
"""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 				__lowercase				:



						'''simple docstring'''


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


						def 						_SCREAMING_SNAKE_CASE      ( self     :      int      )     ->   List[Any]:
							return TaConfig.from_pretrained('google/umt5-base'      )


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

							A						   =   self.get_config()
							A						   =   config.num_attention_heads
							A						   =   self.prepare_inputs_dict(A_  ,A_  ,A_      )
							return config, input_dict


						def 						_SCREAMING_SNAKE_CASE      ( self     :      int      )     ->   List[str]:
							A   ,      A						   =   self.prepare_config_and_inputs()
							return config, inputs_dict


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

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

							A   ,      A						   =   outputs.to_tuple()

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

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

							A						   =   model(A_      )['last_hidden_state']
							A						   =   model(A_  ,past_key_values=A_      )['last_hidden_state']

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







@require_torch
class 				__lowercase				(      _lowercase		,					_lowercase		,					_lowercase		,					unittest.TestCase    ):



						'''simple docstring'''


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


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Tuple      )     ->   Any:
							A						   =   UMTaModelTester(self      )


						@unittest.skip('Test has a segmentation fault on torch 1.8.0'      )
						def 						_SCREAMING_SNAKE_CASE      ( self     :      str      )     ->   int:
							A						   =   self.model_tester.prepare_config_and_inputs()
							A						   =   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     :      int      )     ->   Union[str, Any]:
							A						   =   self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model_fpaa_forward(*A_      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Tuple      )     ->   Optional[Any]:
							A						   =   ['encoder_attentions', 'decoder_attentions', 'cross_attentions']
							A						   =   self.model_tester.prepare_config_and_inputs()
							A						   =   config_and_inputs[0]
							A						   =   UMTaForConditionalGeneration(A_      ).eval()
							model.to(A_      )

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

								A						   =   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
								A						   =   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     :      Union[str, Any]      )     ->   str:
							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 						_SCREAMING_SNAKE_CASE      ( self     :      str      )     ->   Dict:
							A						   =   UMTaForConditionalGeneration.from_pretrained('google/umt5-small'  ,return_dict=A_      ).to(A_      )
							A						   =   AutoTokenizer.from_pretrained('google/umt5-small'  ,use_fast=A_  ,legacy=A_      )
							A						   =   [
							    '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>.',
							]
							A						   =   tokenizer(A_  ,return_tensors='pt'  ,padding=A_      ).input_ids
							# fmt: off
							A						   =   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_      )

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

"""simple docstring"""


from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
    is_vision_available,
)


_lowercase							   =				{
    '''configuration_clip''': [
        '''CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''CLIPConfig''',
        '''CLIPOnnxConfig''',
        '''CLIPTextConfig''',
        '''CLIPVisionConfig''',
    ],
    '''processing_clip''': ['''CLIPProcessor'''],
    '''tokenization_clip''': ['''CLIPTokenizer'''],
}

try:
		if not is_tokenizers_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_lowercase							   =				['''CLIPTokenizerFast''']

try:
		if not is_vision_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_lowercase							   =				['''CLIPFeatureExtractor''']
		_lowercase							   =				['''CLIPImageProcessor''']

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_lowercase							   =				[
		    '''CLIP_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''CLIPModel''',
		    '''CLIPPreTrainedModel''',
		    '''CLIPTextModel''',
		    '''CLIPTextModelWithProjection''',
		    '''CLIPVisionModel''',
		    '''CLIPVisionModelWithProjection''',
		]

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_lowercase							   =				[
		    '''TF_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''TFCLIPModel''',
		    '''TFCLIPPreTrainedModel''',
		    '''TFCLIPTextModel''',
		    '''TFCLIPVisionModel''',
		]

try:
		if not is_flax_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_lowercase							   =				[
		    '''FlaxCLIPModel''',
		    '''FlaxCLIPPreTrainedModel''',
		    '''FlaxCLIPTextModel''',
		    '''FlaxCLIPTextPreTrainedModel''',
		    '''FlaxCLIPVisionModel''',
		    '''FlaxCLIPVisionPreTrainedModel''',
		]


if TYPE_CHECKING:
		from .configuration_clip import (
		    CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
		    CLIPConfig,
		    CLIPOnnxConfig,
		    CLIPTextConfig,
		    CLIPVisionConfig,
		)
		from .processing_clip import CLIPProcessor
		from .tokenization_clip import CLIPTokenizer

		try:
				if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_clip_fast import CLIPTokenizerFast

		try:
				if not is_vision_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .feature_extraction_clip import CLIPFeatureExtractor
				from .image_processing_clip import CLIPImageProcessor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_clip import (
				    CLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
				    CLIPModel,
				    CLIPPreTrainedModel,
				    CLIPTextModel,
				    CLIPTextModelWithProjection,
				    CLIPVisionModel,
				    CLIPVisionModelWithProjection,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_clip import (
				    TF_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFCLIPModel,
				    TFCLIPPreTrainedModel,
				    TFCLIPTextModel,
				    TFCLIPVisionModel,
				)

		try:
				if not is_flax_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_flax_clip import (
				    FlaxCLIPModel,
				    FlaxCLIPPreTrainedModel,
				    FlaxCLIPTextModel,
				    FlaxCLIPTextPreTrainedModel,
				    FlaxCLIPVisionModel,
				    FlaxCLIPVisionPreTrainedModel,
				)


else:
		import sys

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