"""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 						__A							:






          @staticmethod
          def     __A  (		*a__				,     **a__  ):
            pass





def 							SCREAMING_SNAKE_CASE     (			_lowerCamelCase						:       Image	)       ->      str:
  _lowerCAmelCase						:       str  			=     hashlib.mda(image.tobytes()	)
  return m.hexdigest()[:10]





def 							SCREAMING_SNAKE_CASE     (			_lowerCamelCase						:       Image	)       ->      Dict:
  _lowerCAmelCase						:       Dict  			=     np.array(_lowerCamelCase	)
  _lowerCAmelCase						:       Union[str, Any]  			=     npimg.shape
  return {"hash": hashimage(_lowerCamelCase	), "shape": shape}






@is_pipeline_test
@require_vision
@require_torch
class 						__A							( unittest.TestCase		):
     _UpperCamelCase							:			Optional[Any]							=		dict(
         (list(MODEL_FOR_MASK_GENERATION_MAPPING.items()		) if MODEL_FOR_MASK_GENERATION_MAPPING else [])		)
     _UpperCamelCase							:			Union[str, Any]							=		dict(
         (list(TF_MODEL_FOR_MASK_GENERATION_MAPPING.items()		) if TF_MODEL_FOR_MASK_GENERATION_MAPPING else [])		)






     def     __A  (		self				,     a__				,     a__				,     a__  ):
       _lowerCAmelCase						:       Union[str, Any]  			=     MaskGenerationPipeline(model=a__				,     image_processor=a__  )
       return image_segmenter, [
           "./tests/fixtures/tests_samples/COCO/000000039769.png",
           "./tests/fixtures/tests_samples/COCO/000000039769.png",
       ]






     def     __A  (		self				,     a__				,     a__  ):
       pass






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






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

       _lowerCAmelCase						:       Optional[int]  			=     image_segmenter("""http://images.cocodataset.org/val2017/000000039769.jpg"""				,     points_per_batch=256  )

       # Shortening by hashing
       _lowerCAmelCase						:       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""": 1.0_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     __A  (		self  ):
       _lowerCAmelCase						:       Optional[int]  			=     """facebook/sam-vit-huge"""
       _lowerCAmelCase						:       Any  			=     pipeline("""mask-generation"""				,     model=a__  )

       _lowerCAmelCase						:       Optional[int]  			=     image_segmenter(
           """http://images.cocodataset.org/val2017/000000039769.jpg"""				,     pred_iou_thresh=1				,     points_per_batch=256  )

       # Shortening by hashing
       _lowerCAmelCase						:       Tuple  			=     []
       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""": 1.0_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},
           ]				,     )









'''simple docstring'''




import unittest

import numpy as np

from transformers import DistilBertConfig, 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.distilbert.modeling_flax_distilbert import (
						    FlaxDistilBertForMaskedLM,
						    FlaxDistilBertForMultipleChoice,
						    FlaxDistilBertForQuestionAnswering,
						    FlaxDistilBertForSequenceClassification,
						    FlaxDistilBertForTokenClassification,
						    FlaxDistilBertModel,
						)







class 	_lowerCAmelCase							(			unittest.TestCase ):


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


	def     _a	(self							):
				A_	:						List[str]	=     ids_tensor([self.batch_size, self.seq_length]       ,      self.vocab_size							)

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

				A_	:						Union[str, Any]	=     DistilBertConfig(
				    vocab_size=self.vocab_size       ,      dim=self.hidden_size       ,      n_layers=self.num_hidden_layers       ,      n_heads=self.num_attention_heads       ,      hidden_dim=self.intermediate_size       ,      hidden_act=self.hidden_act       ,      dropout=self.hidden_dropout_prob       ,      attention_dropout=self.attention_probs_dropout_prob       ,      max_position_embeddings=self.max_position_embeddings       ,      initializer_range=self.initializer_range       ,      tie_weights_=lowercase       ,      )

				return config, input_ids, attention_mask


	def     _a	(self							):
				A_	:						List[str]	=     self.prepare_config_and_inputs()
				A_,    A_,    A_	:						str	=     config_and_inputs
				A_	:						Any	=     {"""input_ids""": input_ids, """attention_mask""": attention_mask}
				return config, inputs_dict







@require_flax
class 	_lowerCAmelCase							(			__UpperCAmelCase      ,			unittest.TestCase ):
	__SCREAMING_SNAKE_CASE   :		Optional[Any]					=					(
	    (
	        FlaxDistilBertModel,
	        FlaxDistilBertForMaskedLM,
	        FlaxDistilBertForMultipleChoice,
	        FlaxDistilBertForQuestionAnswering,
	        FlaxDistilBertForSequenceClassification,
	        FlaxDistilBertForTokenClassification,
	        FlaxDistilBertForQuestionAnswering,
	    )
	    if is_flax_available()
	    else ()
	)


	def     _a	(self							):
				A_	:						Tuple	=     FlaxDistilBertModelTester(self							)


	@slow
	def     _a	(self							):
				for model_class_name in self.all_model_classes:
							A_	:						Union[str, Any]	=     model_class_name.from_pretrained("""distilbert-base-uncased"""							)
							A_	:						Any	=     model(np.ones((1, 1)							)							)
							self.assertIsNotNone(lowercase							)







@require_flax
class 	_lowerCAmelCase							(			unittest.TestCase ):


	@slow
	def     _a	(self							):
				A_	:						List[str]	=     FlaxDistilBertModel.from_pretrained("""distilbert-base-uncased"""							)
				A_	:						Optional[Any]	=     np.array([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 1588, 2]]							)
				A_	:						int	=     np.array([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]							)
				A_	:						Optional[int]	=     model(lowercase       ,      attention_mask=lowercase							)[0]
				A_	:						Optional[Any]	=     (1, 11, 768)
				self.assertEqual(output.shape       ,      lowercase							)
				A_	:						Union[str, Any]	=     np.array([[[-0.16_39, 0.32_99, 0.16_48], [-0.17_46, 0.32_89, 0.17_10], [-0.18_84, 0.33_57, 0.18_10]]]							)

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