"""simple docstring"""






import unittest

import numpy as np

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

from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
						import jax.numpy as jnp

						from transformers.models.albert.modeling_flax_albert import (
						    FlaxAlbertForMaskedLM,
						    FlaxAlbertForMultipleChoice,
						    FlaxAlbertForPreTraining,
						    FlaxAlbertForQuestionAnswering,
						    FlaxAlbertForSequenceClassification,
						    FlaxAlbertForTokenClassification,
						    FlaxAlbertModel,
						)
class 				snake_case       ( unittest.TestCase				):




					"""simple docstring"""

					def __init__(				self   :	Optional[Any]    ,lowerCamelCase__   :	Dict    ,lowerCamelCase__   :	List[str]=13    ,lowerCamelCase__   :	Tuple=7    ,lowerCamelCase__   :	Dict=True    ,lowerCamelCase__   :	Dict=True    ,lowerCamelCase__   :	str=True    ,lowerCamelCase__   :	List[Any]=True    ,lowerCamelCase__   :	Dict=99    ,lowerCamelCase__   :	str=32    ,lowerCamelCase__   :	Union[str, Any]=5    ,lowerCamelCase__   :	Any=4    ,lowerCamelCase__   :	Any=37    ,lowerCamelCase__   :	int="gelu"    ,lowerCamelCase__   :	int=0.1    ,lowerCamelCase__   :	Tuple=0.1    ,lowerCamelCase__   :	int=512    ,lowerCamelCase__   :	Tuple=16    ,lowerCamelCase__   :	Optional[Any]=2    ,lowerCamelCase__   :	Optional[int]=0.0_2    ,lowerCamelCase__   :	Tuple=4    ,):
									UpperCAmelCase__             =							parent
									UpperCAmelCase__             =							batch_size
									UpperCAmelCase__             =							seq_length
									UpperCAmelCase__             =							is_training
									UpperCAmelCase__             =							use_attention_mask
									UpperCAmelCase__             =							use_token_type_ids
									UpperCAmelCase__             =							use_labels
									UpperCAmelCase__             =							vocab_size
									UpperCAmelCase__             =							hidden_size
									UpperCAmelCase__             =							num_hidden_layers
									UpperCAmelCase__             =							num_attention_heads
									UpperCAmelCase__             =							intermediate_size
									UpperCAmelCase__             =							hidden_act
									UpperCAmelCase__             =							hidden_dropout_prob
									UpperCAmelCase__             =							attention_probs_dropout_prob
									UpperCAmelCase__             =							max_position_embeddings
									UpperCAmelCase__             =							type_vocab_size
									UpperCAmelCase__             =							type_sequence_label_size
									UpperCAmelCase__             =							initializer_range
									UpperCAmelCase__             =							num_choices







					def 		__lowerCAmelCase						(				self   :	Union[str, Any]			):
									UpperCAmelCase__             =							ids_tensor([self.batch_size, self.seq_length]    ,self.vocab_size			)

									UpperCAmelCase__             =							None
									if self.use_attention_mask:
													UpperCAmelCase__             =							random_attention_mask([self.batch_size, self.seq_length]			)

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

									UpperCAmelCase__             =							AlbertConfig(
									    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=lowerCamelCase__    ,initializer_range=self.initializer_range    ,)

									return config, input_ids, token_type_ids, attention_mask







					def 		__lowerCAmelCase						(				self   :	Optional[int]			):
									UpperCAmelCase__             =							self.prepare_config_and_inputs()
									UpperCAmelCase__      ,	UpperCAmelCase__      ,	UpperCAmelCase__      ,	UpperCAmelCase__             =							config_and_inputs
									UpperCAmelCase__             =							{'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': attention_mask}
									return config, inputs_dict






@require_flax
class 				snake_case       ( __UpperCAmelCase      ,    unittest.TestCase				):




					"""simple docstring"""

					snake_case__				    = (
					    (
					        FlaxAlbertModel,
					        FlaxAlbertForPreTraining,
					        FlaxAlbertForMaskedLM,
					        FlaxAlbertForMultipleChoice,
					        FlaxAlbertForQuestionAnswering,
					        FlaxAlbertForSequenceClassification,
					        FlaxAlbertForTokenClassification,
					        FlaxAlbertForQuestionAnswering,
					    )
					    if is_flax_available()
					    else ()
					)







					def 		__lowerCAmelCase						(				self   :	str			):
									UpperCAmelCase__             =							FlaxAlbertModelTester(self			)







					@slow
					def 		__lowerCAmelCase						(				self   :	Optional[int]			):
									for model_class_name in self.all_model_classes:
													UpperCAmelCase__             =							model_class_name.from_pretrained('albert-base-v2'			)
													UpperCAmelCase__             =							model(np.ones((1, 1)			)			)
													self.assertIsNotNone(lowerCamelCase__			)






@require_flax
class 				snake_case       ( unittest.TestCase				):




					"""simple docstring"""

					@slow
					def 		__lowerCAmelCase						(				self   :	List[str]			):
									UpperCAmelCase__             =							FlaxAlbertModel.from_pretrained('albert-base-v2'			)
									UpperCAmelCase__             =							np.array([[0, 345, 232, 328, 740, 140, 1_695, 69, 6_078, 1_588, 2]]			)
									UpperCAmelCase__             =							np.array([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]			)
									UpperCAmelCase__             =							model(lowerCamelCase__    ,attention_mask=lowerCamelCase__			)[0]
									UpperCAmelCase__             =							(1, 11, 768)
									self.assertEqual(output.shape    ,lowerCamelCase__			)
									UpperCAmelCase__             =							np.array(
									    [[[-0.6_5_1_3, 1.5_0_3_5, -0.2_7_6_6], [-0.6_5_1_5, 1.5_0_4_6, -0.2_7_8_0], [-0.6_5_1_2, 1.5_0_4_9, -0.2_7_8_4]]]			)

									self.assertTrue(jnp.allclose(output[:, 1:4, 1:4]    ,lowerCamelCase__    ,atol=1e-4			)			)




"""simple docstring"""






import hashlib
import unittest
from typing import Dict

import numpy as np

from transformers import (
    MODEL_FOR_MASK_GENERATION_MAPPING,
    TF_MODEL_FOR_MASK_GENERATION_MAPPING,
    is_vision_available,
    pipeline,
)
from transformers.pipelines import MaskGenerationPipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_torch,
    require_vision,
    slow,
)


if is_vision_available():
						from PIL import Image
else:
						class 				snake_case       :




											"""simple docstring"""

											@staticmethod
											def 		__lowerCAmelCase						(				*lowerCamelCase__   :	List[Any]    ,**lowerCamelCase__   :	Union[str, Any]			):
															pass



def   a_						(					lowerCamelCase       ):
				UpperCAmelCase__             =							hashlib.mda(image.tobytes()       )
				return m.hexdigest()[:1_0]



def   a_						(					lowerCamelCase       ):
				UpperCAmelCase__             =							np.array(lowerCamelCase       )
				UpperCAmelCase__             =							npimg.shape
				return {"hash": hashimage(lowerCamelCase       ), "shape": shape}







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




					"""simple docstring"""

					snake_case__				    = dict(
					    (list(MODEL_FOR_MASK_GENERATION_MAPPING.items()				) if MODEL_FOR_MASK_GENERATION_MAPPING else [])				)
					snake_case__				    = dict(
					    (list(TF_MODEL_FOR_MASK_GENERATION_MAPPING.items()				) if TF_MODEL_FOR_MASK_GENERATION_MAPPING else [])				)







					def 		__lowerCAmelCase						(				self   :	List[Any]    ,lowerCamelCase__   :	Dict    ,lowerCamelCase__   :	Optional[int]    ,lowerCamelCase__   :	str			):
									UpperCAmelCase__             =							MaskGenerationPipeline(model=lowerCamelCase__    ,image_processor=lowerCamelCase__			)
									return image_segmenter, [
									    "./tests/fixtures/tests_samples/COCO/000000039769.png",
									    "./tests/fixtures/tests_samples/COCO/000000039769.png",
									]







					def 		__lowerCAmelCase						(				self   :	Union[str, Any]    ,lowerCamelCase__   :	Tuple    ,lowerCamelCase__   :	str			):
									pass







					@require_tf
					@unittest.skip('Image segmentation not implemented in TF'			)
					def 		__lowerCAmelCase						(				self   :	Optional[Any]			):
									pass







					@slow
					@require_torch
					def 		__lowerCAmelCase						(				self   :	List[str]			):
									UpperCAmelCase__             =							pipeline('mask-generation'    ,model='facebook/sam-vit-huge'			)

									UpperCAmelCase__             =							image_segmenter('http://images.cocodataset.org/val2017/000000039769.jpg'    ,points_per_batch=256			)

									# Shortening by hashing
									UpperCAmelCase__             =							[]
									for i, o in enumerate(outputs['masks']			):
													new_outupt += [{"mask": mask_to_test_readable(lowerCamelCase__			), "scores": outputs["scores"][i]}]

									# fmt: off
									self.assertEqual(
									    nested_simplify(lowerCamelCase__    ,decimals=4			)    ,[
									        {'mask': {'hash': '115ad19f5f', 'shape': (480, 640)}, 'scores': 1.0_4_4_4},
									        {'mask': {'hash': '6affa964c6', 'shape': (480, 640)}, 'scores': 1.0_2_1},
									        {'mask': {'hash': 'dfe28a0388', 'shape': (480, 640)}, 'scores': 1.0_1_6_7},
									        {'mask': {'hash': 'c0a5f4a318', 'shape': (480, 640)}, 'scores': 1.0_1_3_2},
									        {'mask': {'hash': 'fe8065c197', 'shape': (480, 640)}, 'scores': 1.0_0_5_3},
									        {'mask': {'hash': 'e2d0b7a0b7', 'shape': (480, 640)}, 'scores': 0.9_9_6_7},
									        {'mask': {'hash': '453c7844bd', 'shape': (480, 640)}, 'scores': 0.9_9_3},
									        {'mask': {'hash': '3d44f2926d', 'shape': (480, 640)}, 'scores': 0.9_9_0_9},
									        {'mask': {'hash': '64033ddc3f', 'shape': (480, 640)}, 'scores': 0.9_8_7_9},
									        {'mask': {'hash': '801064ff79', 'shape': (480, 640)}, 'scores': 0.9_8_3_4},
									        {'mask': {'hash': '6172f276ef', 'shape': (480, 640)}, 'scores': 0.9_7_1_6},
									        {'mask': {'hash': 'b49e60e084', 'shape': (480, 640)}, 'scores': 0.9_6_1_2},
									        {'mask': {'hash': 'a811e775fd', 'shape': (480, 640)}, 'scores': 0.9_5_9_9},
									        {'mask': {'hash': 'a6a8ebcf4b', 'shape': (480, 640)}, 'scores': 0.9_5_5_2},
									        {'mask': {'hash': '9d8257e080', 'shape': (480, 640)}, 'scores': 0.9_5_3_2},
									        {'mask': {'hash': '32de6454a8', 'shape': (480, 640)}, 'scores': 0.9_5_1_6},
									        {'mask': {'hash': 'af3d4af2c8', 'shape': (480, 640)}, 'scores': 0.9_4_9_9},
									        {'mask': {'hash': '3c6db475fb', 'shape': (480, 640)}, 'scores': 0.9_4_8_3},
									        {'mask': {'hash': 'c290813fb9', 'shape': (480, 640)}, 'scores': 0.9_4_6_4},
									        {'mask': {'hash': 'b6f0b8f606', 'shape': (480, 640)}, 'scores': 0.9_4_3},
									        {'mask': {'hash': '92ce16bfdf', 'shape': (480, 640)}, 'scores': 0.9_4_3},
									        {'mask': {'hash': 'c749b25868', 'shape': (480, 640)}, 'scores': 0.9_4_0_8},
									        {'mask': {'hash': 'efb6cab859', 'shape': (480, 640)}, 'scores': 0.9_3_3_5},
									        {'mask': {'hash': '1ff2eafb30', 'shape': (480, 640)}, 'scores': 0.9_3_2_6},
									        {'mask': {'hash': '788b798e24', 'shape': (480, 640)}, 'scores': 0.9_2_6_2},
									        {'mask': {'hash': 'abea804f0e', 'shape': (480, 640)}, 'scores': 0.8_9_9_9},
									        {'mask': {'hash': '7b9e8ddb73', 'shape': (480, 640)}, 'scores': 0.8_9_8_6},
									        {'mask': {'hash': 'cd24047c8a', 'shape': (480, 640)}, 'scores': 0.8_9_8_4},
									        {'mask': {'hash': '6943e6bcbd', 'shape': (480, 640)}, 'scores': 0.8_8_7_3},
									        {'mask': {'hash': 'b5f47c9191', 'shape': (480, 640)}, 'scores': 0.8_8_7_1}
									    ]    ,)
									# fmt: on







					@require_torch
					@slow
					def 		__lowerCAmelCase						(				self   :	Optional[Any]			):
									UpperCAmelCase__             =							'facebook/sam-vit-huge'
									UpperCAmelCase__             =							pipeline('mask-generation'    ,model=lowerCamelCase__			)

									UpperCAmelCase__             =							image_segmenter(
									    'http://images.cocodataset.org/val2017/000000039769.jpg'    ,pred_iou_thresh=1    ,points_per_batch=256			)

									# Shortening by hashing
									UpperCAmelCase__             =							[]
									for i, o in enumerate(outputs['masks']			):
													new_outupt += [{"mask": mask_to_test_readable(lowerCamelCase__			), "scores": outputs["scores"][i]}]

									self.assertEqual(
									    nested_simplify(lowerCamelCase__    ,decimals=4			)    ,[
									        {'mask': {'hash': '115ad19f5f', 'shape': (480, 640)}, 'scores': 1.0_4_4_4},
									        {'mask': {'hash': '6affa964c6', 'shape': (480, 640)}, 'scores': 1.0_2_1_0},
									        {'mask': {'hash': 'dfe28a0388', 'shape': (480, 640)}, 'scores': 1.0_1_6_7},
									        {'mask': {'hash': 'c0a5f4a318', 'shape': (480, 640)}, 'scores': 1.0_1_3_2},
									        {'mask': {'hash': 'fe8065c197', 'shape': (480, 640)}, 'scores': 1.0_0_5_3},
									    ]    ,)



