






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 			lowerCAmelCase_							:




							def __init__( self  : Any			,				SCREAMING_SNAKE_CASE_  : Optional[Any]			,				SCREAMING_SNAKE_CASE_  : Optional[Any]=99			,				SCREAMING_SNAKE_CASE_  : Any=13			,				SCREAMING_SNAKE_CASE_  : Optional[int]=7			,				SCREAMING_SNAKE_CASE_  : Tuple=9			,				SCREAMING_SNAKE_CASE_  : List[Any]=True			,				SCREAMING_SNAKE_CASE_  : Union[str, Any]=True			,				SCREAMING_SNAKE_CASE_  : List[str]=False			,				SCREAMING_SNAKE_CASE_  : Optional[Any]=32			,				SCREAMING_SNAKE_CASE_  : int=5			,				SCREAMING_SNAKE_CASE_  : Any=4			,				SCREAMING_SNAKE_CASE_  : Union[str, Any]=37			,				SCREAMING_SNAKE_CASE_  : Optional[Any]=8			,				SCREAMING_SNAKE_CASE_  : List[Any]=0.1			,				SCREAMING_SNAKE_CASE_  : List[Any]=0.002			,				SCREAMING_SNAKE_CASE_  : str=1			,				SCREAMING_SNAKE_CASE_  : Union[str, Any]=0			,				SCREAMING_SNAKE_CASE_  : Union[str, Any]=0			,				SCREAMING_SNAKE_CASE_  : Any=None			,				SCREAMING_SNAKE_CASE_  : List[Any]=None			,				):
											lowerCAmelCase__         =    parent
											lowerCAmelCase__         =    batch_size
											lowerCAmelCase__         =    encoder_seq_length
											lowerCAmelCase__         =    decoder_seq_length
											# For common tests
											lowerCAmelCase__         =    self.decoder_seq_length
											lowerCAmelCase__         =    is_training
											lowerCAmelCase__         =    use_attention_mask
											lowerCAmelCase__         =    use_labels
											lowerCAmelCase__         =    vocab_size
											lowerCAmelCase__         =    hidden_size
											lowerCAmelCase__         =    num_hidden_layers
											lowerCAmelCase__         =    num_attention_heads
											lowerCAmelCase__         =    d_ff
											lowerCAmelCase__         =    relative_attention_num_buckets
											lowerCAmelCase__         =    dropout_rate
											lowerCAmelCase__         =    initializer_factor
											lowerCAmelCase__         =    eos_token_id
											lowerCAmelCase__         =    pad_token_id
											lowerCAmelCase__         =    decoder_start_token_id
											lowerCAmelCase__         =    None
											lowerCAmelCase__         =    decoder_layers




							def     __snake_case       ( self  : Optional[int]						):
											return TaConfig.from_pretrained('''google/umt5-base'''						)




							def     __snake_case       ( self  : Tuple			,				SCREAMING_SNAKE_CASE_  : Dict			,				SCREAMING_SNAKE_CASE_  : Any			,				SCREAMING_SNAKE_CASE_  : int			,				SCREAMING_SNAKE_CASE_  : Tuple=None			,				SCREAMING_SNAKE_CASE_  : Tuple=None			,				SCREAMING_SNAKE_CASE_  : str=None			,				SCREAMING_SNAKE_CASE_  : int=None			,				SCREAMING_SNAKE_CASE_  : int=None			,				):
											if attention_mask is None:
															lowerCAmelCase__         =    input_ids.ne(config.pad_token_id						)
											if decoder_attention_mask is None:
															lowerCAmelCase__         =    decoder_input_ids.ne(config.pad_token_id						)
											if head_mask is None:
															lowerCAmelCase__         =    torch.ones(config.num_hidden_layers			,				config.num_attention_heads			,				device=SCREAMING_SNAKE_CASE_						)
											if decoder_head_mask is None:
															lowerCAmelCase__         =    torch.ones(config.num_decoder_layers			,				config.num_attention_heads			,				device=SCREAMING_SNAKE_CASE_						)
											if cross_attn_head_mask is None:
															lowerCAmelCase__         =    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     __snake_case       ( self  : List[Any]						):
											lowerCAmelCase__         =    ids_tensor([self.batch_size, self.encoder_seq_length]			,				self.vocab_size						)
											lowerCAmelCase__         =    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
											lowerCAmelCase__         =    input_ids.clamp(self.pad_token_id + 1						)
											lowerCAmelCase__         =    decoder_input_ids.clamp(self.pad_token_id + 1						)

											lowerCAmelCase__         =    self.get_config()
											lowerCAmelCase__         =    config.num_attention_heads
											lowerCAmelCase__         =    self.prepare_inputs_dict(SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_						)
											return config, input_dict




							def     __snake_case       ( self  : Dict						):
											lowerCAmelCase__      ,		lowerCAmelCase__         =    self.prepare_config_and_inputs()
											return config, inputs_dict




							def     __snake_case       ( self  : Any						):
											return TaConfig(
											    vocab_size=166			,				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     __snake_case       ( self  : Tuple						):
											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     __snake_case       ( self  : Any			,				SCREAMING_SNAKE_CASE_  : List[str]			,				SCREAMING_SNAKE_CASE_  : Union[str, Any]			,				SCREAMING_SNAKE_CASE_  : Dict			,				SCREAMING_SNAKE_CASE_  : Union[str, Any]			,				SCREAMING_SNAKE_CASE_  : Optional[Any]			,				SCREAMING_SNAKE_CASE_  : Any			,				):
											lowerCAmelCase__         =    UMTaModel(config=SCREAMING_SNAKE_CASE_						)
											model.to(SCREAMING_SNAKE_CASE_						)
											model.eval()
											lowerCAmelCase__         =    model(
											    input_ids=SCREAMING_SNAKE_CASE_			,				decoder_input_ids=SCREAMING_SNAKE_CASE_			,				attention_mask=SCREAMING_SNAKE_CASE_			,				decoder_attention_mask=SCREAMING_SNAKE_CASE_			,				)
											lowerCAmelCase__         =    model(input_ids=SCREAMING_SNAKE_CASE_			,				decoder_input_ids=SCREAMING_SNAKE_CASE_						)
											lowerCAmelCase__         =    result.last_hidden_state
											lowerCAmelCase__         =    result.past_key_values
											lowerCAmelCase__         =    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     __snake_case       ( self  : Any			,				SCREAMING_SNAKE_CASE_  : str			,				SCREAMING_SNAKE_CASE_  : Union[str, Any]			,				SCREAMING_SNAKE_CASE_  : List[str]			,				SCREAMING_SNAKE_CASE_  : int			,				SCREAMING_SNAKE_CASE_  : Dict			,				SCREAMING_SNAKE_CASE_  : Any			,				):
											lowerCAmelCase__         =    UMTaModel(config=SCREAMING_SNAKE_CASE_						).get_decoder().to(SCREAMING_SNAKE_CASE_						).eval()
											# first forward pass
											lowerCAmelCase__         =    model(SCREAMING_SNAKE_CASE_			,				use_cache=SCREAMING_SNAKE_CASE_						)
											lowerCAmelCase__         =    model(SCREAMING_SNAKE_CASE_						)
											lowerCAmelCase__         =    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						)

											lowerCAmelCase__      ,		lowerCAmelCase__         =    outputs.to_tuple()

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

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

											lowerCAmelCase__         =    model(SCREAMING_SNAKE_CASE_						)['''last_hidden_state''']
											lowerCAmelCase__         =    model(SCREAMING_SNAKE_CASE_			,				past_key_values=SCREAMING_SNAKE_CASE_						)['''last_hidden_state''']

											# select random slice
											lowerCAmelCase__         =    ids_tensor((1,)			,				output_from_past.shape[-1]						).item()
											lowerCAmelCase__         =    output_from_no_past[:, -1, random_slice_idx].detach()
											lowerCAmelCase__         =    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     __snake_case       ( self  : Any			,				SCREAMING_SNAKE_CASE_  : Optional[Any]			,				SCREAMING_SNAKE_CASE_  : int			,				):
											lowerCAmelCase__         =    UMTaModel(config=SCREAMING_SNAKE_CASE_						).to(SCREAMING_SNAKE_CASE_						).half().eval()
											lowerCAmelCase__         =    model(**SCREAMING_SNAKE_CASE_						)['''last_hidden_state''']
											self.parent.assertFalse(torch.isnan(SCREAMING_SNAKE_CASE_						).any().item()						)



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




							def     __snake_case       ( self  : Tuple						):
											lowerCAmelCase__         =    UMTaModelTester(self						)




							@unittest.skip('''Test has a segmentation fault on torch 1.8.0'''						)
							def     __snake_case       ( self  : Any						):
											lowerCAmelCase__         =    self.model_tester.prepare_config_and_inputs()
											lowerCAmelCase__         =    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     __snake_case       ( self  : int						):
											lowerCAmelCase__         =    self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_model_fpaa_forward(*SCREAMING_SNAKE_CASE_						)




							def     __snake_case       ( self  : Any						):
											lowerCAmelCase__         =    ['''encoder_attentions''', '''decoder_attentions''', '''cross_attentions''']
											lowerCAmelCase__         =    self.model_tester.prepare_config_and_inputs()
											lowerCAmelCase__         =    config_and_inputs[0]
											lowerCAmelCase__         =    UMTaForConditionalGeneration(SCREAMING_SNAKE_CASE_						).eval()
											model.to(SCREAMING_SNAKE_CASE_						)

											lowerCAmelCase__         =    {
											    '''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()						):
															lowerCAmelCase__         =    {name: mask}
															# Explicitly pass decoder_head_mask as it is required from T5 model when head_mask specified
															if name == "head_mask":
																			lowerCAmelCase__         =    torch.ones(
																			    config.num_decoder_layers			,				config.num_heads			,				device=SCREAMING_SNAKE_CASE_						)

															lowerCAmelCase__         =    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
															lowerCAmelCase__         =    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     __snake_case       ( self  : Optional[Any]						):
											pass



@require_torch
@require_sentencepiece
@require_tokenizers
class 			lowerCAmelCase_							(					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     __snake_case       ( self  : Optional[Any]						):
											lowerCAmelCase__         =    UMTaForConditionalGeneration.from_pretrained('''google/umt5-small'''			,				return_dict=SCREAMING_SNAKE_CASE_						).to(SCREAMING_SNAKE_CASE_						)
											lowerCAmelCase__         =    AutoTokenizer.from_pretrained('''google/umt5-small'''			,				use_fast=SCREAMING_SNAKE_CASE_			,				legacy=SCREAMING_SNAKE_CASE_						)
											lowerCAmelCase__         =    [
											    '''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>.''',
											]
											lowerCAmelCase__         =    tokenizer(SCREAMING_SNAKE_CASE_			,				return_tensors='''pt'''			,				padding=SCREAMING_SNAKE_CASE_						).input_ids
											# fmt: off
											lowerCAmelCase__         =    torch.tensor(
											    [
											        [ 38_530, 210_703, 256_299, 1_410, 256_298, 274, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
											        [   826, 321, 671, 25_922, 256_299, 274, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
											        [  1_460, 339, 312, 19_014, 10_620, 758, 256_299, 2_355,274, 1, 0, 0, 0, 0, 0, 0,0, 0],
											        [   517, 256_299, 14_869, 281, 301, 256_298, 275, 119_983,1, 0, 0, 0, 0, 0, 0, 0,0, 0],
											        [   320, 256_299, 14_869, 281, 2_234, 289, 2_275, 333,61_391, 289, 256_298, 543, 256_297, 168_714, 329, 256_296,274, 1],
											    ]						)
											# fmt: on
											torch.testing.assert_allclose(SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_						)

											lowerCAmelCase__         =    model.generate(input_ids.to(SCREAMING_SNAKE_CASE_						)						)
											lowerCAmelCase__         =    [
											    '''<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>''',
											]
											lowerCAmelCase__         =    tokenizer.batch_decode(SCREAMING_SNAKE_CASE_						)
											self.assertEqual(SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_						)







from ...configuration_utils import PretrainedConfig
from ...utils import logging


__magic_name__  							=	logging.get_logger(__name__)

__magic_name__  							=	{
    '''microsoft/biogpt''': '''https://huggingface.co/microsoft/biogpt/resolve/main/config.json''',
    # See all BioGPT models at https://huggingface.co/models?filter=biogpt
}




class        _SCREAMING_SNAKE_CASE	(   __UpperCamelCase			):
      _A    :		Dict     						=  'biogpt'
      def __init__(      self		,						lowerCamelCase=4_23_84		,						lowerCamelCase=10_24		,						lowerCamelCase=24		,						lowerCamelCase=16		,						lowerCamelCase=40_96		,						lowerCamelCase="gelu"		,						lowerCamelCase=0.1		,						lowerCamelCase=0.1		,						lowerCamelCase=10_24		,						lowerCamelCase=0.0_2		,						lowerCamelCase=1e-12		,						lowerCamelCase=True		,						lowerCamelCase=True		,						lowerCamelCase=0.0		,						lowerCamelCase=0.0		,						lowerCamelCase=1		,						lowerCamelCase=0		,						lowerCamelCase=2		,						**lowerCamelCase		,						):
          snake_case__    =					vocab_size
          snake_case__    =					max_position_embeddings
          snake_case__    =					hidden_size
          snake_case__    =					num_hidden_layers
          snake_case__    =					num_attention_heads
          snake_case__    =					intermediate_size
          snake_case__    =					hidden_act
          snake_case__    =					hidden_dropout_prob
          snake_case__    =					attention_probs_dropout_prob
          snake_case__    =					initializer_range
          snake_case__    =					layer_norm_eps
          snake_case__    =					scale_embedding
          snake_case__    =					use_cache
          snake_case__    =					layerdrop
          snake_case__    =					activation_dropout
          super().__init__(pad_token_id=lowerCamelCase		,						bos_token_id=lowerCamelCase		,						eos_token_id=lowerCamelCase		,						**lowerCamelCase			)



