






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 						_snake_case     :






			def __init__(				self						,    a						,    a=99						,    a=13						,    a=7						,    a=9						,    a=True						,    a=True						,    a=False						,    a=32						,    a=5						,    a=4						,    a=37						,    a=8						,    a=0.1						,    a=0.0_02						,    a=1						,    a=0						,    a=0						,    a=None						,    a=None						,    )					->				List[str]:
						SCREAMING_SNAKE_CASE						 =       parent
						SCREAMING_SNAKE_CASE						 =       batch_size
						SCREAMING_SNAKE_CASE						 =       encoder_seq_length
						SCREAMING_SNAKE_CASE						 =       decoder_seq_length
						# For common tests
						SCREAMING_SNAKE_CASE						 =       self.decoder_seq_length
						SCREAMING_SNAKE_CASE						 =       is_training
						SCREAMING_SNAKE_CASE						 =       use_attention_mask
						SCREAMING_SNAKE_CASE						 =       use_labels
						SCREAMING_SNAKE_CASE						 =       vocab_size
						SCREAMING_SNAKE_CASE						 =       hidden_size
						SCREAMING_SNAKE_CASE						 =       num_hidden_layers
						SCREAMING_SNAKE_CASE						 =       num_attention_heads
						SCREAMING_SNAKE_CASE						 =       d_ff
						SCREAMING_SNAKE_CASE						 =       relative_attention_num_buckets
						SCREAMING_SNAKE_CASE						 =       dropout_rate
						SCREAMING_SNAKE_CASE						 =       initializer_factor
						SCREAMING_SNAKE_CASE						 =       eos_token_id
						SCREAMING_SNAKE_CASE						 =       pad_token_id
						SCREAMING_SNAKE_CASE						 =       decoder_start_token_id
						SCREAMING_SNAKE_CASE						 =       None
						SCREAMING_SNAKE_CASE						 =       decoder_layers






			def       SCREAMING_SNAKE_CASE__			(				self)					->				str:
						return TaConfig.from_pretrained('google/umt5-base')






			def       SCREAMING_SNAKE_CASE__			(				self						,    a						,    a						,    a						,    a=None						,    a=None						,    a=None						,    a=None						,    a=None						,    )					->				str:







						if attention_mask is None:
									SCREAMING_SNAKE_CASE						 =       input_ids.ne(config.pad_token_id)
						if decoder_attention_mask is None:
									SCREAMING_SNAKE_CASE						 =       decoder_input_ids.ne(config.pad_token_id)
						if head_mask is None:
									SCREAMING_SNAKE_CASE						 =       torch.ones(config.num_hidden_layers						,    config.num_attention_heads						,    device=a)
						if decoder_head_mask is None:
									SCREAMING_SNAKE_CASE						 =       torch.ones(config.num_decoder_layers						,    config.num_attention_heads						,    device=a)
						if cross_attn_head_mask is None:
									SCREAMING_SNAKE_CASE						 =       torch.ones(
									    config.num_decoder_layers						,    config.num_attention_heads						,    device=a)
						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)					->				Optional[int]:
						SCREAMING_SNAKE_CASE						 =       ids_tensor([self.batch_size, self.encoder_seq_length]						,    self.vocab_size)
						SCREAMING_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
						SCREAMING_SNAKE_CASE						 =       input_ids.clamp(self.pad_token_id + 1)
						SCREAMING_SNAKE_CASE						 =       decoder_input_ids.clamp(self.pad_token_id + 1)

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






			def       SCREAMING_SNAKE_CASE__			(				self)					->				Optional[int]:
						SCREAMING_SNAKE_CASE ,  SCREAMING_SNAKE_CASE						 =       self.prepare_config_and_inputs()
						return config, inputs_dict






			def       SCREAMING_SNAKE_CASE__			(				self)					->				Union[str, 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       SCREAMING_SNAKE_CASE__			(				self)					->				Optional[int]:
						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						,    a						,    a						,    a						,    a						,    a						,    a						,    )					->				Dict:
						SCREAMING_SNAKE_CASE						 =       UMTaModel(config=a)
						model.to(a)
						model.eval()
						SCREAMING_SNAKE_CASE						 =       model(
						    input_ids=a						,    decoder_input_ids=a						,    attention_mask=a						,    decoder_attention_mask=a						,    )
						SCREAMING_SNAKE_CASE						 =       model(input_ids=a						,    decoder_input_ids=a)
						SCREAMING_SNAKE_CASE						 =       result.last_hidden_state
						SCREAMING_SNAKE_CASE						 =       result.past_key_values
						SCREAMING_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(a)						,    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						,    a						,    a						,    a						,    a						,    a						,    a						,    )					->				List[str]:
						SCREAMING_SNAKE_CASE						 =       UMTaModel(config=a).get_decoder().to(a).eval()
						# first forward pass
						SCREAMING_SNAKE_CASE						 =       model(a						,    use_cache=a)
						SCREAMING_SNAKE_CASE						 =       model(a)
						SCREAMING_SNAKE_CASE						 =       model(a						,    use_cache=a)

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

						SCREAMING_SNAKE_CASE ,  SCREAMING_SNAKE_CASE						 =       outputs.to_tuple()

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

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

						SCREAMING_SNAKE_CASE						 =       model(a)['last_hidden_state']
						SCREAMING_SNAKE_CASE						 =       model(a						,    past_key_values=a)['last_hidden_state']

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

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







			def       SCREAMING_SNAKE_CASE__			(				self						,    a						,    a						,    )					->				Optional[int]:
						SCREAMING_SNAKE_CASE						 =       UMTaModel(config=a).to(a).half().eval()
						SCREAMING_SNAKE_CASE						 =       model(**a)['last_hidden_state']
						self.parent.assertFalse(torch.isnan(a).any().item())



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






			def       SCREAMING_SNAKE_CASE__			(				self)					->				Union[str, Any]:
						SCREAMING_SNAKE_CASE						 =       UMTaModelTester(self)






			@unittest.skip('Test has a segmentation fault on torch 1.8.0')
			def       SCREAMING_SNAKE_CASE__			(				self)					->				Optional[Any]:
						SCREAMING_SNAKE_CASE						 =       self.model_tester.prepare_config_and_inputs()
						SCREAMING_SNAKE_CASE						 =       UMTaModel(config_and_inputs[0]).to(a)
						with tempfile.TemporaryDirectory() as tmpdirname:
									torch.onnx.export(
									    a						,    (config_and_inputs[1], config_and_inputs[3], config_and_inputs[2])						,    f'''{tmpdirname}/t5_test.onnx'''						,    export_params=a						,    opset_version=9						,    input_names=['input_ids', 'decoder_input_ids']						,    )






			@unittest.skipIf(torch_device == 'cpu'						,    'Cant do half precision')
			def       SCREAMING_SNAKE_CASE__			(				self)					->				str:
						SCREAMING_SNAKE_CASE						 =       self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_model_fpaa_forward(*a)






			def       SCREAMING_SNAKE_CASE__			(				self)					->				Dict:
						SCREAMING_SNAKE_CASE						 =       ['encoder_attentions', 'decoder_attentions', 'cross_attentions']
						SCREAMING_SNAKE_CASE						 =       self.model_tester.prepare_config_and_inputs()
						SCREAMING_SNAKE_CASE						 =       config_and_inputs[0]
						SCREAMING_SNAKE_CASE						 =       UMTaForConditionalGeneration(a).eval()
						model.to(a)

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

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

									SCREAMING_SNAKE_CASE						 =       model.generate(
									    config_and_inputs[1]['input_ids']						,    num_beams=1						,    max_length=3						,    output_attentions=a						,    return_dict_in_generate=a						,    **a						,    )
									# We check the state of decoder_attentions and cross_attentions just from the last step
									SCREAMING_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)					->				Dict:
						pass



@require_torch
@require_sentencepiece
@require_tokenizers
class 						_snake_case     (     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)					->				Tuple:
						SCREAMING_SNAKE_CASE						 =       UMTaForConditionalGeneration.from_pretrained('google/umt5-small'						,    return_dict=a).to(a)
						SCREAMING_SNAKE_CASE						 =       AutoTokenizer.from_pretrained('google/umt5-small'						,    use_fast=a						,    legacy=a)
						SCREAMING_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>.',
						]
						SCREAMING_SNAKE_CASE						 =       tokenizer(a						,    return_tensors='pt'						,    padding=a).input_ids
						# fmt: off
						SCREAMING_SNAKE_CASE						 =       torch.tensor(
						    [
						        [ 3_8530, 21_0703, 25_6299, 1410, 25_6298, 274, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
						        [   826, 321, 671, 2_5922, 25_6299, 274, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
						        [  1460, 339, 312, 1_9014, 1_0620, 758, 25_6299, 2355,274, 1, 0, 0, 0, 0, 0, 0,0, 0],
						        [   517, 25_6299, 1_4869, 281, 301, 25_6298, 275, 11_9983,1, 0, 0, 0, 0, 0, 0, 0,0, 0],
						        [   320, 25_6299, 1_4869, 281, 2234, 289, 2275, 333,6_1391, 289, 25_6298, 543, 25_6297, 16_8714, 329, 25_6296,274, 1],
						    ])
						# fmt: on
						torch.testing.assert_allclose(a						,    a)

						SCREAMING_SNAKE_CASE						 =       model.generate(input_ids.to(a))
						SCREAMING_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>',
						]
						SCREAMING_SNAKE_CASE						 =       tokenizer.batch_decode(a)
						self.assertEqual(a						,    a)










from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__UpperCamelCase					:       Optional[Any]    					=      {
    "configuration_jukebox": [
        "JUKEBOX_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "JukeboxConfig",
        "JukeboxPriorConfig",
        "JukeboxVQVAEConfig",
    ],
    "tokenization_jukebox": ["JukeboxTokenizer"],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__UpperCamelCase					:       Tuple    					=      [
		    "JUKEBOX_PRETRAINED_MODEL_ARCHIVE_LIST",
		    "JukeboxModel",
		    "JukeboxPreTrainedModel",
		    "JukeboxVQVAE",
		    "JukeboxPrior",
		]

if TYPE_CHECKING:
		from .configuration_jukebox import (
		    JUKEBOX_PRETRAINED_CONFIG_ARCHIVE_MAP,
		    JukeboxConfig,
		    JukeboxPriorConfig,
		    JukeboxVQVAEConfig,
		)
		from .tokenization_jukebox import JukeboxTokenizer

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_jukebox import (
				    JUKEBOX_PRETRAINED_MODEL_ARCHIVE_LIST,
				    JukeboxModel,
				    JukeboxPreTrainedModel,
				    JukeboxPrior,
				    JukeboxVQVAE,
				)

else:
		import sys

		__UpperCamelCase					:       List[Any]    					=      _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

