
'''simple docstring'''

import unittest

import numpy as np

from transformers import RobertaPreLayerNormConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_flax_available():
	import jax.numpy as jnp

	from transformers.models.roberta_prelayernorm.modeling_flax_roberta_prelayernorm import (
	    FlaxRobertaPreLayerNormForCausalLM,
	    FlaxRobertaPreLayerNormForMaskedLM,
	    FlaxRobertaPreLayerNormForMultipleChoice,
	    FlaxRobertaPreLayerNormForQuestionAnswering,
	    FlaxRobertaPreLayerNormForSequenceClassification,
	    FlaxRobertaPreLayerNormForTokenClassification,
	    FlaxRobertaPreLayerNormModel,
	)

class       UpperCAmelCase					(       unittest.TestCase			):





		"""simple docstring"""





		def __init__(     self	,       _snake_case	,       _snake_case=13	,       _snake_case=7	,       _snake_case=True	,       _snake_case=True	,       _snake_case=True	,       _snake_case=True	,       _snake_case=99	,       _snake_case=32	,       _snake_case=5	,       _snake_case=4	,       _snake_case=37	,       _snake_case="gelu"	,       _snake_case=0.1	,       _snake_case=0.1	,       _snake_case=512	,       _snake_case=16	,       _snake_case=2	,       _snake_case=0.02	,       _snake_case=4	,       )					->     int:
				_UpperCamelCase							:			Any     =   parent
				_UpperCamelCase							:			Union[str, Any]     =   batch_size
				_UpperCamelCase							:			int     =   seq_length
				_UpperCamelCase							:			Dict     =   is_training
				_UpperCamelCase							:			Optional[Any]     =   use_attention_mask
				_UpperCamelCase							:			List[Any]     =   use_token_type_ids
				_UpperCamelCase							:			Optional[Any]     =   use_labels
				_UpperCamelCase							:			Optional[int]     =   vocab_size
				_UpperCamelCase							:			List[str]     =   hidden_size
				_UpperCamelCase							:			List[str]     =   num_hidden_layers
				_UpperCamelCase							:			Union[str, Any]     =   num_attention_heads
				_UpperCamelCase							:			List[Any]     =   intermediate_size
				_UpperCamelCase							:			List[str]     =   hidden_act
				_UpperCamelCase							:			Dict     =   hidden_dropout_prob
				_UpperCamelCase							:			Any     =   attention_probs_dropout_prob
				_UpperCamelCase							:			Union[str, Any]     =   max_position_embeddings
				_UpperCamelCase							:			Optional[int]     =   type_vocab_size
				_UpperCamelCase							:			List[Any]     =   type_sequence_label_size
				_UpperCamelCase							:			Dict     =   initializer_range
				_UpperCamelCase							:			int     =   num_choices
		def _lowercase	(     self  )					->     Dict:
				_UpperCamelCase							:			Optional[int]     =   ids_tensor([self.batch_size, self.seq_length]	,       self.vocab_size  )

				_UpperCamelCase							:			Dict     =   None
				if self.use_attention_mask:
						_UpperCamelCase							:			Optional[int]     =   random_attention_mask([self.batch_size, self.seq_length]  )

				_UpperCamelCase							:			List[str]     =   None
				if self.use_token_type_ids:
						_UpperCamelCase							:			str     =   ids_tensor([self.batch_size, self.seq_length]	,       self.type_vocab_size  )

				_UpperCamelCase							:			str     =   RobertaPreLayerNormConfig(
				    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	,       is_decoder=_lowercase	,       initializer_range=self.initializer_range	,       )

				return config, input_ids, token_type_ids, attention_mask
		def _lowercase	(     self  )					->     List[Any]:
				_UpperCamelCase							:			List[Any]     =   self.prepare_config_and_inputs()
				_UpperCamelCase,				_UpperCamelCase,				_UpperCamelCase,				_UpperCamelCase							:			Any     =   config_and_inputs
				_UpperCamelCase							:			int     =   {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': attention_mask}
				return config, inputs_dict




		def _lowercase	(     self  )					->     Optional[Any]:
				_UpperCamelCase							:			List[str]     =   self.prepare_config_and_inputs()
				_UpperCamelCase,				_UpperCamelCase,				_UpperCamelCase,				_UpperCamelCase							:			Union[str, Any]     =   config_and_inputs

				_UpperCamelCase							:			Dict     =   True
				_UpperCamelCase							:			Optional[int]     =   floats_tensor([self.batch_size, self.seq_length, self.hidden_size]  )
				_UpperCamelCase							:			Dict     =   ids_tensor([self.batch_size, self.seq_length]	,       vocab_size=2  )

				return (
				    config,
				    input_ids,
				    token_type_ids,
				    encoder_hidden_states,
				    encoder_attention_mask,
				)







@require_flax
# Copied from tests.models.roberta.test_modelling_flax_roberta.FlaxRobertaPreLayerNormModelTest with ROBERTA->ROBERTA_PRELAYERNORM,Roberta->RobertaPreLayerNorm,roberta-base->andreasmadsen/efficient_mlm_m0.40
class       UpperCAmelCase					(       lowercase_	,		unittest.TestCase			):





		"""simple docstring"""





		A__	: str    =     True

		A__	: List[str]    =     (
		    (
		        FlaxRobertaPreLayerNormModel,
		        FlaxRobertaPreLayerNormForCausalLM,
		        FlaxRobertaPreLayerNormForMaskedLM,
		        FlaxRobertaPreLayerNormForSequenceClassification,
		        FlaxRobertaPreLayerNormForTokenClassification,
		        FlaxRobertaPreLayerNormForMultipleChoice,
		        FlaxRobertaPreLayerNormForQuestionAnswering,
		    )
		    if is_flax_available()
		    else ()
		)
		def _lowercase	(     self  )					->     Dict:
				_UpperCamelCase							:			List[str]     =   FlaxRobertaPreLayerNormModelTester(self  )




		@slow
		def _lowercase	(     self  )					->     List[Any]:
				for model_class_name in self.all_model_classes:
						_UpperCamelCase							:			List[Any]     =   model_class_name.from_pretrained('''andreasmadsen/efficient_mlm_m0.40'''	,       from_pt=_lowercase  )
						_UpperCamelCase							:			List[str]     =   model(np.ones((1, 1)  )  )
						self.assertIsNotNone(_lowercase  )







@require_flax
class       UpperCAmelCase					(       unittest.TestCase			):





		"""simple docstring"""





		@slow
		def _lowercase	(     self  )					->     Dict:
				_UpperCamelCase							:			int     =   FlaxRobertaPreLayerNormForMaskedLM.from_pretrained('''andreasmadsen/efficient_mlm_m0.40'''	,       from_pt=_lowercase  )

				_UpperCamelCase							:			List[str]     =   np.array([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]]	,       dtype=jnp.intaa  )
				_UpperCamelCase							:			Optional[Any]     =   model(_lowercase  )[0]
				_UpperCamelCase							:			Dict     =   [1, 11, 50265]
				self.assertEqual(list(output.shape  )	,       _lowercase  )
				# compare the actual values for a slice.
				_UpperCamelCase							:			Any     =   np.array(
				    [[[40.4_880, 18.0_199, -5.2_367], [-1.8_877, -4.0_885, 10.7_085], [-2.2_613, -5.6_110, 7.2_665]]]	,       dtype=np.floataa  )
				self.assertTrue(np.allclose(output[:, :3, :3]	,       _lowercase	,       atol=1E-4  )  )




		@slow
		def _lowercase	(     self  )					->     Optional[Any]:
				_UpperCamelCase							:			str     =   FlaxRobertaPreLayerNormModel.from_pretrained('''andreasmadsen/efficient_mlm_m0.40'''	,       from_pt=_lowercase  )

				_UpperCamelCase							:			List[Any]     =   np.array([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]]	,       dtype=jnp.intaa  )
				_UpperCamelCase							:			Tuple     =   model(_lowercase  )[0]
				# compare the actual values for a slice.
				_UpperCamelCase							:			List[str]     =   np.array(
				    [[[0.0_208, -0.0_356, 0.0_237], [-0.1_569, -0.0_411, -0.2_626], [0.1_879, 0.0_125, -0.0_089]]]	,       dtype=np.floataa  )
				self.assertTrue(np.allclose(output[:, :3, :3]	,       _lowercase	,       atol=1E-4  )  )








'''simple docstring'''

import unittest

from transformers import MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING, is_vision_available, pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_torch,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_vision_available():
							from PIL import Image
else:






							class    snake_case     :






											"""simple docstring"""



											@staticmethod
											def   a__					(    *_lowercase,     **_lowercase       )     ->							Tuple:
																		pass






@is_pipeline_test
@require_vision
@require_torch
class    snake_case     (      unittest.TestCase		):






				"""simple docstring"""
				_a    	=     MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING



				def   a__					(    self,     _lowercase,     _lowercase,     _lowercase       )     ->							Tuple:
											SCREAMING_SNAKE_CASE_				=				pipeline(
											    'zero-shot-object-detection',     model='hf-internal-testing/tiny-random-owlvit-object-detection'       )

											SCREAMING_SNAKE_CASE_				=				[
											    {
											        'image': './tests/fixtures/tests_samples/COCO/000000039769.png',
											        'candidate_labels': ['cat', 'remote', 'couch'],
											    }
											]
											return object_detector, examples



				def   a__					(    self,     _lowercase,     _lowercase       )     ->							str:
											SCREAMING_SNAKE_CASE_				=				object_detector(examples[0],     threshold=0.0       )

											SCREAMING_SNAKE_CASE_				=				len(_lowercase       )
											self.assertGreater(_lowercase,     0       )
											self.assertEqual(
											    _lowercase,     [
											        {
											            'score': ANY(_lowercase       ),
											            'label': ANY(_lowercase       ),
											            'box': {'xmin': ANY(_lowercase       ), 'ymin': ANY(_lowercase       ), 'xmax': ANY(_lowercase       ), 'ymax': ANY(_lowercase       )},
											        }
											        for i in range(_lowercase       )
											    ],     )



				@require_tf
				@unittest.skip('Zero Shot Object Detection not implemented in TF'       )
				def   a__					(    self       )     ->							Union[str, Any]:
											pass



				@require_torch
				def   a__					(    self       )     ->							str:
											SCREAMING_SNAKE_CASE_				=				pipeline(
											    'zero-shot-object-detection',     model='hf-internal-testing/tiny-random-owlvit-object-detection'       )

											SCREAMING_SNAKE_CASE_				=				object_detector(
											    './tests/fixtures/tests_samples/COCO/000000039769.png',     candidate_labels=['cat', 'remote', 'couch'],     threshold=0.64,     )

											self.assertEqual(
											    nested_simplify(_lowercase,     decimals=4       ),     [
											        {'score': 0.7_235, 'label': 'cat', 'box': {'xmin': 204, 'ymin': 167, 'xmax': 232, 'ymax': 190}},
											        {'score': 0.7_218, 'label': 'remote', 'box': {'xmin': 204, 'ymin': 167, 'xmax': 232, 'ymax': 190}},
											        {'score': 0.7_184, 'label': 'couch', 'box': {'xmin': 204, 'ymin': 167, 'xmax': 232, 'ymax': 190}},
											        {'score': 0.6_748, 'label': 'remote', 'box': {'xmin': 571, 'ymin': 83, 'xmax': 598, 'ymax': 103}},
											        {'score': 0.6_656, 'label': 'cat', 'box': {'xmin': 571, 'ymin': 83, 'xmax': 598, 'ymax': 103}},
											        {'score': 0.6_614, 'label': 'couch', 'box': {'xmin': 571, 'ymin': 83, 'xmax': 598, 'ymax': 103}},
											        {'score': 0.6_456, 'label': 'remote', 'box': {'xmin': 494, 'ymin': 105, 'xmax': 521, 'ymax': 127}},
											        {'score': 0.642, 'label': 'remote', 'box': {'xmin': 67, 'ymin': 274, 'xmax': 93, 'ymax': 297}},
											        {'score': 0.6_419, 'label': 'cat', 'box': {'xmin': 494, 'ymin': 105, 'xmax': 521, 'ymax': 127}},
											    ],     )

											SCREAMING_SNAKE_CASE_				=				object_detector(
											    [
											        {
											            'image': './tests/fixtures/tests_samples/COCO/000000039769.png',
											            'candidate_labels': ['cat', 'remote', 'couch'],
											        }
											    ],     threshold=0.64,     )

											self.assertEqual(
											    nested_simplify(_lowercase,     decimals=4       ),     [
											        [
											            {'score': 0.7_235, 'label': 'cat', 'box': {'xmin': 204, 'ymin': 167, 'xmax': 232, 'ymax': 190}},
											            {'score': 0.7_218, 'label': 'remote', 'box': {'xmin': 204, 'ymin': 167, 'xmax': 232, 'ymax': 190}},
											            {'score': 0.7_184, 'label': 'couch', 'box': {'xmin': 204, 'ymin': 167, 'xmax': 232, 'ymax': 190}},
											            {'score': 0.6_748, 'label': 'remote', 'box': {'xmin': 571, 'ymin': 83, 'xmax': 598, 'ymax': 103}},
											            {'score': 0.6_656, 'label': 'cat', 'box': {'xmin': 571, 'ymin': 83, 'xmax': 598, 'ymax': 103}},
											            {'score': 0.6_614, 'label': 'couch', 'box': {'xmin': 571, 'ymin': 83, 'xmax': 598, 'ymax': 103}},
											            {'score': 0.6_456, 'label': 'remote', 'box': {'xmin': 494, 'ymin': 105, 'xmax': 521, 'ymax': 127}},
											            {'score': 0.642, 'label': 'remote', 'box': {'xmin': 67, 'ymin': 274, 'xmax': 93, 'ymax': 297}},
											            {'score': 0.6_419, 'label': 'cat', 'box': {'xmin': 494, 'ymin': 105, 'xmax': 521, 'ymax': 127}},
											        ]
											    ],     )



				@require_torch
				@slow
				def   a__					(    self       )     ->							Optional[int]:
											SCREAMING_SNAKE_CASE_				=				pipeline('zero-shot-object-detection'       )

											SCREAMING_SNAKE_CASE_				=				object_detector(
											    'http://images.cocodataset.org/val2017/000000039769.jpg',     candidate_labels=['cat', 'remote', 'couch'],     )
											self.assertEqual(
											    nested_simplify(_lowercase,     decimals=4       ),     [
											        {'score': 0.2_868, 'label': 'cat', 'box': {'xmin': 324, 'ymin': 20, 'xmax': 640, 'ymax': 373}},
											        {'score': 0.277, 'label': 'remote', 'box': {'xmin': 40, 'ymin': 72, 'xmax': 177, 'ymax': 115}},
											        {'score': 0.2_537, 'label': 'cat', 'box': {'xmin': 1, 'ymin': 55, 'xmax': 315, 'ymax': 472}},
											        {'score': 0.1_474, 'label': 'remote', 'box': {'xmin': 335, 'ymin': 74, 'xmax': 371, 'ymax': 187}},
											        {'score': 0.1_208, 'label': 'couch', 'box': {'xmin': 4, 'ymin': 0, 'xmax': 642, 'ymax': 476}},
											    ],     )

											SCREAMING_SNAKE_CASE_				=				object_detector(
											    [
											        {
											            'image': 'http://images.cocodataset.org/val2017/000000039769.jpg',
											            'candidate_labels': ['cat', 'remote', 'couch'],
											        },
											        {
											            'image': 'http://images.cocodataset.org/val2017/000000039769.jpg',
											            'candidate_labels': ['cat', 'remote', 'couch'],
											        },
											    ],     )
											self.assertEqual(
											    nested_simplify(_lowercase,     decimals=4       ),     [
											        [
											            {'score': 0.2_868, 'label': 'cat', 'box': {'xmin': 324, 'ymin': 20, 'xmax': 640, 'ymax': 373}},
											            {'score': 0.277, 'label': 'remote', 'box': {'xmin': 40, 'ymin': 72, 'xmax': 177, 'ymax': 115}},
											            {'score': 0.2_537, 'label': 'cat', 'box': {'xmin': 1, 'ymin': 55, 'xmax': 315, 'ymax': 472}},
											            {'score': 0.1_474, 'label': 'remote', 'box': {'xmin': 335, 'ymin': 74, 'xmax': 371, 'ymax': 187}},
											            {'score': 0.1_208, 'label': 'couch', 'box': {'xmin': 4, 'ymin': 0, 'xmax': 642, 'ymax': 476}},
											        ],
											        [
											            {'score': 0.2_868, 'label': 'cat', 'box': {'xmin': 324, 'ymin': 20, 'xmax': 640, 'ymax': 373}},
											            {'score': 0.277, 'label': 'remote', 'box': {'xmin': 40, 'ymin': 72, 'xmax': 177, 'ymax': 115}},
											            {'score': 0.2_537, 'label': 'cat', 'box': {'xmin': 1, 'ymin': 55, 'xmax': 315, 'ymax': 472}},
											            {'score': 0.1_474, 'label': 'remote', 'box': {'xmin': 335, 'ymin': 74, 'xmax': 371, 'ymax': 187}},
											            {'score': 0.1_208, 'label': 'couch', 'box': {'xmin': 4, 'ymin': 0, 'xmax': 642, 'ymax': 476}},
											        ],
											    ],     )



				@require_tf
				@unittest.skip('Zero Shot Object Detection not implemented in TF'       )
				def   a__					(    self       )     ->							Optional[Any]:
											pass



				@require_torch
				@slow
				def   a__					(    self       )     ->							Any:
											SCREAMING_SNAKE_CASE_				=				0.2
											SCREAMING_SNAKE_CASE_				=				pipeline('zero-shot-object-detection'       )

											SCREAMING_SNAKE_CASE_				=				object_detector(
											    'http://images.cocodataset.org/val2017/000000039769.jpg',     candidate_labels=['cat', 'remote', 'couch'],     threshold=_lowercase,     )
											self.assertEqual(
											    nested_simplify(_lowercase,     decimals=4       ),     [
											        {'score': 0.2_868, 'label': 'cat', 'box': {'xmin': 324, 'ymin': 20, 'xmax': 640, 'ymax': 373}},
											        {'score': 0.277, 'label': 'remote', 'box': {'xmin': 40, 'ymin': 72, 'xmax': 177, 'ymax': 115}},
											        {'score': 0.2_537, 'label': 'cat', 'box': {'xmin': 1, 'ymin': 55, 'xmax': 315, 'ymax': 472}},
											    ],     )





				@require_torch
				@slow
				def   a__					(    self       )     ->							int:
											SCREAMING_SNAKE_CASE_				=				2
											SCREAMING_SNAKE_CASE_				=				pipeline('zero-shot-object-detection'       )

											SCREAMING_SNAKE_CASE_				=				object_detector(
											    'http://images.cocodataset.org/val2017/000000039769.jpg',     candidate_labels=['cat', 'remote', 'couch'],     top_k=_lowercase,     )
											self.assertEqual(
											    nested_simplify(_lowercase,     decimals=4       ),     [
											        {'score': 0.2_868, 'label': 'cat', 'box': {'xmin': 324, 'ymin': 20, 'xmax': 640, 'ymax': 373}},
											        {'score': 0.277, 'label': 'remote', 'box': {'xmin': 40, 'ymin': 72, 'xmax': 177, 'ymax': 115}},
											    ],     )





