





'''simple docstring'''

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[str]							,			A__      :	str							,			A__      :	Optional[int]=9_9							,			A__      :	Union[str, Any]=1_3							,			A__      :	Optional[Any]=7							,			A__      :	Optional[Any]=9							,			A__      :	Optional[int]=True							,			A__      :	Any=True							,			A__      :	Optional[int]=False							,			A__      :	Optional[Any]=3_2							,			A__      :	List[Any]=5							,			A__      :	Any=4							,			A__      :	Any=3_7							,			A__      :	Union[str, Any]=8							,			A__      :	List[Any]=0.1							,			A__      :	Union[str, Any]=0.0_0_2							,			A__      :	Optional[Any]=1							,			A__      :	int=0							,			A__      :	Tuple=0							,			A__      :	int=None							,			A__      :	Dict=None							,			)					-> int:
						lowercase         = parent
						lowercase         = batch_size
						lowercase         = encoder_seq_length
						lowercase         = decoder_seq_length
						# For common tests
						lowercase         = self.decoder_seq_length
						lowercase         = is_training
						lowercase         = use_attention_mask
						lowercase         = use_labels
						lowercase         = vocab_size
						lowercase         = hidden_size
						lowercase         = num_hidden_layers
						lowercase         = num_attention_heads
						lowercase         = d_ff
						lowercase         = relative_attention_num_buckets
						lowercase         = dropout_rate
						lowercase         = initializer_factor
						lowercase         = eos_token_id
						lowercase         = pad_token_id
						lowercase         = decoder_start_token_id
						lowercase         = None
						lowercase         = decoder_layers




		def 				UpperCAmelCase__   (self      :	Dict				)					-> List[Any]:
						return TaConfig.from_pretrained("google/umt5-base"				)




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

						lowercase         = self.get_config()
						lowercase         = config.num_attention_heads
						lowercase         = self.prepare_inputs_dict(A__							,			A__							,			A__				)
						return config, input_dict




		def 				UpperCAmelCase__   (self      :	Any				)					-> str:
						lowercase   ,							lowercase         = self.prepare_config_and_inputs()
						return config, inputs_dict




		def 				UpperCAmelCase__   (self      :	List[Any]				)					-> Optional[int]:
						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 				UpperCAmelCase__   (self      :	List[Any]				)					-> Dict:
						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 				UpperCAmelCase__   (self      :	Optional[int]							,			A__      :	List[Any]							,			A__      :	List[Any]							,			A__      :	Optional[Any]							,			A__      :	Any							,			A__      :	Optional[int]							,			A__      :	Optional[int]							,			)					-> List[Any]:
						lowercase         = UMTaModel(config=A__				)
						model.to(A__				)
						model.eval()
						lowercase         = model(
						    input_ids=A__							,			decoder_input_ids=A__							,			attention_mask=A__							,			decoder_attention_mask=A__							,			)
						lowercase         = model(input_ids=A__							,			decoder_input_ids=A__				)
						lowercase         = result.last_hidden_state
						lowercase         = result.past_key_values
						lowercase         = 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 				UpperCAmelCase__   (self      :	int							,			A__      :	int							,			A__      :	Optional[Any]							,			A__      :	Optional[int]							,			A__      :	List[str]							,			A__      :	int							,			A__      :	Dict							,			)					-> List[Any]:
						lowercase         = UMTaModel(config=A__				).get_decoder().to(A__				).eval()
						# first forward pass
						lowercase         = model(A__							,			use_cache=A__				)
						lowercase         = model(A__				)
						lowercase         = model(A__							,			use_cache=A__				)

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

						lowercase   ,							lowercase         = outputs.to_tuple()

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

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

						lowercase         = model(A__				)["last_hidden_state"]
						lowercase         = model(A__							,			past_key_values=A__				)["last_hidden_state"]

						# select random slice
						lowercase         = ids_tensor((1,)							,			output_from_past.shape[-1]				).item()
						lowercase         = output_from_no_past[:, -1, random_slice_idx].detach()
						lowercase         = 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 				UpperCAmelCase__   (self      :	int							,			A__      :	Tuple							,			A__      :	Union[str, Any]							,			)					-> List[str]:
						lowercase         = UMTaModel(config=A__				).to(A__				).half().eval()
						lowercase         = model(**A__				)["last_hidden_state"]
						self.parent.assertFalse(torch.isnan(A__				).any().item()				)




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




		def 				UpperCAmelCase__   (self      :	int				)					-> Dict:
						lowercase         = UMTaModelTester(self				)




		@unittest.skip("Test has a segmentation fault on torch 1.8.0"				)
		def 				UpperCAmelCase__   (self      :	Dict				)					-> Optional[int]:
						lowercase         = self.model_tester.prepare_config_and_inputs()
						lowercase         = 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 				UpperCAmelCase__   (self      :	str				)					-> Tuple:
						lowercase         = self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_model_fpaa_forward(*A__				)




		def 				UpperCAmelCase__   (self      :	List[str]				)					-> Dict:
						lowercase         = ["encoder_attentions", "decoder_attentions", "cross_attentions"]
						lowercase         = self.model_tester.prepare_config_and_inputs()
						lowercase         = config_and_inputs[0]
						lowercase         = UMTaForConditionalGeneration(A__				).eval()
						model.to(A__				)

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

										lowercase         = 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
										lowercase         = 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 				UpperCAmelCase__   (self      :	Tuple				)					-> List[Any]:
						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 				UpperCAmelCase__   (self      :	List[Any]				)					-> List[Any]:
						lowercase         = UMTaForConditionalGeneration.from_pretrained("google/umt5-small"							,			return_dict=A__				).to(A__				)
						lowercase         = AutoTokenizer.from_pretrained("google/umt5-small"							,			use_fast=A__							,			legacy=A__				)
						lowercase         = [
						    "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>.",
						]
						lowercase         = tokenizer(A__							,			return_tensors="pt"							,			padding=A__				).input_ids
						# fmt: off
						lowercase         = 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(A__							,			A__				)

						lowercase         = model.generate(input_ids.to(A__				)				)
						lowercase         = [
						    "<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>",
						]
						lowercase         = tokenizer.batch_decode(A__				)
						self.assertEqual(A__							,			A__				)











'''simple docstring'''

import warnings
from contextlib import contextmanager

from ....processing_utils import ProcessorMixin

class       UpperCAmelCase (						_lowercase  ):
		UpperCAmelCase					:	Optional[Any] 			=		'''MCTCTFeatureExtractor'''
		UpperCAmelCase					:	Tuple 			=		'''AutoTokenizer'''




		def __init__(self      :	int							,			A__      :	Tuple							,			A__      :	Union[str, Any]				)					-> Dict:
						super().__init__(A__							,			A__				)
						lowercase         = self.feature_extractor
						lowercase         = False




		def __call__(self      :	Tuple							,			*A__      :	str							,			**A__      :	Dict				)					-> int:
						# For backward compatibility
						if self._in_target_context_manager:
										return self.current_processor(*A__							,			**A__				)

						if "raw_speech" in kwargs:
										warnings.warn("Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead."				)
										lowercase         = kwargs.pop("raw_speech"				)
						else:
										lowercase         = kwargs.pop("audio"							,			A__				)
						lowercase         = kwargs.pop("sampling_rate"							,			A__				)
						lowercase         = kwargs.pop("text"							,			A__				)
						if len(A__				) > 0:
										lowercase         = args[0]
										lowercase         = args[1:]

						if audio is None and text is None:
										raise ValueError("You need to specify either an `audio` or `text` input to process."				)

						if audio is not None:
										lowercase         = self.feature_extractor(A__							,			*A__							,			sampling_rate=A__							,			**A__				)
						if text is not None:
										lowercase         = self.tokenizer(A__							,			**A__				)

						if text is None:
										return inputs
						elif audio is None:
										return encodings
						else:
										lowercase         = encodings["input_ids"]
										return inputs




		def 				UpperCAmelCase__   (self      :	Tuple							,			*A__      :	str							,			**A__      :	str				)					-> str:
						return self.tokenizer.batch_decode(*A__							,			**A__				)




		def 				UpperCAmelCase__   (self      :	Any							,			*A__      :	List[Any]							,			**A__      :	List[str]				)					-> Tuple:
						# For backward compatibility
						if self._in_target_context_manager:
										return self.current_processor.pad(*A__							,			**A__				)

						lowercase         = kwargs.pop("input_features"							,			A__				)
						lowercase         = kwargs.pop("labels"							,			A__				)
						if len(A__				) > 0:
										lowercase         = args[0]
										lowercase         = args[1:]

						if input_features is not None:
										lowercase         = self.feature_extractor.pad(A__							,			*A__							,			**A__				)
						if labels is not None:
										lowercase         = self.tokenizer.pad(A__							,			**A__				)

						if labels is None:
										return input_features
						elif input_features is None:
										return labels
						else:
										lowercase         = labels["input_ids"]
										return input_features




		def 				UpperCAmelCase__   (self      :	Tuple							,			*A__      :	Optional[int]							,			**A__      :	Optional[int]				)					-> Tuple:
						return self.tokenizer.decode(*A__							,			**A__				)





		@contextmanager
		def 				UpperCAmelCase__   (self      :	Optional[Any]				)					-> Union[str, Any]:
						warnings.warn(
						    "`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your "
						    "labels by using the argument `text` of the regular `__call__` method (either in the same call as "
						    "your audio inputs, or in a separate call."				)
						lowercase         = True
						lowercase         = self.tokenizer
						yield
						lowercase         = self.feature_extractor
						lowercase         = False




