
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       _snake_case					(     unittest.TestCase				):







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







			def 			SCREAMING_SNAKE_CASE    (				self		):
				__magic_name__		:		Optional[int]	   =     ids_tensor([self.batch_size, self.seq_length]						,   self.vocab_size		)

				__magic_name__		:		List[Any]	   =     None
				if self.use_attention_mask:
					__magic_name__		:		List[str]	   =     random_attention_mask([self.batch_size, self.seq_length]		)

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

				__magic_name__		:		List[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=_a						,   initializer_range=self.initializer_range						,   )

				return config, input_ids, token_type_ids, attention_mask







			def 			SCREAMING_SNAKE_CASE    (				self		):
				__magic_name__		:		int	   =     self.prepare_config_and_inputs()
				__magic_name__   ,    __magic_name__   ,    __magic_name__   ,    __magic_name__		:		List[Any]	   =     config_and_inputs
				__magic_name__		:		List[str]	   =     {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": attention_mask}
				return config, inputs_dict







			def 			SCREAMING_SNAKE_CASE    (				self		):
				__magic_name__		:		Optional[int]	   =     self.prepare_config_and_inputs()
				__magic_name__   ,    __magic_name__   ,    __magic_name__   ,    __magic_name__		:		Union[str, Any]	   =     config_and_inputs

				__magic_name__		:		Tuple	   =     True
				__magic_name__		:		int	   =     floats_tensor([self.batch_size, self.seq_length, self.hidden_size]		)
				__magic_name__		:		Optional[Any]	   =     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       _snake_case					(     snake_case				,						unittest.TestCase				):
			UpperCamelCase__          =    True

			UpperCamelCase__          =    (
			    (
			        FlaxRobertaPreLayerNormModel,
			        FlaxRobertaPreLayerNormForCausalLM,
			        FlaxRobertaPreLayerNormForMaskedLM,
			        FlaxRobertaPreLayerNormForSequenceClassification,
			        FlaxRobertaPreLayerNormForTokenClassification,
			        FlaxRobertaPreLayerNormForMultipleChoice,
			        FlaxRobertaPreLayerNormForQuestionAnswering,
			    )
			    if is_flax_available()
			    else ()
			)







			def 			SCREAMING_SNAKE_CASE    (				self		):
				__magic_name__		:		Optional[Any]	   =     FlaxRobertaPreLayerNormModelTester(self		)







			@slow
			def 			SCREAMING_SNAKE_CASE    (				self		):
				for model_class_name in self.all_model_classes:
					__magic_name__		:		Optional[Any]	   =     model_class_name.from_pretrained("andreasmadsen/efficient_mlm_m0.40"						,   from_pt=_a		)
					__magic_name__		:		Dict	   =     model(np.ones((1, 1)		)		)
					self.assertIsNotNone(_a		)







@require_flax
class       _snake_case					(     unittest.TestCase				):







			@slow
			def 			SCREAMING_SNAKE_CASE    (				self		):
				__magic_name__		:		Dict	   =     FlaxRobertaPreLayerNormForMaskedLM.from_pretrained("andreasmadsen/efficient_mlm_m0.40"						,   from_pt=_a		)

				__magic_name__		:		Union[str, Any]	   =     np.array([[0, 31_414, 232, 328, 740, 1_140, 12_695, 69, 46_078, 1_588, 2]]						,   dtype=jnp.intaa		)
				__magic_name__		:		List[str]	   =     model(_a		)[0]
				__magic_name__		:		str	   =     [1, 11, 50_265]
				self.assertEqual(list(output.shape		)						,   _a		)
				# compare the actual values for a slice.
				__magic_name__		:		List[str]	   =     np.array(
				    [[[40.48_80, 18.01_99, -5.23_67], [-1.88_77, -4.08_85, 10.70_85], [-2.26_13, -5.61_10, 7.26_65]]]						,   dtype=np.floataa		)
				self.assertTrue(np.allclose(output[:, :3, :3]						,   _a						,   atol=1e-4		)		)







			@slow
			def 			SCREAMING_SNAKE_CASE    (				self		):
				__magic_name__		:		List[str]	   =     FlaxRobertaPreLayerNormModel.from_pretrained("andreasmadsen/efficient_mlm_m0.40"						,   from_pt=_a		)

				__magic_name__		:		Tuple	   =     np.array([[0, 31_414, 232, 328, 740, 1_140, 12_695, 69, 46_078, 1_588, 2]]						,   dtype=jnp.intaa		)
				__magic_name__		:		Tuple	   =     model(_a		)[0]
				# compare the actual values for a slice.
				__magic_name__		:		Dict	   =     np.array(
				    [[[0.02_08, -0.03_56, 0.02_37], [-0.15_69, -0.04_11, -0.26_26], [0.18_79, 0.01_25, -0.00_89]]]						,   dtype=np.floataa		)
				self.assertTrue(np.allclose(output[:, :3, :3]						,   _a						,   atol=1e-4		)		)


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					:







							@staticmethod
							def 			SCREAMING_SNAKE_CASE    (				*_a						,   **_a		):
								pass






def   lowerCAmelCase_						(					_snake_case      :  Image   )				->		str:







	'''simple docstring'''


	__magic_name__		:		Optional[int]	   =     hashlib.mda(image.tobytes()   )
	return m.hexdigest()[:10]






def   lowerCAmelCase_						(					_snake_case      :  Image   )				->		Dict:







	'''simple docstring'''


	__magic_name__		:		List[Any]	   =     np.array(_snake_case   )
	__magic_name__		:		Optional[int]	   =     npimg.shape
	return {"hash": hashimage(_snake_case   ), "shape": shape}





@is_pipeline_test
@require_vision
@require_torch
class       _snake_case					(     unittest.TestCase				):
			UpperCamelCase__          =    dict(
			    (list(MODEL_FOR_MASK_GENERATION_MAPPING.items()				) if MODEL_FOR_MASK_GENERATION_MAPPING else [])				)
			UpperCamelCase__          =    dict(
			    (list(TF_MODEL_FOR_MASK_GENERATION_MAPPING.items()				) if TF_MODEL_FOR_MASK_GENERATION_MAPPING else [])				)







			def 			SCREAMING_SNAKE_CASE    (				self						,   _a						,   _a						,   _a		):
				__magic_name__		:		Dict	   =     MaskGenerationPipeline(model=_a						,   image_processor=_a		)
				return image_segmenter, [
				    "./tests/fixtures/tests_samples/COCO/000000039769.png",
				    "./tests/fixtures/tests_samples/COCO/000000039769.png",
				]







			def 			SCREAMING_SNAKE_CASE    (				self						,   _a						,   _a		):
				pass







			@require_tf
			@unittest.skip("Image segmentation not implemented in TF"		)
			def 			SCREAMING_SNAKE_CASE    (				self		):
				pass







			@slow
			@require_torch
			def 			SCREAMING_SNAKE_CASE    (				self		):
				__magic_name__		:		Dict	   =     pipeline("mask-generation"						,   model="facebook/sam-vit-huge"		)

				__magic_name__		:		str	   =     image_segmenter("http://images.cocodataset.org/val2017/000000039769.jpg"						,   points_per_batch=256		)

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

				# fmt: off
				self.assertEqual(
				    nested_simplify(_a						,   decimals=4		)						,   [
				        {"mask": {"hash": "115ad19f5f", "shape": (480, 640)}, "scores": 1.04_44},
				        {"mask": {"hash": "6affa964c6", "shape": (480, 640)}, "scores": 1.0_21},
				        {"mask": {"hash": "dfe28a0388", "shape": (480, 640)}, "scores": 1.01_67},
				        {"mask": {"hash": "c0a5f4a318", "shape": (480, 640)}, "scores": 1.01_32},
				        {"mask": {"hash": "fe8065c197", "shape": (480, 640)}, "scores": 1.00_53},
				        {"mask": {"hash": "e2d0b7a0b7", "shape": (480, 640)}, "scores": 0.99_67},
				        {"mask": {"hash": "453c7844bd", "shape": (480, 640)}, "scores": 0.9_93},
				        {"mask": {"hash": "3d44f2926d", "shape": (480, 640)}, "scores": 0.99_09},
				        {"mask": {"hash": "64033ddc3f", "shape": (480, 640)}, "scores": 0.98_79},
				        {"mask": {"hash": "801064ff79", "shape": (480, 640)}, "scores": 0.98_34},
				        {"mask": {"hash": "6172f276ef", "shape": (480, 640)}, "scores": 0.97_16},
				        {"mask": {"hash": "b49e60e084", "shape": (480, 640)}, "scores": 0.96_12},
				        {"mask": {"hash": "a811e775fd", "shape": (480, 640)}, "scores": 0.95_99},
				        {"mask": {"hash": "a6a8ebcf4b", "shape": (480, 640)}, "scores": 0.95_52},
				        {"mask": {"hash": "9d8257e080", "shape": (480, 640)}, "scores": 0.95_32},
				        {"mask": {"hash": "32de6454a8", "shape": (480, 640)}, "scores": 0.95_16},
				        {"mask": {"hash": "af3d4af2c8", "shape": (480, 640)}, "scores": 0.94_99},
				        {"mask": {"hash": "3c6db475fb", "shape": (480, 640)}, "scores": 0.94_83},
				        {"mask": {"hash": "c290813fb9", "shape": (480, 640)}, "scores": 0.94_64},
				        {"mask": {"hash": "b6f0b8f606", "shape": (480, 640)}, "scores": 0.9_43},
				        {"mask": {"hash": "92ce16bfdf", "shape": (480, 640)}, "scores": 0.9_43},
				        {"mask": {"hash": "c749b25868", "shape": (480, 640)}, "scores": 0.94_08},
				        {"mask": {"hash": "efb6cab859", "shape": (480, 640)}, "scores": 0.93_35},
				        {"mask": {"hash": "1ff2eafb30", "shape": (480, 640)}, "scores": 0.93_26},
				        {"mask": {"hash": "788b798e24", "shape": (480, 640)}, "scores": 0.92_62},
				        {"mask": {"hash": "abea804f0e", "shape": (480, 640)}, "scores": 0.89_99},
				        {"mask": {"hash": "7b9e8ddb73", "shape": (480, 640)}, "scores": 0.89_86},
				        {"mask": {"hash": "cd24047c8a", "shape": (480, 640)}, "scores": 0.89_84},
				        {"mask": {"hash": "6943e6bcbd", "shape": (480, 640)}, "scores": 0.88_73},
				        {"mask": {"hash": "b5f47c9191", "shape": (480, 640)}, "scores": 0.88_71}
				    ]						,   )
				# fmt: on







			@require_torch
			@slow
			def 			SCREAMING_SNAKE_CASE    (				self		):
				__magic_name__		:		str	   =     "facebook/sam-vit-huge"
				__magic_name__		:		str	   =     pipeline("mask-generation"						,   model=_a		)

				__magic_name__		:		Tuple	   =     image_segmenter(
				    "http://images.cocodataset.org/val2017/000000039769.jpg"						,   pred_iou_thresh=1						,   points_per_batch=256		)

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

				self.assertEqual(
				    nested_simplify(_a						,   decimals=4		)						,   [
				        {"mask": {"hash": "115ad19f5f", "shape": (480, 640)}, "scores": 1.04_44},
				        {"mask": {"hash": "6affa964c6", "shape": (480, 640)}, "scores": 1.02_10},
				        {"mask": {"hash": "dfe28a0388", "shape": (480, 640)}, "scores": 1.01_67},
				        {"mask": {"hash": "c0a5f4a318", "shape": (480, 640)}, "scores": 1.01_32},
				        {"mask": {"hash": "fe8065c197", "shape": (480, 640)}, "scores": 1.00_53},
				    ]						,   )
