

"""simple docstring"""


import unittest

from transformers import LiltConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
			import torch

			from transformers import (
			    LiltForQuestionAnswering,
			    LiltForSequenceClassification,
			    LiltForTokenClassification,
			    LiltModel,
			)
			from transformers.models.lilt.modeling_lilt import LILT_PRETRAINED_MODEL_ARCHIVE_LIST





class    snake_case     :

				def __init__(     self							:    Tuple					,  UpperCamelCase__							:    List[Any]					,  UpperCamelCase__							:    int=1_3					,  UpperCamelCase__							:    Tuple=7					,  UpperCamelCase__							:    Dict=True					,  UpperCamelCase__							:    Dict=True					,  UpperCamelCase__							:    Union[str, Any]=True					,  UpperCamelCase__							:    Tuple=True					,  UpperCamelCase__							:    int=9_9					,  UpperCamelCase__							:    List[Any]=2_4					,  UpperCamelCase__							:    List[str]=2					,  UpperCamelCase__							:    Union[str, Any]=6					,  UpperCamelCase__							:    Dict=3_7					,  UpperCamelCase__							:    int="gelu"					,  UpperCamelCase__							:    Dict=0.1					,  UpperCamelCase__							:    Optional[Any]=0.1					,  UpperCamelCase__							:    Dict=5_1_2					,  UpperCamelCase__							:    Dict=1_6					,  UpperCamelCase__							:    Dict=2					,  UpperCamelCase__							:    Any=0.02					,  UpperCamelCase__							:    Dict=3					,  UpperCamelCase__							:    Any=None					,  UpperCamelCase__							:    Union[str, Any]=1_0_0_0					,  )->       int:



						'''simple docstring'''


						__lowerCAmelCase:	Any   				=      parent
						__lowerCAmelCase:	Any   				=      batch_size
						__lowerCAmelCase:	List[Any]   				=      seq_length
						__lowerCAmelCase:	Optional[Any]   				=      is_training
						__lowerCAmelCase:	List[Any]   				=      use_input_mask
						__lowerCAmelCase:	List[Any]   				=      use_token_type_ids
						__lowerCAmelCase:	Union[str, Any]   				=      use_labels
						__lowerCAmelCase:	Optional[int]   				=      vocab_size
						__lowerCAmelCase:	Union[str, Any]   				=      hidden_size
						__lowerCAmelCase:	Optional[Any]   				=      num_hidden_layers
						__lowerCAmelCase:	Optional[int]   				=      num_attention_heads
						__lowerCAmelCase:	List[Any]   				=      intermediate_size
						__lowerCAmelCase:	Dict   				=      hidden_act
						__lowerCAmelCase:	List[Any]   				=      hidden_dropout_prob
						__lowerCAmelCase:	Union[str, Any]   				=      attention_probs_dropout_prob
						__lowerCAmelCase:	str   				=      max_position_embeddings
						__lowerCAmelCase:	List[str]   				=      type_vocab_size
						__lowerCAmelCase:	Optional[Any]   				=      type_sequence_label_size
						__lowerCAmelCase:	str   				=      initializer_range
						__lowerCAmelCase:	int   				=      num_labels
						__lowerCAmelCase:	Union[str, Any]   				=      scope
						__lowerCAmelCase:	Any   				=      range_bbox

				def 							lowercase_					(     self							:    Dict)->       List[str]:



						'''simple docstring'''


						__lowerCAmelCase:	int   				=      ids_tensor([self.batch_size, self.seq_length]					,  self.vocab_size)

						__lowerCAmelCase:	Any   				=      ids_tensor([self.batch_size, self.seq_length, 4]					,  self.range_bbox)
						# Ensure that bbox is legal
						for i in range(bbox.shape[0]):
								for j in range(bbox.shape[1]):
										if bbox[i, j, 3] < bbox[i, j, 1]:
												__lowerCAmelCase:	List[str]   				=      bbox[i, j, 3]
												__lowerCAmelCase:	Union[str, Any]   				=      bbox[i, j, 1]
												__lowerCAmelCase:	int   				=      t
										if bbox[i, j, 2] < bbox[i, j, 0]:
												__lowerCAmelCase:	Dict   				=      bbox[i, j, 2]
												__lowerCAmelCase:	List[str]   				=      bbox[i, j, 0]
												__lowerCAmelCase:	Optional[int]   				=      t

						__lowerCAmelCase:	Optional[int]   				=      None
						if self.use_input_mask:
								__lowerCAmelCase:	List[str]   				=      ids_tensor([self.batch_size, self.seq_length]					,  vocab_size=2)

						__lowerCAmelCase:	List[Any]   				=      None
						if self.use_token_type_ids:
								__lowerCAmelCase:	int   				=      ids_tensor([self.batch_size, self.seq_length]					,  self.type_vocab_size)

						__lowerCAmelCase:	str   				=      None
						__lowerCAmelCase:	Union[str, Any]   				=      None
						if self.use_labels:
								__lowerCAmelCase:	Tuple   				=      ids_tensor([self.batch_size]					,  self.type_sequence_label_size)
								__lowerCAmelCase:	Any   				=      ids_tensor([self.batch_size, self.seq_length]					,  self.num_labels)

						__lowerCAmelCase:	Dict   				=      self.get_config()

						return config, input_ids, bbox, token_type_ids, input_mask, sequence_labels, token_labels

				def 							lowercase_					(     self							:    Dict)->       List[Any]:



						'''simple docstring'''


						return LiltConfig(
						    vocab_size=self.vocab_size					,  hidden_size=self.hidden_size					,  num_hidden_layers=self.num_hidden_layers					,  num_attention_heads=self.num_attention_heads					,  intermediate_size=self.intermediate_size					,  hidden_act=self.hidden_act					,  hidden_dropout_prob=self.hidden_dropout_prob					,  attention_probs_dropout_prob=self.attention_probs_dropout_prob					,  max_position_embeddings=self.max_position_embeddings					,  type_vocab_size=self.type_vocab_size					,  initializer_range=self.initializer_range					,  )

				def 							lowercase_					(     self							:    Dict					,  UpperCamelCase__							:    int					,  UpperCamelCase__							:    Dict					,  UpperCamelCase__							:    str					,  UpperCamelCase__							:    Any					,  UpperCamelCase__							:    Optional[Any]					,  UpperCamelCase__							:    str					,  UpperCamelCase__							:    Optional[int]					,  )->       Optional[int]:



						'''simple docstring'''


						__lowerCAmelCase:	Union[str, Any]   				=      LiltModel(config=UpperCamelCase__)
						model.to(UpperCamelCase__)
						model.eval()
						__lowerCAmelCase:	Dict   				=      model(UpperCamelCase__					,  bbox=UpperCamelCase__					,  attention_mask=UpperCamelCase__					,  token_type_ids=UpperCamelCase__)
						__lowerCAmelCase:	List[Any]   				=      model(UpperCamelCase__					,  bbox=UpperCamelCase__					,  token_type_ids=UpperCamelCase__)
						__lowerCAmelCase:	Tuple   				=      model(UpperCamelCase__					,  bbox=UpperCamelCase__)

						self.parent.assertEqual(result.last_hidden_state.shape					,  (self.batch_size, self.seq_length, self.hidden_size))
						self.parent.assertEqual(result.pooler_output.shape					,  (self.batch_size, self.hidden_size))

				def 							lowercase_					(     self							:    Optional[Any]					,  UpperCamelCase__							:    List[str]					,  UpperCamelCase__							:    Optional[int]					,  UpperCamelCase__							:    Dict					,  UpperCamelCase__							:    Tuple					,  UpperCamelCase__							:    Tuple					,  UpperCamelCase__							:    Dict					,  UpperCamelCase__							:    Optional[int]					,  )->       Dict:



						'''simple docstring'''


						__lowerCAmelCase:	List[Any]   				=      self.num_labels
						__lowerCAmelCase:	Tuple   				=      LiltForTokenClassification(config=UpperCamelCase__)
						model.to(UpperCamelCase__)
						model.eval()
						__lowerCAmelCase:	str   				=      model(
						    UpperCamelCase__					,  bbox=UpperCamelCase__					,  attention_mask=UpperCamelCase__					,  token_type_ids=UpperCamelCase__					,  labels=UpperCamelCase__)
						self.parent.assertEqual(result.logits.shape					,  (self.batch_size, self.seq_length, self.num_labels))

				def 							lowercase_					(     self							:    List[Any]					,  UpperCamelCase__							:    Optional[int]					,  UpperCamelCase__							:    Optional[Any]					,  UpperCamelCase__							:    List[Any]					,  UpperCamelCase__							:    int					,  UpperCamelCase__							:    Tuple					,  UpperCamelCase__							:    Any					,  UpperCamelCase__							:    Any					,  )->       Union[str, Any]:



						'''simple docstring'''


						__lowerCAmelCase:	Union[str, Any]   				=      LiltForQuestionAnswering(config=UpperCamelCase__)
						model.to(UpperCamelCase__)
						model.eval()
						__lowerCAmelCase:	List[str]   				=      model(
						    UpperCamelCase__					,  bbox=UpperCamelCase__					,  attention_mask=UpperCamelCase__					,  token_type_ids=UpperCamelCase__					,  start_positions=UpperCamelCase__					,  end_positions=UpperCamelCase__					,  )
						self.parent.assertEqual(result.start_logits.shape					,  (self.batch_size, self.seq_length))
						self.parent.assertEqual(result.end_logits.shape					,  (self.batch_size, self.seq_length))




				def 							lowercase_					(     self							:    Optional[Any])->       Optional[int]:



						'''simple docstring'''


						__lowerCAmelCase:	str   				=      self.prepare_config_and_inputs()
						(
						    (
						    __lowerCAmelCase
						)				,			(
						    __lowerCAmelCase
						)				,			(
						    __lowerCAmelCase
						)				,			(
						    __lowerCAmelCase
						)				,			(
						    __lowerCAmelCase
						)				,			(
						    __lowerCAmelCase
						)				,			(
						    __lowerCAmelCase
						)				,			
						):	Optional[int]   				=      config_and_inputs
						__lowerCAmelCase:	List[Any]   				=      {
						    "input_ids": input_ids,
						    "bbox": bbox,
						    "token_type_ids": token_type_ids,
						    "attention_mask": input_mask,
						}
						return config, inputs_dict





@require_torch
class    snake_case     (    __snake_case,							__snake_case,							__snake_case,							unittest.TestCase  ):
				SCREAMING_SNAKE_CASE_       :				Any       				=  (
				    (
				        LiltModel,
				        LiltForSequenceClassification,
				        LiltForTokenClassification,
				        LiltForQuestionAnswering,
				    )
				    if is_torch_available()
				    else ()
				)
				SCREAMING_SNAKE_CASE_       :				Union[str, Any]       				=  (
				    {
				        """feature-extraction""": LiltModel,
				        """question-answering""": LiltForQuestionAnswering,
				        """text-classification""": LiltForSequenceClassification,
				        """token-classification""": LiltForTokenClassification,
				        """zero-shot""": LiltForSequenceClassification,
				    }
				    if is_torch_available()
				    else {}
				)
				SCREAMING_SNAKE_CASE_       :				Any       				=  False
				SCREAMING_SNAKE_CASE_       :				Tuple       				=  False

				def 							lowercase_					(     self							:    List[str]					,  UpperCamelCase__							:    Any					,  UpperCamelCase__							:    Tuple					,  UpperCamelCase__							:    Tuple					,  UpperCamelCase__							:    Optional[Any]					,  UpperCamelCase__							:    int)->       Dict:



						'''simple docstring'''


						return True

				def 							lowercase_					(     self							:    List[Any])->       Dict:



						'''simple docstring'''


						__lowerCAmelCase:	Dict   				=      LiltModelTester(self)
						__lowerCAmelCase:	Optional[int]   				=      ConfigTester(self					,  config_class=UpperCamelCase__					,  hidden_size=3_7)

				def 							lowercase_					(     self							:    Tuple)->       Any:



						'''simple docstring'''


						self.config_tester.run_common_tests()

				def 							lowercase_					(     self							:    List[Any])->       Union[str, Any]:



						'''simple docstring'''


						__lowerCAmelCase:	List[str]   				=      self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_model(*UpperCamelCase__)

				def 							lowercase_					(     self							:    str)->       int:



						'''simple docstring'''


						__lowerCAmelCase:	Dict   				=      self.model_tester.prepare_config_and_inputs()
						for type in ["absolute", "relative_key", "relative_key_query"]:
								__lowerCAmelCase:	List[Any]   				=      type
								self.model_tester.create_and_check_model(*UpperCamelCase__)

				def 							lowercase_					(     self							:    str)->       Union[str, Any]:



						'''simple docstring'''


						__lowerCAmelCase:	List[str]   				=      self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_token_classification(*UpperCamelCase__)

				def 							lowercase_					(     self							:    Tuple)->       int:



						'''simple docstring'''


						__lowerCAmelCase:	List[str]   				=      self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_question_answering(*UpperCamelCase__)




				@slow
				def 							lowercase_					(     self							:    Dict)->       List[str]:



						'''simple docstring'''


						for model_name in LILT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
								__lowerCAmelCase:	Any   				=      LiltModel.from_pretrained(UpperCamelCase__)
								self.assertIsNotNone(UpperCamelCase__)





@require_torch
@slow
class    snake_case     (    unittest.TestCase  ):

				def 							lowercase_					(     self							:    Dict)->       Any:



						'''simple docstring'''


						__lowerCAmelCase:	Union[str, Any]   				=      LiltModel.from_pretrained("SCUT-DLVCLab/lilt-roberta-en-base").to(UpperCamelCase__)

						__lowerCAmelCase:	Optional[int]   				=      torch.tensor([[1, 2]]					,  device=UpperCamelCase__)
						__lowerCAmelCase:	int   				=      torch.tensor([[[1, 2, 3, 4], [5, 6, 7, 8]]]					,  device=UpperCamelCase__)

						# forward pass
						with torch.no_grad():
								__lowerCAmelCase:	str   				=      model(input_ids=UpperCamelCase__					,  bbox=UpperCamelCase__)

						__lowerCAmelCase:	Optional[int]   				=      torch.Size([1, 2, 7_6_8])
						__lowerCAmelCase:	Optional[int]   				=      torch.tensor(
						    [[-0.0653, 0.0950, -0.0061], [-0.0545, 0.0926, -0.0324]]					,  device=UpperCamelCase__					,  )

						self.assertTrue(outputs.last_hidden_state.shape					,  UpperCamelCase__)
						self.assertTrue(torch.allclose(outputs.last_hidden_state[0, :, :3]					,  UpperCamelCase__					,  atol=1e-3))





"""simple docstring"""


from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_torch_available,
)


__A						       =      {
    "configuration_speecht5": [
        "SPEECHT5_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "SPEECHT5_PRETRAINED_HIFIGAN_CONFIG_ARCHIVE_MAP",
        "SpeechT5Config",
        "SpeechT5HifiGanConfig",
    ],
    "feature_extraction_speecht5": ["SpeechT5FeatureExtractor"],
    "processing_speecht5": ["SpeechT5Processor"],
}

try:
			if not is_sentencepiece_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			__A						       =      ["SpeechT5Tokenizer"]

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			__A						       =      [
			    "SPEECHT5_PRETRAINED_MODEL_ARCHIVE_LIST",
			    "SpeechT5ForSpeechToText",
			    "SpeechT5ForSpeechToSpeech",
			    "SpeechT5ForTextToSpeech",
			    "SpeechT5Model",
			    "SpeechT5PreTrainedModel",
			    "SpeechT5HifiGan",
			]

if TYPE_CHECKING:
			from .configuration_speechta import (
			    SPEECHT5_PRETRAINED_CONFIG_ARCHIVE_MAP,
			    SPEECHT5_PRETRAINED_HIFIGAN_CONFIG_ARCHIVE_MAP,
			    SpeechTaConfig,
			    SpeechTaHifiGanConfig,
			)
			from .feature_extraction_speechta import SpeechTaFeatureExtractor
			from .processing_speechta import SpeechTaProcessor

			try:
						if not is_sentencepiece_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_speechta import SpeechTaTokenizer

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_speechta import (
						    SPEECHT5_PRETRAINED_MODEL_ARCHIVE_LIST,
						    SpeechTaForSpeechToSpeech,
						    SpeechTaForSpeechToText,
						    SpeechTaForTextToSpeech,
						    SpeechTaHifiGan,
						    SpeechTaModel,
						    SpeechTaPreTrainedModel,
						)

else:
			import sys

			__A						       =      _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)


