
import unittest

from transformers import SqueezeBertConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
						import torch

						from transformers import (
						    SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
						    SqueezeBertForMaskedLM,
						    SqueezeBertForMultipleChoice,
						    SqueezeBertForQuestionAnswering,
						    SqueezeBertForSequenceClassification,
						    SqueezeBertForTokenClassification,
						    SqueezeBertModel,
						)
class 					lowercase__(							UpperCAmelCase				):



	"""simple docstring"""




	def __init__(			self    :     Optional[int]			,				SCREAMING_SNAKE_CASE_    :     int			,				SCREAMING_SNAKE_CASE_    :     str=1_3			,				SCREAMING_SNAKE_CASE_    :     Tuple=7			,				SCREAMING_SNAKE_CASE_    :     int=True			,				SCREAMING_SNAKE_CASE_    :     Optional[Any]=True			,				SCREAMING_SNAKE_CASE_    :     List[Any]=False			,				SCREAMING_SNAKE_CASE_    :     int=True			,				SCREAMING_SNAKE_CASE_    :     Union[str, Any]=9_9			,				SCREAMING_SNAKE_CASE_    :     Optional[Any]=3_2			,				SCREAMING_SNAKE_CASE_    :     List[str]=5			,				SCREAMING_SNAKE_CASE_    :     Any=4			,				SCREAMING_SNAKE_CASE_    :     Dict=6_4			,				SCREAMING_SNAKE_CASE_    :     Any="gelu"			,				SCREAMING_SNAKE_CASE_    :     Optional[int]=0.1			,				SCREAMING_SNAKE_CASE_    :     Optional[Any]=0.1			,				SCREAMING_SNAKE_CASE_    :     Optional[Any]=5_1_2			,				SCREAMING_SNAKE_CASE_    :     str=1_6			,				SCREAMING_SNAKE_CASE_    :     Any=2			,				SCREAMING_SNAKE_CASE_    :     Tuple=0.02			,				SCREAMING_SNAKE_CASE_    :     str=3			,				SCREAMING_SNAKE_CASE_    :     Dict=4			,				SCREAMING_SNAKE_CASE_    :     Optional[int]=None			,				SCREAMING_SNAKE_CASE_    :     int=2			,				SCREAMING_SNAKE_CASE_    :     Tuple=2			,				SCREAMING_SNAKE_CASE_    :     Union[str, Any]=2			,				SCREAMING_SNAKE_CASE_    :     Dict=2			,				SCREAMING_SNAKE_CASE_    :     Any=4			,				SCREAMING_SNAKE_CASE_    :     Optional[Any]=1			,				)			->	Dict:
					lowercase_	    =   parent
					lowercase_	    =   batch_size
					lowercase_	    =   seq_length
					lowercase_	    =   is_training
					lowercase_	    =   use_input_mask
					lowercase_	    =   use_token_type_ids
					lowercase_	    =   use_labels
					lowercase_	    =   vocab_size
					lowercase_	    =   hidden_size
					lowercase_	    =   num_hidden_layers
					lowercase_	    =   num_attention_heads
					lowercase_	    =   intermediate_size
					lowercase_	    =   hidden_act
					lowercase_	    =   hidden_dropout_prob
					lowercase_	    =   attention_probs_dropout_prob
					lowercase_	    =   max_position_embeddings
					lowercase_	    =   type_vocab_size
					lowercase_	    =   type_sequence_label_size
					lowercase_	    =   initializer_range
					lowercase_	    =   num_labels
					lowercase_	    =   num_choices
					lowercase_	    =   scope
					lowercase_	    =   q_groups
					lowercase_	    =   k_groups
					lowercase_	    =   v_groups
					lowercase_	    =   post_attention_groups
					lowercase_	    =   intermediate_groups
					lowercase_	    =   output_groups
	def 		_lowercase    (			self    :     Any			)			->	Optional[int]:
					lowercase_	    =   ids_tensor([self.batch_size, self.seq_length]			,				self.vocab_size			)

					lowercase_	    =   None
					if self.use_input_mask:
									lowercase_	    =   random_attention_mask([self.batch_size, self.seq_length]			)

					lowercase_	    =   None
					lowercase_	    =   None
					lowercase_	    =   None
					if self.use_labels:
									lowercase_	    =   ids_tensor([self.batch_size]			,				self.type_sequence_label_size			)
									lowercase_	    =   ids_tensor([self.batch_size, self.seq_length]			,				self.num_labels			)
									lowercase_	    =   ids_tensor([self.batch_size]			,				self.num_choices			)

					lowercase_	    =   self.get_config()

					return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels
	def 		_lowercase    (			self    :     List[Any]			)			->	Optional[int]:
					return SqueezeBertConfig(
					    embedding_size=self.hidden_size			,				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			,				attention_probs_dropout_prob=self.hidden_dropout_prob			,				attention_dropout=self.attention_probs_dropout_prob			,				max_position_embeddings=self.max_position_embeddings			,				initializer_range=self.initializer_range			,				q_groups=self.q_groups			,				k_groups=self.k_groups			,				v_groups=self.v_groups			,				post_attention_groups=self.post_attention_groups			,				intermediate_groups=self.intermediate_groups			,				output_groups=self.output_groups			,				)
	def 		_lowercase    (			self    :     Optional[Any]			,				SCREAMING_SNAKE_CASE_    :     int			,				SCREAMING_SNAKE_CASE_    :     Tuple			,				SCREAMING_SNAKE_CASE_    :     Any			,				SCREAMING_SNAKE_CASE_    :     Optional[int]			,				SCREAMING_SNAKE_CASE_    :     Optional[int]			,				SCREAMING_SNAKE_CASE_    :     Union[str, Any]			)			->	List[str]:
					lowercase_	    =   SqueezeBertModel(config=SCREAMING_SNAKE_CASE_			)
					model.to(SCREAMING_SNAKE_CASE_			)
					model.eval()
					lowercase_	    =   model(SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			)
					lowercase_	    =   model(SCREAMING_SNAKE_CASE_			)
					self.parent.assertEqual(result.last_hidden_state.shape			,				(self.batch_size, self.seq_length, self.hidden_size)			)
	def 		_lowercase    (			self    :     Union[str, Any]			,				SCREAMING_SNAKE_CASE_    :     List[str]			,				SCREAMING_SNAKE_CASE_    :     Union[str, Any]			,				SCREAMING_SNAKE_CASE_    :     Optional[int]			,				SCREAMING_SNAKE_CASE_    :     Union[str, Any]			,				SCREAMING_SNAKE_CASE_    :     Optional[Any]			,				SCREAMING_SNAKE_CASE_    :     int			)			->	Dict:
					lowercase_	    =   SqueezeBertForMaskedLM(config=SCREAMING_SNAKE_CASE_			)
					model.to(SCREAMING_SNAKE_CASE_			)
					model.eval()
					lowercase_	    =   model(SCREAMING_SNAKE_CASE_			,				attention_mask=SCREAMING_SNAKE_CASE_			,				labels=SCREAMING_SNAKE_CASE_			)
					self.parent.assertEqual(result.logits.shape			,				(self.batch_size, self.seq_length, self.vocab_size)			)
	def 		_lowercase    (			self    :     Optional[int]			,				SCREAMING_SNAKE_CASE_    :     str			,				SCREAMING_SNAKE_CASE_    :     str			,				SCREAMING_SNAKE_CASE_    :     List[Any]			,				SCREAMING_SNAKE_CASE_    :     Any			,				SCREAMING_SNAKE_CASE_    :     Dict			,				SCREAMING_SNAKE_CASE_    :     int			)			->	List[Any]:
					lowercase_	    =   SqueezeBertForQuestionAnswering(config=SCREAMING_SNAKE_CASE_			)
					model.to(SCREAMING_SNAKE_CASE_			)
					model.eval()
					lowercase_	    =   model(
					    SCREAMING_SNAKE_CASE_			,				attention_mask=SCREAMING_SNAKE_CASE_			,				start_positions=SCREAMING_SNAKE_CASE_			,				end_positions=SCREAMING_SNAKE_CASE_			)
					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    :     str			,				SCREAMING_SNAKE_CASE_    :     List[str]			,				SCREAMING_SNAKE_CASE_    :     List[str]			,				SCREAMING_SNAKE_CASE_    :     Tuple			,				SCREAMING_SNAKE_CASE_    :     Dict			,				SCREAMING_SNAKE_CASE_    :     int			,				SCREAMING_SNAKE_CASE_    :     Union[str, Any]			)			->	Optional[Any]:
					lowercase_	    =   self.num_labels
					lowercase_	    =   SqueezeBertForSequenceClassification(SCREAMING_SNAKE_CASE_			)
					model.to(SCREAMING_SNAKE_CASE_			)
					model.eval()
					lowercase_	    =   model(SCREAMING_SNAKE_CASE_			,				attention_mask=SCREAMING_SNAKE_CASE_			,				labels=SCREAMING_SNAKE_CASE_			)
					self.parent.assertEqual(result.logits.shape			,				(self.batch_size, self.num_labels)			)
	def 		_lowercase    (			self    :     Union[str, Any]			,				SCREAMING_SNAKE_CASE_    :     Any			,				SCREAMING_SNAKE_CASE_    :     str			,				SCREAMING_SNAKE_CASE_    :     Optional[int]			,				SCREAMING_SNAKE_CASE_    :     List[Any]			,				SCREAMING_SNAKE_CASE_    :     Union[str, Any]			,				SCREAMING_SNAKE_CASE_    :     int			)			->	Optional[int]:
					lowercase_	    =   self.num_labels
					lowercase_	    =   SqueezeBertForTokenClassification(config=SCREAMING_SNAKE_CASE_			)
					model.to(SCREAMING_SNAKE_CASE_			)
					model.eval()

					lowercase_	    =   model(SCREAMING_SNAKE_CASE_			,				attention_mask=SCREAMING_SNAKE_CASE_			,				labels=SCREAMING_SNAKE_CASE_			)
					self.parent.assertEqual(result.logits.shape			,				(self.batch_size, self.seq_length, self.num_labels)			)
	def 		_lowercase    (			self    :     str			,				SCREAMING_SNAKE_CASE_    :     Union[str, Any]			,				SCREAMING_SNAKE_CASE_    :     str			,				SCREAMING_SNAKE_CASE_    :     int			,				SCREAMING_SNAKE_CASE_    :     Dict			,				SCREAMING_SNAKE_CASE_    :     Tuple			,				SCREAMING_SNAKE_CASE_    :     List[str]			)			->	Optional[Any]:
					lowercase_	    =   self.num_choices
					lowercase_	    =   SqueezeBertForMultipleChoice(config=SCREAMING_SNAKE_CASE_			)
					model.to(SCREAMING_SNAKE_CASE_			)
					model.eval()
					lowercase_	    =   input_ids.unsqueeze(1			).expand(-1			,				self.num_choices			,				-1			).contiguous()
					lowercase_	    =   input_mask.unsqueeze(1			).expand(-1			,				self.num_choices			,				-1			).contiguous()
					lowercase_	    =   model(
					    SCREAMING_SNAKE_CASE_			,				attention_mask=SCREAMING_SNAKE_CASE_			,				labels=SCREAMING_SNAKE_CASE_			,				)
					self.parent.assertEqual(result.logits.shape			,				(self.batch_size, self.num_choices)			)




	def 		_lowercase    (			self    :     Optional[Any]			)			->	Union[str, Any]:
					lowercase_	    =   self.prepare_config_and_inputs()
					((lowercase_)    ,      (lowercase_)    ,      (lowercase_)    ,      (lowercase_)    ,      (lowercase_)    ,      (lowercase_))	    =   config_and_inputs
					lowercase_	    =   {'''input_ids''': input_ids, '''attention_mask''': input_mask}
					return config, inputs_dict

@require_torch
class 					lowercase__(							UpperCAmelCase					,		UpperCAmelCase					,		unittest.TestCase				):



	"""simple docstring"""




	a     :Any       	=      (
	    (
	        SqueezeBertModel,
	        SqueezeBertForMaskedLM,
	        SqueezeBertForMultipleChoice,
	        SqueezeBertForQuestionAnswering,
	        SqueezeBertForSequenceClassification,
	        SqueezeBertForTokenClassification,
	    )
	    if is_torch_available()
	    else None
	)
	a     :Tuple       	=      (
	    {
	        'feature-extraction': SqueezeBertModel,
	        'fill-mask': SqueezeBertForMaskedLM,
	        'question-answering': SqueezeBertForQuestionAnswering,
	        'text-classification': SqueezeBertForSequenceClassification,
	        'token-classification': SqueezeBertForTokenClassification,
	        'zero-shot': SqueezeBertForSequenceClassification,
	    }
	    if is_torch_available()
	    else {}
	)
	a     :Union[str, Any]       	=      False
	a     :Union[str, Any]       	=      True
	a     :Optional[Any]       	=      False
	def 		_lowercase    (			self    :     Optional[int]			)			->	Tuple:
					lowercase_	    =   SqueezeBertModelTester(self			)
					lowercase_	    =   ConfigTester(self			,				config_class=SCREAMING_SNAKE_CASE_			,				dim=3_7			)
	def 		_lowercase    (			self    :     int			)			->	Tuple:
					self.config_tester.run_common_tests()
	def 		_lowercase    (			self    :     Optional[Any]			)			->	List[str]:
					lowercase_	    =   self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_squeezebert_model(*SCREAMING_SNAKE_CASE_			)
	def 		_lowercase    (			self    :     Optional[Any]			)			->	Any:
					lowercase_	    =   self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_squeezebert_for_masked_lm(*SCREAMING_SNAKE_CASE_			)
	def 		_lowercase    (			self    :     Dict			)			->	Dict:
					lowercase_	    =   self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_squeezebert_for_question_answering(*SCREAMING_SNAKE_CASE_			)
	def 		_lowercase    (			self    :     Dict			)			->	int:
					lowercase_	    =   self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_squeezebert_for_sequence_classification(*SCREAMING_SNAKE_CASE_			)
	def 		_lowercase    (			self    :     Optional[Any]			)			->	Union[str, Any]:
					lowercase_	    =   self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_squeezebert_for_token_classification(*SCREAMING_SNAKE_CASE_			)
	def 		_lowercase    (			self    :     Optional[Any]			)			->	Dict:
					lowercase_	    =   self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_squeezebert_for_multiple_choice(*SCREAMING_SNAKE_CASE_			)




	@slow
	def 		_lowercase    (			self    :     Union[str, Any]			)			->	Dict:
					for model_name in SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
									lowercase_	    =   SqueezeBertModel.from_pretrained(SCREAMING_SNAKE_CASE_			)
									self.assertIsNotNone(SCREAMING_SNAKE_CASE_			)

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



	"""simple docstring"""




	@slow
	def 		_lowercase    (			self    :     Dict			)			->	Dict:
					lowercase_	    =   SqueezeBertForSequenceClassification.from_pretrained('''squeezebert/squeezebert-mnli'''			)

					lowercase_	    =   torch.tensor([[1, 2_9_4_1_4, 2_3_2, 3_2_8, 7_4_0, 1_1_4_0, 1_2_6_9_5, 6_9, 1_3, 1_5_8_8, 2]]			)
					lowercase_	    =   model(SCREAMING_SNAKE_CASE_			)[0]
					lowercase_	    =   torch.Size((1, 3)			)
					self.assertEqual(output.shape			,				SCREAMING_SNAKE_CASE_			)
					lowercase_	    =   torch.tensor([[0.64_01, -0.03_49, -0.60_41]]			)
					self.assertTrue(torch.allclose(SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			,				atol=1e-4			)			)








import copy
from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


SCREAMING_SNAKE_CASE__					:     Optional[int]       =     logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__					:     Tuple       =     {
    'microsoft/conditional-detr-resnet-50': (
        'https://huggingface.co/microsoft/conditional-detr-resnet-50/resolve/main/config.json'
    ),
}


class 		_SCREAMING_SNAKE_CASE     (						A     ):
     __SCREAMING_SNAKE_CASE     					=    '''conditional_detr'''
     __SCREAMING_SNAKE_CASE     					=    ['''past_key_values''']
     __SCREAMING_SNAKE_CASE     					=    {
         '''hidden_size''': '''d_model''',
         '''num_attention_heads''': '''encoder_attention_heads''',
     }


     def __init__(  self							,					A_=True							,					A_=None							,					A_=3							,					A_=3_00							,					A_=6							,					A_=20_48							,					A_=8							,					A_=6							,					A_=20_48							,					A_=8							,					A_=0.0							,					A_=0.0							,					A_=True							,					A_="relu"							,					A_=2_56							,					A_=0.1							,					A_=0.0							,					A_=0.0							,					A_=0.0_2							,					A_=1.0							,					A_=False							,					A_="sine"							,					A_="resnet50"							,					A_=True							,					A_=False							,					A_=2							,					A_=5							,					A_=2							,					A_=1							,					A_=1							,					A_=2							,					A_=5							,					A_=2							,					A_=0.2_5							,					**A_							,					):
       if backbone_config is not None and use_timm_backbone:
         raise ValueError("""You can't specify both `backbone_config` and `use_timm_backbone`."""    )

       if not use_timm_backbone:
         if backbone_config is None:
           logger.info("""`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone."""    )
           _UpperCAmelCase		:							int							=     CONFIG_MAPPING["""resnet"""](out_features=["""stage4"""]    )
         elif isinstance(A_							,					A_    ):
           _UpperCAmelCase		:							Tuple							=     backbone_config.get("""model_type"""    )
           _UpperCAmelCase		:							Union[str, Any]							=     CONFIG_MAPPING[backbone_model_type]
           _UpperCAmelCase		:							Optional[Any]							=     config_class.from_dict(A_    )

       _UpperCAmelCase		:							Tuple							=     use_timm_backbone
       _UpperCAmelCase		:							List[str]							=     backbone_config
       _UpperCAmelCase		:							Any							=     num_channels
       _UpperCAmelCase		:							Tuple							=     num_queries
       _UpperCAmelCase		:							Dict							=     d_model
       _UpperCAmelCase		:							Dict							=     encoder_ffn_dim
       _UpperCAmelCase		:							Tuple							=     encoder_layers
       _UpperCAmelCase		:							List[Any]							=     encoder_attention_heads
       _UpperCAmelCase		:							Union[str, Any]							=     decoder_ffn_dim
       _UpperCAmelCase		:							Optional[int]							=     decoder_layers
       _UpperCAmelCase		:							List[str]							=     decoder_attention_heads
       _UpperCAmelCase		:							Optional[Any]							=     dropout
       _UpperCAmelCase		:							str							=     attention_dropout
       _UpperCAmelCase		:							Union[str, Any]							=     activation_dropout
       _UpperCAmelCase		:							Union[str, Any]							=     activation_function
       _UpperCAmelCase		:							int							=     init_std
       _UpperCAmelCase		:							Union[str, Any]							=     init_xavier_std
       _UpperCAmelCase		:							Tuple							=     encoder_layerdrop
       _UpperCAmelCase		:							Tuple							=     decoder_layerdrop
       _UpperCAmelCase		:							Tuple							=     encoder_layers
       _UpperCAmelCase		:							Any							=     auxiliary_loss
       _UpperCAmelCase		:							List[str]							=     position_embedding_type
       _UpperCAmelCase		:							int							=     backbone
       _UpperCAmelCase		:							Any							=     use_pretrained_backbone
       _UpperCAmelCase		:							int							=     dilation
       # Hungarian matcher
       _UpperCAmelCase		:							Dict							=     class_cost
       _UpperCAmelCase		:							Optional[Any]							=     bbox_cost
       _UpperCAmelCase		:							Tuple							=     giou_cost
       # Loss coefficients
       _UpperCAmelCase		:							Union[str, Any]							=     mask_loss_coefficient
       _UpperCAmelCase		:							int							=     dice_loss_coefficient
       _UpperCAmelCase		:							List[Any]							=     cls_loss_coefficient
       _UpperCAmelCase		:							List[Any]							=     bbox_loss_coefficient
       _UpperCAmelCase		:							int							=     giou_loss_coefficient
       _UpperCAmelCase		:							Optional[int]							=     focal_alpha
       super().__init__(is_encoder_decoder=A_							,					**A_    )


     @property
     def        __snake_case(  self    ):
       return self.encoder_attention_heads


     @property
     def        __snake_case(  self    ):
       return self.d_model




     def        __snake_case(  self    ):
       _UpperCAmelCase		:							List[str]							=     copy.deepcopy(self.__dict__    )
       if self.backbone_config is not None:
         _UpperCAmelCase		:							Optional[int]							=     self.backbone_config.to_dict()
       _UpperCAmelCase		:							Dict							=     self.__class__.model_type
       return output


class 		_SCREAMING_SNAKE_CASE     (						A     ):
     __SCREAMING_SNAKE_CASE     					=    version.parse('''1.11'''     )


     @property
     def        __snake_case(  self    ):
       return OrderedDict(
           [
               ("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
               ("""pixel_mask""", {0: """batch"""}),
           ]    )


     @property
     def        __snake_case(  self    ):
       return 1e-5




     @property
     def        __snake_case(  self    ):
       return 12





