAMING_SNAKE_CASE	:		Tuple     ,    SCREAMING_SNAKE_CASE	:		Union[str, Any]     ,    SCREAMING_SNAKE_CASE	:		Union[str, Any]     ,    )			->	str:




           '''simple docstring'''

           __snake_case				=		UMTaModel(config=SCREAMING_SNAKE_CASE						).get_decoder().to(SCREAMING_SNAKE_CASE						).eval()
           # first forward pass
           __snake_case				=		model(SCREAMING_SNAKE_CASE     ,    use_cache=SCREAMING_SNAKE_CASE						)
           __snake_case				=		model(SCREAMING_SNAKE_CASE						)
           __snake_case				=		model(SCREAMING_SNAKE_CASE     ,    use_cache=SCREAMING_SNAKE_CASE						)

           self.parent.assertTrue(len(SCREAMING_SNAKE_CASE						) == len(SCREAMING_SNAKE_CASE						)						)
           self.parent.assertTrue(len(SCREAMING_SNAKE_CASE						) == len(SCREAMING_SNAKE_CASE						) + 1						)

           __snake_case		,					__snake_case				=		outputs.to_tuple()

           # create hypothetical next token and extent to next_input_ids
           __snake_case				=		ids_tensor((self.batch_size, 1)     ,    config.vocab_size						)

           # append to next input_ids and
           __snake_case				=		torch.cat([input_ids, next_tokens]     ,    dim=-1						)

           __snake_case				=		model(SCREAMING_SNAKE_CASE						)["last_hidden_state"]
           __snake_case				=		model(SCREAMING_SNAKE_CASE     ,    past_key_values=SCREAMING_SNAKE_CASE						)["last_hidden_state"]

           # select random slice
           __snake_case				=		ids_tensor((1,)     ,    output_from_past.shape[-1]						).item()
           __snake_case				=		output_from_no_past[:, -1, random_slice_idx].detach()
           __snake_case				=		output_from_past[:, 0, random_slice_idx].detach()

           # test that outputs are equal for slice
           self.parent.assertTrue(torch.allclose(SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE     ,    atol=1e-3						)						)






     def 							SCREAMING_SNAKE_CASE_  (						self	:		Optional[Any]     ,    SCREAMING_SNAKE_CASE	:		Tuple     ,    SCREAMING_SNAKE_CASE	:		str     ,    )			->	Tuple:




           '''simple docstring'''

           __snake_case				=		UMTaModel(config=SCREAMING_SNAKE_CASE						).to(SCREAMING_SNAKE_CASE						).half().eval()
           __snake_case				=		model(**SCREAMING_SNAKE_CASE						)["last_hidden_state"]
           self.parent.assertFalse(torch.isnan(SCREAMING_SNAKE_CASE						).any().item()						)






@require_torch
class     UpperCamelCase(  _a     ,							_a     ,							_a     ,							unittest.TestCase   ):
     snake_case_		:				Optional[Any]										=	(
         (UMTaModel, UMTaForConditionalGeneration, UMTaForQuestionAnswering) if is_torch_available() else ()
     )
     snake_case_		:				Optional[Any]										=	(UMTaForConditionalGeneration,) if is_torch_available() else ()
     snake_case_		:				Optional[Any]										=	(
         {
             """conversational""": UMTaForConditionalGeneration,
             """feature-extraction""": UMTaModel,
             """summarization""": UMTaForConditionalGeneration,
             """text2text-generation""": UMTaForConditionalGeneration,
             """translation""": UMTaForConditionalGeneration,
             """question-answering""": UMTaForQuestionAnswering,
         }
         if is_torch_available()
         else {}
     )
     snake_case_		:				List[str]										=	True
     snake_case_		:				str										=	False
     snake_case_		:				int										=	False
     snake_case_		:				Dict										=	True
     snake_case_		:				str										=	True
     # The small UMT5 model needs higher percentages for CPU/MP tests
     snake_case_		:				List[Any]										=	[0.8, 0.9]


     def 							SCREAMING_SNAKE_CASE_  (						self	:		Optional[Any]						)			->	str:




           '''simple docstring'''

           __snake_case				=		UMTaModelTester(self						)


     @unittest.skip("Test has a segmentation fault on torch 1.8.0"						)
     def 							SCREAMING_SNAKE_CASE_  (						self	:		Any						)			->	Union[str, Any]:




           '''simple docstring'''

           __snake_case				=		self.model_tester.prepare_config_and_inputs()
           __snake_case				=		UMTaModel(config_and_inputs[0]						).to(SCREAMING_SNAKE_CASE						)
           with tempfile.TemporaryDirectory() as tmpdirname:
                 torch.onnx.export(
                     SCREAMING_SNAKE_CASE     ,    (config_and_inputs[1], config_and_inputs[3], config_and_inputs[2])     ,    f'''{tmpdirname}/t5_test.onnx'''     ,    export_params=SCREAMING_SNAKE_CASE     ,    opset_version=9     ,    input_names=["input_ids", "decoder_input_ids"]     ,    )


     @unittest.skipIf(torch_device == "cpu"     ,    "Cant do half precision"						)
     def 							SCREAMING_SNAKE_CASE_  (						self	:		Optional[int]						)			->	Optional[int]:




           '''simple docstring'''

           __snake_case				=		self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_model_fpaa_forward(*SCREAMING_SNAKE_CASE						)


     def 							SCREAMING_SNAKE_CASE_  (						self	:		Optional[int]						)			->	Union[str, Any]:




           '''simple docstring'''

           __snake_case				=		["encoder_attentions", "decoder_attentions", "cross_attentions"]
           __snake_case				=		self.model_tester.prepare_config_and_inputs()
           __snake_case				=		config_and_inputs[0]
           __snake_case				=		UMTaForConditionalGeneration(SCREAMING_SNAKE_CASE						).eval()
           model.to(SCREAMING_SNAKE_CASE						)

           __snake_case				=		{
               "head_mask": torch.zeros(config.num_layers     ,    config.num_heads     ,    device=SCREAMING_SNAKE_CASE						),
               "decoder_head_mask": torch.zeros(config.num_decoder_layers     ,    config.num_heads     ,    device=SCREAMING_SNAKE_CASE						),
               "cross_attn_head_mask": torch.zeros(config.num_decoder_layers     ,    config.num_heads     ,    device=SCREAMING_SNAKE_CASE						),
           }

           for attn_name, (name, mask) in zip(SCREAMING_SNAKE_CASE     ,    head_masking.items()						):
                 __snake_case				=		{name: mask}
                 # Explicitly pass decoder_head_mask as it is required from T5 model when head_mask specified
                 if name == "head_mask":
                       __snake_case				=		torch.ones(
                           config.num_decoder_layers     ,    config.num_heads     ,    device=SCREAMING_SNAKE_CASE						)

                 __snake_case				=		model.generate(
                     config_and_inputs[1]["input_ids"]     ,    num_beams=1     ,    max_length=3     ,    output_attentions=SCREAMING_SNAKE_CASE     ,    return_dict_in_generate=SCREAMING_SNAKE_CASE     ,    **SCREAMING_SNAKE_CASE     ,    )
                 # We check the state of decoder_attentions and cross_attentions just from the last step
                 __snake_case				=		out[attn_name] if attn_name == attention_names[0] else out[attn_name][-1]
                 self.assertEqual(sum([w.sum().item() for w in attn_weights]						)     ,    0.0						)






     @unittest.skip("Does not work on the tiny model as we keep hitting edge cases."						)
     def 							SCREAMING_SNAKE_CASE_  (						self	:		int						)			->	str:




           '''simple docstring'''

           pass






@require_torch
@require_sentencepiece
@require_tokenizers
class     UpperCamelCase(  unittest.TestCase   ):


     @slow
     @unittest.skip(
         "Unless we stop stripping left and right by default for all special tokens, the expected ids obtained here will not match the original ones. Wait for https://github.com/huggingface/transformers/pull/23909 to be merged"						)
     def 							SCREAMING_SNAKE_CASE_  (						self	:		Optional[int]						)			->	Union[str, Any]:




           '''simple docstring'''

           __snake_case				=		UMTaForConditionalGeneration.from_pretrained("google/umt5-small"     ,    return_dict=SCREAMING_SNAKE_CASE						).to(SCREAMING_SNAKE_CASE						)
           __snake_case				=		AutoTokenizer.from_pretrained("google/umt5-small"     ,    use_fast=SCREAMING_SNAKE_CASE     ,    legacy=SCREAMING_SNAKE_CASE						)
           __snake_case				=		[
               "Bonjour monsieur <extra_id_0> bien <extra_id_1>.",
               "No se como puedo <extra_id_0>.",
               "This is the reason why we <extra_id_0> them.",
               "The <extra_id_0> walks in <extra_id_1>, seats",
               "A <extra_id_0> walks into a bar and orders a <extra_id_1> with <extra_id_2> pinch of <extra_id_3>.",
           ]
           __snake_case				=		tokenizer(SCREAMING_SNAKE_CASE     ,    return_tensors="pt"     ,    padding=SCREAMING_SNAKE_CASE						).input_ids
           # fmt: off
           __snake_case				=		torch.tensor(
               [
                   [ 3_8_5_3_0, 2_1_0_7_0_3, 2_5_6_2_9_9, 1_4_1_0, 2_5_6_2_9_8, 2_7_4, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
                   [   8_2_6, 3_2_1, 6_7_1, 2_5_9_2_2, 2_5_6_2_9_9, 2_7_4, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
                   [  1_4_6_0, 3_3_9, 3_1_2, 1_9_0_1_4, 1_0_6_2_0, 7_5_8, 2_5_6_2_9_9, 2_3_5_5,2_7_4, 1, 0, 0, 0, 0, 0, 0,0, 0],
                   [   5_1_7, 2_5_6_2_9_9, 1_4_8_6_9, 2_8_1, 3_0_1, 2_5_6_2_9_8, 2_7_5, 1_1_9_9_8_3,1, 0, 0, 0, 0, 0, 0, 0,0, 0],
                   [   3_2_0, 2_5_6_2_9_9, 1_4_8_6_9, 2_8_1, 2_2_3_4, 2_8_9, 2_2_7_5, 3_3_3,6_1_3_9_1, 2_8_9, 2_5_6_2_9_8, 5_4_3, 2_5_6_2_9_7, 1_6_8_7_1_4, 3_2_9, 2_5_6_2_9_6,2_7_4, 1],
               ]						)
           # fmt: on
           torch.testing.assert_allclose(SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE						)

           __snake_case				=		model.generate(input_ids.to(SCREAMING_SNAKE_CASE						)						)
           __snake_case				=		[
               "<pad><extra_id_0> et<extra_id_1> [eod] <extra_id_2><extra_id_55>.. [eod] 💐 💐 💐 💐 💐 💐 💐 💐 💐 💐 💐 <extra_id_56>ajšietosto<extra_id_56>lleux<extra_id_19><extra_id_6>ajšie</s>",
               "<pad><extra_id_0>.<extra_id_1>.,<0x0A>...spech <0x0A><extra_id_20> <extra_id_21></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>",
               "<pad><extra_id_0> are not going to be a part of the world. We are not going to be a part of<extra_id_1> and<extra_id_2><0x0A><extra_id_48>.<extra_id_48></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>",
               "<pad><extra_id_0> door<extra_id_1>, the door<extra_id_2> 피해[/</s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>",
               "<pad><extra_id_0>nyone who<extra_id_1> drink<extra_id_2> a<extra_id_3> alcohol<extra_id_4> A<extra_id_5> A. This<extra_id_6> I<extra_id_7><extra_id_52><extra_id_53></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>",
           ]
           __snake_case				=		tokenizer.batch_decode(SCREAMING_SNAKE_CASE						)
           self.assertEqual(SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE						)









from collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...file_utils import TensorType, is_torch_available
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import logging


A							:					Tuple     						=     logging.get_logger(__name__)

A							:					int     						=     {
    'facebook/blenderbot_small-90M': 'https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/config.json',
    # See all BlenderbotSmall models at https://huggingface.co/models?filter=blenderbot_small
}






class     UpperCamelCase(  _a   ):
					snake_case_		:				str										=	"""blenderbot-small"""
					snake_case_		:				List[Any]										=	["""past_key_values"""]
					snake_case_		:				Any										=	{"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}


					def __init__(						self	:		Optional[Any]     ,    SCREAMING_SNAKE_CASE	:		Optional[Any]=5_0_2_6_5     ,    SCREAMING_SNAKE_CASE	:		Optional[int]=5_1_2     ,    SCREAMING_SNAKE_CASE	:		Any=8     ,    SCREAMING_SNAKE_CASE	:		int=2_0_4_8     ,    SCREAMING_SNAKE_CASE	:		Any=1_6     ,    SCREAMING_SNAKE_CASE	:		Tuple=8     ,    SCREAMING_SNAKE_CASE	:		List[Any]=2_0_4_8     ,    SCREAMING_SNAKE_CASE	:		Optional[int]=1_6     ,    SCREAMING_SNAKE_CASE	:		Optional[Any]=0.0     ,    SCREAMING_SNAKE_CASE	:		str=0.0     ,    SCREAMING_SNAKE_CASE	:		Optional[int]=True     ,    SCREAMING_SNAKE_CASE	:		int=True     ,    SCREAMING_SNAKE_CASE	:		Optional[Any]="gelu"     ,    SCREAMING_SNAKE_CASE	:		Dict=5_1_2     ,    SCREAMING_SNAKE_CASE	:		Any=0.1     ,    SCREAMING_SNAKE_CASE	:		Dict=0.0     ,    SCREAMING_SNAKE_CASE	:		Any=0.0     ,    SCREAMING_SNAKE_CASE	:		int=0.02     ,    SCREAMING_SNAKE_CASE	:		List[Any]=1     ,    SCREAMING_SNAKE_CASE	:		int=False     ,    SCREAMING_SNAKE_CASE	:		Union[str, Any]=0     ,    SCREAMING_SNAKE_CASE	:		str=1     ,    SCREAMING_SNAKE_CASE	:		Optional[int]=2     ,    SCREAMING_SNAKE_CASE	:		Any=2     ,    **SCREAMING_SNAKE_CASE	:		Dict     ,    )			->	Optional[int]:




											'''simple docstring'''

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

											super().__init__(
											    pad_token_id=SCREAMING_SNAKE_CASE     ,    bos_token_id=SCREAMING_SNAKE_CASE     ,    eos_token_id=SCREAMING_SNAKE_CASE     ,    is_encoder_decoder=SCREAMING_SNAKE_CASE     ,    decoder_start_token_id=SCREAMING_SNAKE_CASE     ,    forced_eos_token_id=SCREAMING_SNAKE_CASE     ,    **SCREAMING_SNAKE_CASE     ,    )






class     UpperCamelCase(  _a   ):


					@property
					def 							SCREAMING_SNAKE_CASE_  (						self	:		Optional[int]						)			->	Mapping[str, Mapping[int, str]]:




											'''simple docstring'''

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

																	if self.use_past:
																							__snake_case				=		{0: "batch"}
																							__snake_case				=		{0: "batch", 1: "past_decoder_sequence + sequence"}
																	else:
																							__snake_case				=		{0: "batch", 1: "decoder_sequence"}
																							__snake_case				=		{0: "batch", 1: "decoder_sequence"}

																	if self.use_past:
																							self.fill_with_past_key_values_(SCREAMING_SNAKE_CASE     ,    direction="inputs"						)
											elif self.task == "causal-lm":
																	# TODO: figure this case out.
																	__snake_case				=		OrderedDict(
																	    [
																	        ("input_ids", {0: "batch", 1: "encoder_sequence"}),
																	        ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
																	    ]						)
																	if self.use_past:
																							__snake_case		,					__snake_case				=		self.num_layers
																							for i in range(SCREAMING_SNAKE_CASE						):
																													__snake_case				=		{0: "batch", 2: "past_sequence + sequence"}
																													__snake_case				=		{0: "batch", 2: "past_sequence + sequence"}
											else:
																	__snake_case				=		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
					def 							SCREAMING_SNAKE_CASE_  (						self	:		str						)			->	Mapping[str, Mapping[int, str]]:




											'''simple docstring'''

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


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




											'''simple docstring'''

											__snake_case				=		self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
											    SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE						)

											# Generate decoder inputs
											__snake_case				=		seq_length if not self.use_past else 1
											__snake_case				=		self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
											    SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE						)
											__snake_case				=		{f'''decoder_{name}''': tensor for name, tensor in decoder_inputs.items()}
											__snake_case				=		dict(**SCREAMING_SNAKE_CASE     ,    **SCREAMING_SNAKE_CASE						)

											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				=		common_inputs["input_ids"].shape
																	__snake_case				=		common_inputs["decoder_input_ids"].shape[1]
																	__snake_case		,					__snake_case				=		self.num_attention_heads
																	__snake_case				=		(
																	    batch,
																	    num_encoder_attention_heads,
																	    encoder_seq_length,
																	    self._config.hidden_size // num_encoder_attention_heads,
																	)
																	__snake_case				=		decoder_seq_length + 3
																	__snake_case				=		(
																	    batch,
																	    num_decoder_attention_heads,
																	    decoder_past_length,
																	    self._config.hidden_size // num_decoder_attention_heads,
																	)

																	__snake_case				=		torch.cat(
																	    [common_inputs["decoder_attention_mask"], torch.ones(SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE						)]     ,    dim=1						)

																	__snake_case				=		[]
																	# If the number of encoder and decoder layers are present in the model configuration, both are considered
																	__snake_case		,					__snake_case				=		self.num_layers
																	__snake_case				=		min(SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE						)
																	__snake_case				=		max(SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE						) - min_num_layers
																	__snake_case				=		"encoder" if num_encoder_layers > num_decoder_layers else "decoder"

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


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




											'''simple docstring'''

											__snake_case				=		self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
											    SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE						)

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

																	__snake_case				=		common_inputs["attention_mask"].dtype
																	__snake_case				=		torch.cat(
																	    [common_inputs["attention_mask"], torch.ones(SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE     ,    dtype=SCREAMING_SNAKE_CASE						)]     ,    dim=1						)
																	__snake_case				=		[
																	    (torch.zeros(SCREAMING_SNAKE_CASE						), torch.zeros(SCREAMING_SNAKE_CASE						)) for _ in range(SCREAMING_SNAKE_CASE						)
																	]
											return common_inputs


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




											'''simple docstring'''

											__snake_case				=		compute_effective_axis_dimension(
											    SCREAMING_SNAKE_CASE     ,    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				=		tokenizer.num_special_tokens_to_add(SCREAMING_SNAKE_CASE						)
											__snake_case				=		compute_effective_axis_dimension(
											    SCREAMING_SNAKE_CASE     ,    fixed_dimension=OnnxConfig.default_fixed_sequence     ,    num_token_to_add=SCREAMING_SNAKE_CASE						)

											# Generate dummy inputs according to compute batch and sequence
											__snake_case				=		[" ".join([tokenizer.unk_token]						) * seq_length] * batch_size
											__snake_case				=		dict(tokenizer(SCREAMING_SNAKE_CASE     ,    return_tensors=SCREAMING_SNAKE_CASE						)						)
											return common_inputs


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




											'''simple docstring'''

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

											elif self.task == "causal-lm":
																	__snake_case				=		self._generate_dummy_inputs_for_causal_lm(
																	    SCREAMING_SNAKE_CASE     ,    batch_size=SCREAMING_SNAKE_CASE     ,    seq_length=SCREAMING_SNAKE_CASE     ,    is_pair=SCREAMING_SNAKE_CASE     ,    framework=SCREAMING_SNAKE_CASE						)
											else:
																	__snake_case				=		self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
																	    SCREAMING_SNAKE_CASE     ,    batch_size=SCREAMING_SNAKE_CASE     ,    seq_length=SCREAMING_SNAKE_CASE     ,    is_pair=SCREAMING_SNAKE_CASE     ,    framework=SCREAMING_SNAKE_CASE						)

											return common_inputs






					def 							SCREAMING_SNAKE_CASE_  (						self	:		int     ,    SCREAMING_SNAKE_CASE	:		Any     ,    SCREAMING_SNAKE_CASE	:		int     ,    SCREAMING_SNAKE_CASE	:		List[Any]     ,    SCREAMING_SNAKE_CASE	:		int						)			->	Union[str, Any]:




											'''simple docstring'''

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





