





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 							__lowercase       :







	'''simple docstring'''

	def __init__(self     ,_lowerCamelCase     ,_lowerCamelCase=99     ,_lowerCamelCase=13     ,_lowerCamelCase=7     ,_lowerCamelCase=9     ,_lowerCamelCase=True     ,_lowerCamelCase=True     ,_lowerCamelCase=False     ,_lowerCamelCase=32     ,_lowerCamelCase=5     ,_lowerCamelCase=4     ,_lowerCamelCase=37     ,_lowerCamelCase=8     ,_lowerCamelCase=0.1     ,_lowerCamelCase=0.0_0_2     ,_lowerCamelCase=1     ,_lowerCamelCase=0     ,_lowerCamelCase=0     ,_lowerCamelCase=None     ,_lowerCamelCase=None     ,)							->							Optional[Any]:







				'''simple docstring'''





				__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 )							->							Optional[Any]:







				'''simple docstring'''





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


	def 						_UpperCAmelCase     (self     ,_lowerCamelCase     ,_lowerCamelCase     ,_lowerCamelCase     ,_lowerCamelCase=None     ,_lowerCamelCase=None     ,_lowerCamelCase=None     ,_lowerCamelCase=None     ,_lowerCamelCase=None     ,)							->							str:







				'''simple docstring'''





				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:







				'''simple docstring'''





				__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 )							->							Tuple:







				'''simple docstring'''





				__lowercase										=							self.prepare_config_and_inputs()
				return config, inputs_dict


	def 						_UpperCAmelCase     (self )							->							Dict:







				'''simple docstring'''





				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]:







				'''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 						_UpperCAmelCase     (self     ,_lowerCamelCase     ,_lowerCamelCase     ,_lowerCamelCase     ,_lowerCamelCase     ,_lowerCamelCase     ,_lowerCamelCase     ,)							->							Union[str, Any]:







				'''simple docstring'''





				__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     ,_lowerCamelCase     ,_lowerCamelCase     ,_lowerCamelCase     ,_lowerCamelCase     ,_lowerCamelCase     ,_lowerCamelCase     ,)							->							Union[str, Any]:







				'''simple docstring'''





				__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										=							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     ,_lowerCamelCase     ,_lowerCamelCase     ,)							->							Dict:







				'''simple docstring'''





				__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 							__lowercase       (      _UpperCamelCase ,      _UpperCamelCase ,      _UpperCamelCase ,      unittest.TestCase						):







	'''simple docstring'''

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


	def 						_UpperCAmelCase     (self )							->							Optional[int]:







				'''simple docstring'''





				__lowercase										=							UMTaModelTester(self )


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







				'''simple docstring'''





				__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 )							->							List[str]:







				'''simple docstring'''





				__lowercase										=							self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_model_fpaa_forward(*__a )


	def 						_UpperCAmelCase     (self )							->							Tuple:







				'''simple docstring'''





				__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:







				'''simple docstring'''





				pass






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







	'''simple docstring'''

	@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 )							->							Optional[Any]:







				'''simple docstring'''





				__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(
				    [
				        [ 38530, 210703, 256299, 1410, 256298, 274, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
				        [   826, 321, 671, 25922, 256299, 274, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
				        [  1460, 339, 312, 19014, 10620, 758, 256299, 2355,274, 1, 0, 0, 0, 0, 0, 0,0, 0],
				        [   517, 256299, 14869, 281, 301, 256298, 275, 119983,1, 0, 0, 0, 0, 0, 0, 0,0, 0],
				        [   320, 256299, 14869, 281, 2234, 289, 2275, 333,61391, 289, 256298, 543, 256297, 168714, 329, 256296,274, 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'''



from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_SCREAMING_SNAKE_CASE					=    {
    '''configuration_clipseg''': [
        '''CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''CLIPSegConfig''',
        '''CLIPSegTextConfig''',
        '''CLIPSegVisionConfig''',
    ],
    '''processing_clipseg''': ['''CLIPSegProcessor'''],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_SCREAMING_SNAKE_CASE					=    [
	    '''CLIPSEG_PRETRAINED_MODEL_ARCHIVE_LIST''',
	    '''CLIPSegModel''',
	    '''CLIPSegPreTrainedModel''',
	    '''CLIPSegTextModel''',
	    '''CLIPSegVisionModel''',
	    '''CLIPSegForImageSegmentation''',
	]

if TYPE_CHECKING:
	from .configuration_clipseg import (
	    CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP,
	    CLIPSegConfig,
	    CLIPSegTextConfig,
	    CLIPSegVisionConfig,
	)
	from .processing_clipseg import CLIPSegProcessor

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_clipseg import (
		    CLIPSEG_PRETRAINED_MODEL_ARCHIVE_LIST,
		    CLIPSegForImageSegmentation,
		    CLIPSegModel,
		    CLIPSegPreTrainedModel,
		    CLIPSegTextModel,
		    CLIPSegVisionModel,
		)

else:
	import sys

	_SCREAMING_SNAKE_CASE					=    _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)





