



'''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			,				UpperCamelCase			,				UpperCamelCase=99			,				UpperCamelCase=13			,				UpperCamelCase=7			,				UpperCamelCase=9			,				UpperCamelCase=True			,				UpperCamelCase=True			,				UpperCamelCase=False			,				UpperCamelCase=32			,				UpperCamelCase=5			,				UpperCamelCase=4			,				UpperCamelCase=37			,				UpperCamelCase=8			,				UpperCamelCase=0.1			,				UpperCamelCase=0.0_02			,				UpperCamelCase=1			,				UpperCamelCase=0			,				UpperCamelCase=0			,				UpperCamelCase=None			,				UpperCamelCase=None			,				)     ->			Any:
						__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        UpperCAmelCase_ (    self     )     ->			Optional[Any]:
						return TaConfig.from_pretrained("google/umt5-base"     )



					def        UpperCAmelCase_ (    self			,				UpperCamelCase			,				UpperCamelCase			,				UpperCamelCase			,				UpperCamelCase=None			,				UpperCamelCase=None			,				UpperCamelCase=None			,				UpperCamelCase=None			,				UpperCamelCase=None			,				)     ->			List[Any]:
						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=UpperCamelCase     )
						if decoder_head_mask is None:
							__lowerCAmelCase												=  torch.ones(config.num_decoder_layers			,				config.num_attention_heads			,				device=UpperCamelCase     )
						if cross_attn_head_mask is None:
							__lowerCAmelCase												=  torch.ones(
							    config.num_decoder_layers			,				config.num_attention_heads			,				device=UpperCamelCase     )
						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     )     ->			Optional[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(UpperCamelCase			,				UpperCamelCase			,				UpperCamelCase     )
						return config, input_dict



					def        UpperCAmelCase_ (    self     )     ->			List[str]:
						__lowerCAmelCase							,       __lowerCAmelCase												=  self.prepare_config_and_inputs()
						return config, inputs_dict



					def        UpperCAmelCase_ (    self     )     ->			Optional[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        UpperCAmelCase_ (    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        UpperCAmelCase_ (    self			,				UpperCamelCase			,				UpperCamelCase			,				UpperCamelCase			,				UpperCamelCase			,				UpperCamelCase			,				UpperCamelCase			,				)     ->			str:
						__lowerCAmelCase												=  UMTaModel(config=UpperCamelCase     )
						model.to(UpperCamelCase     )
						model.eval()
						__lowerCAmelCase												=  model(
						    input_ids=UpperCamelCase			,				decoder_input_ids=UpperCamelCase			,				attention_mask=UpperCamelCase			,				decoder_attention_mask=UpperCamelCase			,				)
						__lowerCAmelCase												=  model(input_ids=UpperCamelCase			,				decoder_input_ids=UpperCamelCase     )
						__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(UpperCamelCase     )			,				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			,				UpperCamelCase			,				UpperCamelCase			,				UpperCamelCase			,				UpperCamelCase			,				UpperCamelCase			,				UpperCamelCase			,				)     ->			Tuple:
						__lowerCAmelCase												=  UMTaModel(config=UpperCamelCase     ).get_decoder().to(UpperCamelCase     ).eval()
						# first forward pass
						__lowerCAmelCase												=  model(UpperCamelCase			,				use_cache=UpperCamelCase     )
						__lowerCAmelCase												=  model(UpperCamelCase     )
						__lowerCAmelCase												=  model(UpperCamelCase			,				use_cache=UpperCamelCase     )

						self.parent.assertTrue(len(UpperCamelCase     ) == len(UpperCamelCase     )     )
						self.parent.assertTrue(len(UpperCamelCase     ) == len(UpperCamelCase     ) + 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(UpperCamelCase     )["last_hidden_state"]
						__lowerCAmelCase												=  model(UpperCamelCase			,				past_key_values=UpperCamelCase     )["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(UpperCamelCase			,				UpperCamelCase			,				atol=1E-3     )     )



					def        UpperCAmelCase_ (    self			,				UpperCamelCase			,				UpperCamelCase			,				)     ->			int:
						__lowerCAmelCase												=  UMTaModel(config=UpperCamelCase     ).to(UpperCamelCase     ).half().eval()
						__lowerCAmelCase												=  model(**UpperCamelCase     )["last_hidden_state"]
						self.parent.assertFalse(torch.isnan(UpperCamelCase     ).any().item()     )






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



					def        UpperCAmelCase_ (    self     )     ->			Optional[int]:
						__lowerCAmelCase												=  UMTaModelTester(self     )



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



					@unittest.skipIf(torch_device == "cpu"			,				"Cant do half precision"     )
					def        UpperCAmelCase_ (    self     )     ->			Dict:
						__lowerCAmelCase												=  self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_model_fpaa_forward(*UpperCamelCase     )



					def        UpperCAmelCase_ (    self     )     ->			Any:
						__lowerCAmelCase												=  ["encoder_attentions", "decoder_attentions", "cross_attentions"]
						__lowerCAmelCase												=  self.model_tester.prepare_config_and_inputs()
						__lowerCAmelCase												=  config_and_inputs[0]
						__lowerCAmelCase												=  UMTaForConditionalGeneration(UpperCamelCase     ).eval()
						model.to(UpperCamelCase     )

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

						for attn_name, (name, mask) in zip(UpperCamelCase			,				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=UpperCamelCase     )

							__lowerCAmelCase												=  model.generate(
							    config_and_inputs[1]["input_ids"]			,				num_beams=1			,				max_length=3			,				output_attentions=UpperCamelCase			,				return_dict_in_generate=UpperCamelCase			,				**UpperCamelCase			,				)
							# 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        UpperCAmelCase_ (    self     )     ->			Optional[int]:
						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     )     ->			Any:
						__lowerCAmelCase												=  UMTaForConditionalGeneration.from_pretrained("google/umt5-small"			,				return_dict=UpperCamelCase     ).to(UpperCamelCase     )
						__lowerCAmelCase												=  AutoTokenizer.from_pretrained("google/umt5-small"			,				use_fast=UpperCamelCase			,				legacy=UpperCamelCase     )
						__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(UpperCamelCase			,				return_tensors="pt"			,				padding=UpperCamelCase     ).input_ids
						# fmt: off
						__lowerCAmelCase												=  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(UpperCamelCase			,				UpperCamelCase     )

						__lowerCAmelCase												=  model.generate(input_ids.to(UpperCamelCase     )     )
						__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(UpperCamelCase     )
						self.assertEqual(UpperCamelCase			,				UpperCamelCase     )




'''simple docstring'''



import os
import sys
import unittest


lowerCAmelCase	:		str				=   os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, '''utils'''))

import get_test_info  # noqa: E402
from get_test_info import (  # noqa: E402
    get_model_to_test_mapping,
    get_model_to_tester_mapping,
    get_test_to_tester_mapping,
)


lowerCAmelCase	:		Tuple				=   os.path.join('''tests''', '''models''', '''bert''', '''test_modeling_bert.py''')
lowerCAmelCase	:		Tuple				=   os.path.join('''tests''', '''models''', '''blip''', '''test_modeling_blip.py''')






class 					UpperCAmelCase__	(		unittest.TestCase ):



					def        UpperCAmelCase_ (    self     )     ->			str:
						__lowerCAmelCase												=  get_test_to_tester_mapping(UpperCamelCase     )
						__lowerCAmelCase												=  get_test_to_tester_mapping(UpperCamelCase     )

						__lowerCAmelCase												=  {"BertModelTest": "BertModelTester"}

						__lowerCAmelCase												=  {
						    "BlipModelTest": "BlipModelTester",
						    "BlipTextImageModelTest": "BlipTextImageModelsModelTester",
						    "BlipTextModelTest": "BlipTextModelTester",
						    "BlipTextRetrievalModelTest": "BlipTextRetrievalModelTester",
						    "BlipVQAModelTest": "BlipVQAModelTester",
						    "BlipVisionModelTest": "BlipVisionModelTester",
						}

						self.assertEqual(get_test_info.to_json(UpperCamelCase     )			,				UpperCamelCase     )
						self.assertEqual(get_test_info.to_json(UpperCamelCase     )			,				UpperCamelCase     )



					def        UpperCAmelCase_ (    self     )     ->			List[Any]:
						__lowerCAmelCase												=  get_model_to_test_mapping(UpperCamelCase     )
						__lowerCAmelCase												=  get_model_to_test_mapping(UpperCamelCase     )

						__lowerCAmelCase												=  {
						    "BertForMaskedLM": ["BertModelTest"],
						    "BertForMultipleChoice": ["BertModelTest"],
						    "BertForNextSentencePrediction": ["BertModelTest"],
						    "BertForPreTraining": ["BertModelTest"],
						    "BertForQuestionAnswering": ["BertModelTest"],
						    "BertForSequenceClassification": ["BertModelTest"],
						    "BertForTokenClassification": ["BertModelTest"],
						    "BertLMHeadModel": ["BertModelTest"],
						    "BertModel": ["BertModelTest"],
						}

						__lowerCAmelCase												=  {
						    "BlipForConditionalGeneration": ["BlipTextImageModelTest"],
						    "BlipForImageTextRetrieval": ["BlipTextRetrievalModelTest"],
						    "BlipForQuestionAnswering": ["BlipVQAModelTest"],
						    "BlipModel": ["BlipModelTest"],
						    "BlipTextModel": ["BlipTextModelTest"],
						    "BlipVisionModel": ["BlipVisionModelTest"],
						}

						self.assertEqual(get_test_info.to_json(UpperCamelCase     )			,				UpperCamelCase     )
						self.assertEqual(get_test_info.to_json(UpperCamelCase     )			,				UpperCamelCase     )



					def        UpperCAmelCase_ (    self     )     ->			str:
						__lowerCAmelCase												=  get_model_to_tester_mapping(UpperCamelCase     )
						__lowerCAmelCase												=  get_model_to_tester_mapping(UpperCamelCase     )

						__lowerCAmelCase												=  {
						    "BertForMaskedLM": ["BertModelTester"],
						    "BertForMultipleChoice": ["BertModelTester"],
						    "BertForNextSentencePrediction": ["BertModelTester"],
						    "BertForPreTraining": ["BertModelTester"],
						    "BertForQuestionAnswering": ["BertModelTester"],
						    "BertForSequenceClassification": ["BertModelTester"],
						    "BertForTokenClassification": ["BertModelTester"],
						    "BertLMHeadModel": ["BertModelTester"],
						    "BertModel": ["BertModelTester"],
						}

						__lowerCAmelCase												=  {
						    "BlipForConditionalGeneration": ["BlipTextImageModelsModelTester"],
						    "BlipForImageTextRetrieval": ["BlipTextRetrievalModelTester"],
						    "BlipForQuestionAnswering": ["BlipVQAModelTester"],
						    "BlipModel": ["BlipModelTester"],
						    "BlipTextModel": ["BlipTextModelTester"],
						    "BlipVisionModel": ["BlipVisionModelTester"],
						}

						self.assertEqual(get_test_info.to_json(UpperCamelCase     )			,				UpperCamelCase     )
						self.assertEqual(get_test_info.to_json(UpperCamelCase     )			,				UpperCamelCase     )