
import unittest

import numpy as np

from transformers import is_flax_available
from transformers.testing_utils import require_flax

from ..test_modeling_flax_common import ids_tensor


if is_flax_available():
    import jax
    import jax.numpy as jnp

    from transformers.generation import (
        FlaxForcedBOSTokenLogitsProcessor,
        FlaxForcedEOSTokenLogitsProcessor,
        FlaxLogitsProcessorList,
        FlaxMinLengthLogitsProcessor,
        FlaxTemperatureLogitsWarper,
        FlaxTopKLogitsWarper,
        FlaxTopPLogitsWarper,
    )




@require_flax
class      __A(    unittest.TestCase			):

  """simple docstring"""







  def 	UpperCAmelCase_ (self	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_						):
       UpperCamelCase__				=    jnp.ones((batch_size, length)						) / length
       return scores







  def 	UpperCAmelCase_ (self						):
       UpperCamelCase__				=    None
       UpperCamelCase__				=    20

       UpperCamelCase__				=    self._get_uniform_logits(batch_size=2	,      length=lowerCamelCase_						)

       # tweak scores to not be uniform anymore
       UpperCamelCase__				=    scores.at[1, 5].set((1 / length) + 0.1						)  # peak, 1st batch
       UpperCamelCase__				=    scores.at[1, 10].set((1 / length) - 0.4						)  # valley, 1st batch

       # compute softmax
       UpperCamelCase__				=    jax.nn.softmax(lowerCamelCase_	,      axis=-1						)

       UpperCamelCase__				=    FlaxTemperatureLogitsWarper(temperature=0.5						)
       UpperCamelCase__				=    FlaxTemperatureLogitsWarper(temperature=1.3						)

       UpperCamelCase__				=    jax.nn.softmax(temp_dist_warper_sharper(lowerCamelCase_	,      scores.copy()	,      cur_len=lowerCamelCase_						)	,      axis=-1						)
       UpperCamelCase__				=    jax.nn.softmax(temp_dist_warper_smoother(lowerCamelCase_	,      scores.copy()	,      cur_len=lowerCamelCase_						)	,      axis=-1						)

       # uniform distribution stays uniform
       self.assertTrue(jnp.allclose(probs[0, :]	,      warped_prob_sharp[0, :]	,      atol=1E-3						)						)
       self.assertTrue(jnp.allclose(probs[0, :]	,      warped_prob_smooth[0, :]	,      atol=1E-3						)						)

       # sharp peaks get higher, valleys get lower
       self.assertLess(probs[1, :].max()	,      warped_prob_sharp[1, :].max()						)
       self.assertGreater(probs[1, :].min()	,      warped_prob_sharp[1, :].min()						)

       # smooth peaks get lower, valleys get higher
       self.assertGreater(probs[1, :].max()	,      warped_prob_smooth[1, :].max()						)
       self.assertLess(probs[1, :].min()	,      warped_prob_smooth[1, :].min()						)







  def 	UpperCAmelCase_ (self						):
       UpperCamelCase__				=    None
       UpperCamelCase__				=    10
       UpperCamelCase__				=    2

       # create ramp distribution
       UpperCamelCase__				=    np.broadcast_to(np.arange(lowerCamelCase_						)[None, :]	,      (batch_size, vocab_size)						).copy()
       UpperCamelCase__				=    ramp_logits[1:, : vocab_size // 2] + vocab_size

       UpperCamelCase__				=    FlaxTopKLogitsWarper(3						)

       UpperCamelCase__				=    top_k_warp(lowerCamelCase_	,      lowerCamelCase_	,      cur_len=lowerCamelCase_						)

       # check that correct tokens are filtered
       self.assertListEqual(jnp.isinf(scores[0]						).tolist()	,      7 * [True] + 3 * [False]						)
       self.assertListEqual(jnp.isinf(scores[1]						).tolist()	,      2 * [True] + 3 * [False] + 5 * [True]						)

       # check special case
       UpperCamelCase__				=    5
       UpperCamelCase__				=    FlaxTopKLogitsWarper(top_k=1	,      filter_value=0.0	,      min_tokens_to_keep=3						)

       UpperCamelCase__				=    np.broadcast_to(np.arange(lowerCamelCase_						)[None, :]	,      (batch_size, length)						).copy()
       UpperCamelCase__				=    top_k_warp_safety_check(lowerCamelCase_	,      lowerCamelCase_	,      cur_len=lowerCamelCase_						)

       # min_tokens overwrites k: 3 tokens are kept => 2 tokens are nullified
       self.assertListEqual((scores == 0.0).sum(axis=-1						).tolist()	,      [2, 2]						)







  def 	UpperCAmelCase_ (self						):
       UpperCamelCase__				=    None
       UpperCamelCase__				=    10
       UpperCamelCase__				=    2

       # create distribution and take log (inverse to Softmax as taken in TopPLogitsWarper)
       UpperCamelCase__				=    np.log(np.array([[0.3, 0.1, 0.1, 0.5], [0.15, 0.3, 0.3, 0.25]]						)						)

       UpperCamelCase__				=    FlaxTopPLogitsWarper(0.8						)
       UpperCamelCase__				=    np.exp(top_p_warp(lowerCamelCase_	,      lowerCamelCase_	,      cur_len=lowerCamelCase_						)						)

       # dist should be filtered to keep min num values so that sum is >= top_p
       # exp (-inf) => 0
       UpperCamelCase__				=    np.array([[0.3, 0.0, 0.0, 0.5], [0.0, 0.3, 0.3, 0.25]]						)
       self.assertTrue(np.allclose(lowerCamelCase_	,      lowerCamelCase_	,      atol=1E-3						)						)

       # check edge cases with negative and extreme logits
       UpperCamelCase__				=    np.broadcast_to(np.arange(lowerCamelCase_						)[None, :]	,      (batch_size, vocab_size)						).copy() - (
           vocab_size // 2
       )

       # make ramp_logits more extreme
       UpperCamelCase__				=    ramp_logits[1] * 1_00.0

       # make sure at least 2 tokens are kept
       UpperCamelCase__				=    FlaxTopPLogitsWarper(0.9	,      min_tokens_to_keep=2	,      filter_value=0.0						)
       UpperCamelCase__				=    top_p_warp(lowerCamelCase_	,      lowerCamelCase_	,      cur_len=lowerCamelCase_						)

       # first batch should keep three tokens, second batch would keep only 1, but due to `min_tokens_to_keep=2` keeps 2.
       self.assertListEqual((filtered_dist != 0.0).sum(axis=-1						).tolist()	,      [3, 2]						)







  def 	UpperCAmelCase_ (self						):
       UpperCamelCase__				=    20
       UpperCamelCase__				=    4
       UpperCamelCase__				=    0

       UpperCamelCase__				=    FlaxMinLengthLogitsProcessor(min_length=10	,      eos_token_id=lowerCamelCase_						)

       # check that min length is applied at length 5
       UpperCamelCase__				=    ids_tensor((batch_size, 20)	,      vocab_size=20						)
       UpperCamelCase__				=    5
       UpperCamelCase__				=    self._get_uniform_logits(lowerCamelCase_	,      lowerCamelCase_						)
       UpperCamelCase__				=    min_dist_processor(lowerCamelCase_	,      lowerCamelCase_	,      cur_len=lowerCamelCase_						)
       self.assertListEqual(scores_before_min_length[:, eos_token_id].tolist()	,      4 * [-float("""inf"""						)]						)

       # check that min length is not applied anymore at length 15
       UpperCamelCase__				=    self._get_uniform_logits(lowerCamelCase_	,      lowerCamelCase_						)
       UpperCamelCase__				=    15
       UpperCamelCase__				=    min_dist_processor(lowerCamelCase_	,      lowerCamelCase_	,      cur_len=lowerCamelCase_						)
       self.assertFalse(jnp.isinf(lowerCamelCase_						).any()						)







  def 	UpperCAmelCase_ (self						):
       UpperCamelCase__				=    20
       UpperCamelCase__				=    4
       UpperCamelCase__				=    0

       UpperCamelCase__				=    FlaxForcedBOSTokenLogitsProcessor(bos_token_id=lowerCamelCase_						)

       # check that all scores are -inf except the bos_token_id score
       UpperCamelCase__				=    ids_tensor((batch_size, 1)	,      vocab_size=20						)
       UpperCamelCase__				=    1
       UpperCamelCase__				=    self._get_uniform_logits(lowerCamelCase_	,      lowerCamelCase_						)
       UpperCamelCase__				=    logits_processor(lowerCamelCase_	,      lowerCamelCase_	,      cur_len=lowerCamelCase_						)
       self.assertTrue(jnp.isneginf(scores[:, bos_token_id + 1 :]						).all()						)
       self.assertListEqual(scores[:, bos_token_id].tolist()	,      4 * [0]						)  # score for bos_token_id shold be zero

       # check that bos_token_id is not forced if current length is greater than 1
       UpperCamelCase__				=    3
       UpperCamelCase__				=    self._get_uniform_logits(lowerCamelCase_	,      lowerCamelCase_						)
       UpperCamelCase__				=    logits_processor(lowerCamelCase_	,      lowerCamelCase_	,      cur_len=lowerCamelCase_						)
       self.assertFalse(jnp.isinf(lowerCamelCase_						).any()						)







  def 	UpperCAmelCase_ (self						):
       UpperCamelCase__				=    20
       UpperCamelCase__				=    4
       UpperCamelCase__				=    0
       UpperCamelCase__				=    5

       UpperCamelCase__				=    FlaxForcedEOSTokenLogitsProcessor(max_length=lowerCamelCase_	,      eos_token_id=lowerCamelCase_						)

       # check that all scores are -inf except the eos_token_id when max_length is reached
       UpperCamelCase__				=    ids_tensor((batch_size, 4)	,      vocab_size=20						)
       UpperCamelCase__				=    4
       UpperCamelCase__				=    self._get_uniform_logits(lowerCamelCase_	,      lowerCamelCase_						)
       UpperCamelCase__				=    logits_processor(lowerCamelCase_	,      lowerCamelCase_	,      cur_len=lowerCamelCase_						)
       self.assertTrue(jnp.isneginf(scores[:, eos_token_id + 1 :]						).all()						)
       self.assertListEqual(scores[:, eos_token_id].tolist()	,      4 * [0]						)  # score for eos_token_id should be zero

       # check that eos_token_id is not forced if max_length is not reached
       UpperCamelCase__				=    3
       UpperCamelCase__				=    self._get_uniform_logits(lowerCamelCase_	,      lowerCamelCase_						)
       UpperCamelCase__				=    logits_processor(lowerCamelCase_	,      lowerCamelCase_	,      cur_len=lowerCamelCase_						)
       self.assertFalse(jnp.isinf(lowerCamelCase_						).any()						)







  def 	UpperCAmelCase_ (self						):
       UpperCamelCase__				=    4
       UpperCamelCase__				=    10
       UpperCamelCase__				=    15
       UpperCamelCase__				=    2
       UpperCamelCase__				=    1
       UpperCamelCase__				=    15

       # dummy input_ids and scores
       UpperCamelCase__				=    ids_tensor((batch_size, sequence_length)	,      lowerCamelCase_						)
       UpperCamelCase__				=    input_ids.copy()

       UpperCamelCase__				=    self._get_uniform_logits(lowerCamelCase_	,      lowerCamelCase_						)
       UpperCamelCase__				=    scores.copy()

       # instantiate all dist processors
       UpperCamelCase__				=    FlaxTemperatureLogitsWarper(temperature=0.5						)
       UpperCamelCase__				=    FlaxTopKLogitsWarper(3						)
       UpperCamelCase__				=    FlaxTopPLogitsWarper(0.8						)

       # instantiate all logits processors
       UpperCamelCase__				=    FlaxMinLengthLogitsProcessor(min_length=10	,      eos_token_id=lowerCamelCase_						)
       UpperCamelCase__				=    FlaxForcedBOSTokenLogitsProcessor(bos_token_id=lowerCamelCase_						)
       UpperCamelCase__				=    FlaxForcedEOSTokenLogitsProcessor(max_length=lowerCamelCase_	,      eos_token_id=lowerCamelCase_						)

       UpperCamelCase__				=    10

       # no processor list
       UpperCamelCase__				=    temp_dist_warp(lowerCamelCase_	,      lowerCamelCase_	,      cur_len=lowerCamelCase_						)
       UpperCamelCase__				=    top_k_warp(lowerCamelCase_	,      lowerCamelCase_	,      cur_len=lowerCamelCase_						)
       UpperCamelCase__				=    top_p_warp(lowerCamelCase_	,      lowerCamelCase_	,      cur_len=lowerCamelCase_						)
       UpperCamelCase__				=    min_dist_proc(lowerCamelCase_	,      lowerCamelCase_	,      cur_len=lowerCamelCase_						)
       UpperCamelCase__				=    bos_dist_proc(lowerCamelCase_	,      lowerCamelCase_	,      cur_len=lowerCamelCase_						)
       UpperCamelCase__				=    eos_dist_proc(lowerCamelCase_	,      lowerCamelCase_	,      cur_len=lowerCamelCase_						)

       # with processor list
       UpperCamelCase__				=    FlaxLogitsProcessorList(
           [temp_dist_warp, top_k_warp, top_p_warp, min_dist_proc, bos_dist_proc, eos_dist_proc]						)
       UpperCamelCase__				=    processor(lowerCamelCase_	,      lowerCamelCase_	,      cur_len=lowerCamelCase_						)

       # scores should be equal
       self.assertTrue(jnp.allclose(lowerCamelCase_	,      lowerCamelCase_	,      atol=1E-3						)						)

       # input_ids should never be changed
       self.assertListEqual(input_ids.tolist()	,      input_ids_comp.tolist()						)







  def 	UpperCAmelCase_ (self						):
       UpperCamelCase__				=    4
       UpperCamelCase__				=    10
       UpperCamelCase__				=    15
       UpperCamelCase__				=    2
       UpperCamelCase__				=    1
       UpperCamelCase__				=    15

       # dummy input_ids and scores
       UpperCamelCase__				=    ids_tensor((batch_size, sequence_length)	,      lowerCamelCase_						)
       UpperCamelCase__				=    input_ids.copy()

       UpperCamelCase__				=    self._get_uniform_logits(lowerCamelCase_	,      lowerCamelCase_						)
       UpperCamelCase__				=    scores.copy()

       # instantiate all dist processors
       UpperCamelCase__				=    FlaxTemperatureLogitsWarper(temperature=0.5						)
       UpperCamelCase__				=    FlaxTopKLogitsWarper(3						)
       UpperCamelCase__				=    FlaxTopPLogitsWarper(0.8						)

       # instantiate all logits processors
       UpperCamelCase__				=    FlaxMinLengthLogitsProcessor(min_length=10	,      eos_token_id=lowerCamelCase_						)
       UpperCamelCase__				=    FlaxForcedBOSTokenLogitsProcessor(bos_token_id=lowerCamelCase_						)
       UpperCamelCase__				=    FlaxForcedEOSTokenLogitsProcessor(max_length=lowerCamelCase_	,      eos_token_id=lowerCamelCase_						)

       UpperCamelCase__				=    10

       # no processor list
       def run_no_processor_list(SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_						):
            UpperCamelCase__				=    temp_dist_warp(lowerCamelCase_	,      lowerCamelCase_	,      cur_len=lowerCamelCase_						)
            UpperCamelCase__				=    top_k_warp(lowerCamelCase_	,      lowerCamelCase_	,      cur_len=lowerCamelCase_						)
            UpperCamelCase__				=    top_p_warp(lowerCamelCase_	,      lowerCamelCase_	,      cur_len=lowerCamelCase_						)
            UpperCamelCase__				=    min_dist_proc(lowerCamelCase_	,      lowerCamelCase_	,      cur_len=lowerCamelCase_						)
            UpperCamelCase__				=    bos_dist_proc(lowerCamelCase_	,      lowerCamelCase_	,      cur_len=lowerCamelCase_						)
            UpperCamelCase__				=    eos_dist_proc(lowerCamelCase_	,      lowerCamelCase_	,      cur_len=lowerCamelCase_						)
            return scores

       # with processor list
       def run_processor_list(SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_						):
            UpperCamelCase__				=    FlaxLogitsProcessorList(
                [temp_dist_warp, top_k_warp, top_p_warp, min_dist_proc, bos_dist_proc, eos_dist_proc]						)
            UpperCamelCase__				=    processor(lowerCamelCase_	,      lowerCamelCase_	,      cur_len=lowerCamelCase_						)
            return scores

       UpperCamelCase__				=    jax.jit(lowerCamelCase_						)
       UpperCamelCase__				=    jax.jit(lowerCamelCase_						)

       UpperCamelCase__				=    jitted_run_no_processor_list(lowerCamelCase_	,      lowerCamelCase_	,      lowerCamelCase_						)
       UpperCamelCase__				=    jitted_run_processor_list(lowerCamelCase_	,      lowerCamelCase_	,      lowerCamelCase_						)

       # scores should be equal
       self.assertTrue(jnp.allclose(lowerCamelCase_	,      lowerCamelCase_	,      atol=1E-3						)						)

       # input_ids should never be changed
       self.assertListEqual(input_ids.tolist()	,      input_ids_comp.tolist()						)







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:

      """simple docstring"""







      @staticmethod
      def 	UpperCAmelCase_ (*SCREAMING_SNAKE_CASE_	,      **SCREAMING_SNAKE_CASE_						):
           pass






def 						__magic_name__			(   __a      :	Image						):



     '''simple docstring'''




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






def 						__magic_name__			(   __a      :	Image						):



     '''simple docstring'''




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




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

  """simple docstring"""
  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	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_						):
       UpperCamelCase__				=    MaskGenerationPipeline(model=SCREAMING_SNAKE_CASE_	,      image_processor=SCREAMING_SNAKE_CASE_						)
       return image_segmenter, [
           "./tests/fixtures/tests_samples/COCO/000000039769.png",
           "./tests/fixtures/tests_samples/COCO/000000039769.png",
       ]







  def 	UpperCAmelCase_ (self	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_						):
       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=2_56						)

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

       # fmt: off
       self.assertEqual(
           nested_simplify(SCREAMING_SNAKE_CASE_	,      decimals=4						)	,      [
               {"""mask""": {"""hash""": """115ad19f5f""", """shape""": (4_80, 6_40)}, """scores""": 1.0444},
               {"""mask""": {"""hash""": """6affa964c6""", """shape""": (4_80, 6_40)}, """scores""": 1.021},
               {"""mask""": {"""hash""": """dfe28a0388""", """shape""": (4_80, 6_40)}, """scores""": 1.0167},
               {"""mask""": {"""hash""": """c0a5f4a318""", """shape""": (4_80, 6_40)}, """scores""": 1.0132},
               {"""mask""": {"""hash""": """fe8065c197""", """shape""": (4_80, 6_40)}, """scores""": 1.0053},
               {"""mask""": {"""hash""": """e2d0b7a0b7""", """shape""": (4_80, 6_40)}, """scores""": 0.9967},
               {"""mask""": {"""hash""": """453c7844bd""", """shape""": (4_80, 6_40)}, """scores""": 0.993},
               {"""mask""": {"""hash""": """3d44f2926d""", """shape""": (4_80, 6_40)}, """scores""": 0.9909},
               {"""mask""": {"""hash""": """64033ddc3f""", """shape""": (4_80, 6_40)}, """scores""": 0.9879},
               {"""mask""": {"""hash""": """801064ff79""", """shape""": (4_80, 6_40)}, """scores""": 0.9834},
               {"""mask""": {"""hash""": """6172f276ef""", """shape""": (4_80, 6_40)}, """scores""": 0.9716},
               {"""mask""": {"""hash""": """b49e60e084""", """shape""": (4_80, 6_40)}, """scores""": 0.9612},
               {"""mask""": {"""hash""": """a811e775fd""", """shape""": (4_80, 6_40)}, """scores""": 0.9599},
               {"""mask""": {"""hash""": """a6a8ebcf4b""", """shape""": (4_80, 6_40)}, """scores""": 0.9552},
               {"""mask""": {"""hash""": """9d8257e080""", """shape""": (4_80, 6_40)}, """scores""": 0.9532},
               {"""mask""": {"""hash""": """32de6454a8""", """shape""": (4_80, 6_40)}, """scores""": 0.9516},
               {"""mask""": {"""hash""": """af3d4af2c8""", """shape""": (4_80, 6_40)}, """scores""": 0.9499},
               {"""mask""": {"""hash""": """3c6db475fb""", """shape""": (4_80, 6_40)}, """scores""": 0.9483},
               {"""mask""": {"""hash""": """c290813fb9""", """shape""": (4_80, 6_40)}, """scores""": 0.9464},
               {"""mask""": {"""hash""": """b6f0b8f606""", """shape""": (4_80, 6_40)}, """scores""": 0.943},
               {"""mask""": {"""hash""": """92ce16bfdf""", """shape""": (4_80, 6_40)}, """scores""": 0.943},
               {"""mask""": {"""hash""": """c749b25868""", """shape""": (4_80, 6_40)}, """scores""": 0.9408},
               {"""mask""": {"""hash""": """efb6cab859""", """shape""": (4_80, 6_40)}, """scores""": 0.9335},
               {"""mask""": {"""hash""": """1ff2eafb30""", """shape""": (4_80, 6_40)}, """scores""": 0.9326},
               {"""mask""": {"""hash""": """788b798e24""", """shape""": (4_80, 6_40)}, """scores""": 0.9262},
               {"""mask""": {"""hash""": """abea804f0e""", """shape""": (4_80, 6_40)}, """scores""": 0.8999},
               {"""mask""": {"""hash""": """7b9e8ddb73""", """shape""": (4_80, 6_40)}, """scores""": 0.8986},
               {"""mask""": {"""hash""": """cd24047c8a""", """shape""": (4_80, 6_40)}, """scores""": 0.8984},
               {"""mask""": {"""hash""": """6943e6bcbd""", """shape""": (4_80, 6_40)}, """scores""": 0.8873},
               {"""mask""": {"""hash""": """b5f47c9191""", """shape""": (4_80, 6_40)}, """scores""": 0.8871}
           ]	,      )
       # fmt: on







  @require_torch
  @slow
  def 	UpperCAmelCase_ (self						):
       UpperCamelCase__				=    """facebook/sam-vit-huge"""
       UpperCamelCase__				=    pipeline("""mask-generation"""	,      model=SCREAMING_SNAKE_CASE_						)

       UpperCamelCase__				=    image_segmenter(
           """http://images.cocodataset.org/val2017/000000039769.jpg"""	,      pred_iou_thresh=1	,      points_per_batch=2_56						)

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

       self.assertEqual(
           nested_simplify(SCREAMING_SNAKE_CASE_	,      decimals=4						)	,      [
               {"""mask""": {"""hash""": """115ad19f5f""", """shape""": (4_80, 6_40)}, """scores""": 1.0444},
               {"""mask""": {"""hash""": """6affa964c6""", """shape""": (4_80, 6_40)}, """scores""": 1.0210},
               {"""mask""": {"""hash""": """dfe28a0388""", """shape""": (4_80, 6_40)}, """scores""": 1.0167},
               {"""mask""": {"""hash""": """c0a5f4a318""", """shape""": (4_80, 6_40)}, """scores""": 1.0132},
               {"""mask""": {"""hash""": """fe8065c197""", """shape""": (4_80, 6_40)}, """scores""": 1.0053},
           ]	,      )





