






"""simple docstring"""







import unittest

from transformers import BigBirdConfig, 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

		from transformers.models.big_bird.modeling_flax_big_bird import (
		    FlaxBigBirdForCausalLM,
		    FlaxBigBirdForMaskedLM,
		    FlaxBigBirdForMultipleChoice,
		    FlaxBigBirdForPreTraining,
		    FlaxBigBirdForQuestionAnswering,
		    FlaxBigBirdForSequenceClassification,
		    FlaxBigBirdForTokenClassification,
		    FlaxBigBirdModel,
		)







class  lowerCamelCase__ (	unittest.TestCase			):






				def __init__(			self     ,A     ,A=2     ,A=56     ,A=True     ,A=True     ,A=True     ,A=True     ,A=99     ,A=32     ,A=2     ,A=2     ,A=7     ,A="gelu_new"     ,A=0.1     ,A=0.1     ,A=512     ,A=16     ,A=2     ,A=0.02     ,A=4     ,A="block_sparse"     ,A=True     ,A=False     ,A=2     ,A=3     ,):
										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

										UpperCAmelCase          =	rescale_embeddings
										UpperCAmelCase          =	attention_type
										UpperCAmelCase          =	use_bias
										UpperCAmelCase          =	block_size
										UpperCAmelCase          =	num_random_blocks






				def     _UpperCamelCase							(			self ):
										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          =	BigBirdConfig(
										    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     ,attention_type=self.attention_type     ,block_size=self.block_size     ,num_random_blocks=self.num_random_blocks     ,use_bias=self.use_bias     ,rescale_embeddings=self.rescale_embeddings     ,)

										return config, input_ids, token_type_ids, attention_mask






				def     _UpperCamelCase							(			self ):
										UpperCAmelCase          =	self.prepare_config_and_inputs()
										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  lowerCamelCase__ (	a__   ,					unittest.TestCase			):
				SCREAMING_SNAKE_CASE		=      (
				    (
				        FlaxBigBirdForCausalLM,
				        FlaxBigBirdModel,
				        FlaxBigBirdForPreTraining,
				        FlaxBigBirdForMaskedLM,
				        FlaxBigBirdForMultipleChoice,
				        FlaxBigBirdForQuestionAnswering,
				        FlaxBigBirdForSequenceClassification,
				        FlaxBigBirdForTokenClassification,
				    )
				    if is_flax_available()
				    else ()
				)

				SCREAMING_SNAKE_CASE		=      False
				SCREAMING_SNAKE_CASE		=      False






				def     _UpperCamelCase							(			self ):
										UpperCAmelCase          =	FlaxBigBirdModelTester(self )






				@slow
				# copied from `test_modeling_flax_common` because it takes much longer than other models
				def     _UpperCamelCase							(			self ):
										super().test_from_pretrained_save_pretrained()






				@slow
				# copied from `test_modeling_flax_common` because it takes much longer than other models
				def     _UpperCamelCase							(			self ):
										super().test_from_pretrained_with_no_automatic_init()






				@slow
				# copied from `test_modeling_flax_common` because it takes much longer than other models
				def     _UpperCamelCase							(			self ):
										super().test_no_automatic_init()






				@slow
				# copied from `test_modeling_flax_common` because it takes much longer than other models
				def     _UpperCamelCase							(			self ):
										super().test_hidden_states_output()






				@slow
				def     _UpperCamelCase							(			self ):
										for model_class_name in self.all_model_classes:
																UpperCAmelCase          =	model_class_name.from_pretrained("""google/bigbird-roberta-base""" )
																self.assertIsNotNone(_lowerCamelCase )






				def     _UpperCamelCase							(			self ):
										if self.test_attn_probs:
																super().test_attention_outputs()






				@slow
				# copied from `test_modeling_flax_common` because it takes much longer than other models
				def     _UpperCamelCase							(			self ):
										UpperCAmelCase          =	self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
																with self.subTest(model_class.__name__ ):
																						UpperCAmelCase          =	self._prepare_for_class(_lowerCamelCase     ,_lowerCamelCase )
																						UpperCAmelCase          =	model_class(_lowerCamelCase )

																						@jax.jit
																						def model_jitted(A     ,A=None     ,**A ):
																												return model(input_ids=_lowerCamelCase     ,attention_mask=_lowerCamelCase     ,**_lowerCamelCase )

																						with self.subTest("""JIT Enabled""" ):
																												UpperCAmelCase          =	model_jitted(**_lowerCamelCase ).to_tuple()

																						with self.subTest("""JIT Disabled""" ):
																												with jax.disable_jit():
																																		UpperCAmelCase          =	model_jitted(**_lowerCamelCase ).to_tuple()

																						self.assertEqual(len(_lowerCamelCase )     ,len(_lowerCamelCase ) )
																						for jitted_output, output in zip(_lowerCamelCase     ,_lowerCamelCase ):
																												self.assertEqual(jitted_output.shape     ,output.shape )






				def     _UpperCamelCase							(			self     ,A     ,A     ,A     ,A=1e-5     ,A="outputs"     ,A=None ):
										# `bigbird_block_sparse_attention` in `FlaxBigBird` returns `attention_probs = None`, while in PyTorch version,
										# an effort was done to return `attention_probs` (yet to be verified).
										if name.startswith("""outputs.attentions""" ):
																return
										else:
																super().check_pt_flax_outputs(_lowerCamelCase     ,_lowerCamelCase     ,_lowerCamelCase     ,_lowerCamelCase     ,_lowerCamelCase     ,_lowerCamelCase )











"""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  lowerCamelCase__ :






      @staticmethod
      def     _UpperCamelCase							(			*A     ,**A ):
            pass

def _a			(		_snake_case							):







      """simple docstring"""






      UpperCAmelCase          =	hashlib.mda(image.tobytes()							)
      return m.hexdigest()[:10]

def _a			(		_snake_case							):







      """simple docstring"""






      UpperCAmelCase          =	np.array(_snake_case							)
      UpperCAmelCase          =	npimg.shape
      return {"hash": hashimage(_snake_case							), "shape": shape}







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






    def     _UpperCamelCase							(			self     ,A     ,A     ,A ):
          UpperCAmelCase          =	MaskGenerationPipeline(model=A     ,image_processor=A )
          return image_segmenter, [
              "./tests/fixtures/tests_samples/COCO/000000039769.png",
              "./tests/fixtures/tests_samples/COCO/000000039769.png",
          ]






    def     _UpperCamelCase							(			self     ,A     ,A ):
          pass






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






    @slow
    @require_torch
    def     _UpperCamelCase							(			self ):
          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(A ), "scores": outputs["scores"][i]}]

          # fmt: off
          self.assertEqual(
              nested_simplify(A     ,decimals=4 )     ,[
                  {"""mask""": {"""hash""": """115ad19f5f""", """shape""": (480, 640)}, """scores""": 1.0444},
                  {"""mask""": {"""hash""": """6affa964c6""", """shape""": (480, 640)}, """scores""": 1.021},
                  {"""mask""": {"""hash""": """dfe28a0388""", """shape""": (480, 640)}, """scores""": 1.0167},
                  {"""mask""": {"""hash""": """c0a5f4a318""", """shape""": (480, 640)}, """scores""": 1.0132},
                  {"""mask""": {"""hash""": """fe8065c197""", """shape""": (480, 640)}, """scores""": 1.0053},
                  {"""mask""": {"""hash""": """e2d0b7a0b7""", """shape""": (480, 640)}, """scores""": 0.9967},
                  {"""mask""": {"""hash""": """453c7844bd""", """shape""": (480, 640)}, """scores""": 0.993},
                  {"""mask""": {"""hash""": """3d44f2926d""", """shape""": (480, 640)}, """scores""": 0.9909},
                  {"""mask""": {"""hash""": """64033ddc3f""", """shape""": (480, 640)}, """scores""": 0.9879},
                  {"""mask""": {"""hash""": """801064ff79""", """shape""": (480, 640)}, """scores""": 0.9834},
                  {"""mask""": {"""hash""": """6172f276ef""", """shape""": (480, 640)}, """scores""": 0.9716},
                  {"""mask""": {"""hash""": """b49e60e084""", """shape""": (480, 640)}, """scores""": 0.9612},
                  {"""mask""": {"""hash""": """a811e775fd""", """shape""": (480, 640)}, """scores""": 0.9599},
                  {"""mask""": {"""hash""": """a6a8ebcf4b""", """shape""": (480, 640)}, """scores""": 0.9552},
                  {"""mask""": {"""hash""": """9d8257e080""", """shape""": (480, 640)}, """scores""": 0.9532},
                  {"""mask""": {"""hash""": """32de6454a8""", """shape""": (480, 640)}, """scores""": 0.9516},
                  {"""mask""": {"""hash""": """af3d4af2c8""", """shape""": (480, 640)}, """scores""": 0.9499},
                  {"""mask""": {"""hash""": """3c6db475fb""", """shape""": (480, 640)}, """scores""": 0.9483},
                  {"""mask""": {"""hash""": """c290813fb9""", """shape""": (480, 640)}, """scores""": 0.9464},
                  {"""mask""": {"""hash""": """b6f0b8f606""", """shape""": (480, 640)}, """scores""": 0.943},
                  {"""mask""": {"""hash""": """92ce16bfdf""", """shape""": (480, 640)}, """scores""": 0.943},
                  {"""mask""": {"""hash""": """c749b25868""", """shape""": (480, 640)}, """scores""": 0.9408},
                  {"""mask""": {"""hash""": """efb6cab859""", """shape""": (480, 640)}, """scores""": 0.9335},
                  {"""mask""": {"""hash""": """1ff2eafb30""", """shape""": (480, 640)}, """scores""": 0.9326},
                  {"""mask""": {"""hash""": """788b798e24""", """shape""": (480, 640)}, """scores""": 0.9262},
                  {"""mask""": {"""hash""": """abea804f0e""", """shape""": (480, 640)}, """scores""": 0.8999},
                  {"""mask""": {"""hash""": """7b9e8ddb73""", """shape""": (480, 640)}, """scores""": 0.8986},
                  {"""mask""": {"""hash""": """cd24047c8a""", """shape""": (480, 640)}, """scores""": 0.8984},
                  {"""mask""": {"""hash""": """6943e6bcbd""", """shape""": (480, 640)}, """scores""": 0.8873},
                  {"""mask""": {"""hash""": """b5f47c9191""", """shape""": (480, 640)}, """scores""": 0.8871}
              ]     ,)
          # fmt: on






    @require_torch
    @slow
    def     _UpperCamelCase							(			self ):
          UpperCAmelCase          =	"""facebook/sam-vit-huge"""
          UpperCAmelCase          =	pipeline("""mask-generation"""     ,model=A )

          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(A ), "scores": outputs["scores"][i]}]

          self.assertEqual(
              nested_simplify(A     ,decimals=4 )     ,[
                  {"""mask""": {"""hash""": """115ad19f5f""", """shape""": (480, 640)}, """scores""": 1.0444},
                  {"""mask""": {"""hash""": """6affa964c6""", """shape""": (480, 640)}, """scores""": 1.0210},
                  {"""mask""": {"""hash""": """dfe28a0388""", """shape""": (480, 640)}, """scores""": 1.0167},
                  {"""mask""": {"""hash""": """c0a5f4a318""", """shape""": (480, 640)}, """scores""": 1.0132},
                  {"""mask""": {"""hash""": """fe8065c197""", """shape""": (480, 640)}, """scores""": 1.0053},
              ]     ,)



