


'''simple docstring'''

import importlib
import math
import os
from dataclasses import dataclass
from enum import Enum
from typing import Any, Dict, Optional, Tuple, Union

import flax
import jax.numpy as jnp

from ..utils import BaseOutput


a_						:       Tuple      			=				"""scheduler_config.json"""





class   snake_case					(					__lowercase    ):





		"""simple docstring"""




		_lowerCamelCase						=						1
		_lowerCamelCase						=						2
		_lowerCamelCase						=						3
		_lowerCamelCase						=						4
		_lowerCamelCase						=						5





@dataclass
class   snake_case					(					__lowercase    ):





		"""simple docstring"""




		_lowerCamelCase						=						42





class   snake_case					:





		"""simple docstring"""




		_lowerCamelCase						=						SCHEDULER_CONFIG_NAME
		_lowerCamelCase						=						['dtype']
		_lowerCamelCase						=						[]
		_lowerCamelCase						=						True






		@classmethod
		def 						snake_case	(					cls    ,						UpperCamelCase = None    ,						UpperCamelCase = None    ,						UpperCamelCase=False    ,						**UpperCamelCase    ,						):







			"""simple docstring"""
			lowerCamelCase_				,lowerCamelCase_  				=						cls.load_config(
			    pretrained_model_name_or_path=__A    ,						subfolder=__A    ,						return_unused_kwargs=__A    ,						**__A    ,						)
			lowerCamelCase_				,lowerCamelCase_  				=						cls.from_config(__A    ,						return_unused_kwargs=__A    ,						**__A  )

			if hasattr(__A    ,						"create_state"  ) and getattr(__A    ,						"has_state"    ,						__A  ):
				lowerCamelCase_  				=						scheduler.create_state()

			if return_unused_kwargs:
				return scheduler, state, unused_kwargs

			return scheduler, state






		def 						snake_case	(					self    ,						UpperCamelCase    ,						UpperCamelCase = False    ,						**UpperCamelCase  ):







			"""simple docstring"""
			self.save_config(save_directory=__A    ,						push_to_hub=__A    ,						**__A  )






		@property
		def 						snake_case	(					self  ):







			"""simple docstring"""
			return self._get_compatibles()







		@classmethod
		def 						snake_case	(					cls  ):







			"""simple docstring"""
			lowerCamelCase_  				=						list(set([cls.__name__] + cls._compatibles  )  )
			lowerCamelCase_  				=						importlib.import_module(__name__.split("."  )[0]  )
			lowerCamelCase_  				=						[
			    getattr(__A    ,						__A  ) for c in compatible_classes_str if hasattr(__A    ,						__A  )
			]
			return compatible_classes





def __snake_case      (   UpperCAmelCase_		:		List[Any]      ,       UpperCAmelCase_		:		Any	):
	assert len(a__	) >= x.ndim
	return jnp.broadcast_to(x.reshape(x.shape + (1,) * (len(a__	) - x.ndim)	)      ,       a__	)





def __snake_case      (   UpperCAmelCase_		:		str      ,       UpperCAmelCase_		:		Union[str, Any]=0.999      ,       UpperCAmelCase_		:		List[Any]=jnp.floataa	):






	def alpha_bar(UpperCAmelCase_		:		int	):
		return math.cos((time_step + 0.008) / 1.008 * math.pi / 2	) ** 2

	lowerCamelCase_  				=						[]
	for i in range(a__	):
		lowerCamelCase_  				=						i / num_diffusion_timesteps
		lowerCamelCase_  				=						(i + 1) / num_diffusion_timesteps
		betas.append(min(1 - alpha_bar(a__	) / alpha_bar(a__	)      ,       a__	)	)
	return jnp.array(a__      ,       dtype=a__	)





@flax.struct.dataclass
class   snake_case					:





		"""simple docstring"""




		_lowerCamelCase						=						42
		_lowerCamelCase						=						42
		_lowerCamelCase						=						42






		@classmethod
		def 						snake_case	(					cls    ,						UpperCamelCase  ):







			"""simple docstring"""
			lowerCamelCase_  				=						scheduler.config

			if config.trained_betas is not None:
				lowerCamelCase_  				=						jnp.asarray(config.trained_betas    ,						dtype=scheduler.dtype  )
			elif config.beta_schedule == "linear":
				lowerCamelCase_  				=						jnp.linspace(config.beta_start    ,						config.beta_end    ,						config.num_train_timesteps    ,						dtype=scheduler.dtype  )
			elif config.beta_schedule == "scaled_linear":
				# this schedule is very specific to the latent diffusion model.
				lowerCamelCase_  				=						(
				    jnp.linspace(
				        config.beta_start**0.5    ,						config.beta_end**0.5    ,						config.num_train_timesteps    ,						dtype=scheduler.dtype  )
				    ** 2
				)
			elif config.beta_schedule == "squaredcos_cap_v2":
				# Glide cosine schedule
				lowerCamelCase_  				=						betas_for_alpha_bar(config.num_train_timesteps    ,						dtype=scheduler.dtype  )
			else:
				raise NotImplementedError(
				    f'''beta_schedule {config.beta_schedule} is not implemented for scheduler {scheduler.__class__.__name__}'''  )

			lowerCamelCase_  				=						1.0 - betas

			lowerCamelCase_  				=						jnp.cumprod(__A    ,						axis=0  )

			return cls(
			    alphas=__A    ,						betas=__A    ,						alphas_cumprod=__A    ,						)





def __snake_case      (   UpperCAmelCase_		:		int      ,       UpperCAmelCase_		:		Any      ,       UpperCAmelCase_		:		List[str]      ,       UpperCAmelCase_		:		Any	):
	lowerCamelCase_  				=						state.alphas_cumprod

	lowerCamelCase_  				=						alphas_cumprod[timesteps] ** 0.5
	lowerCamelCase_  				=						sqrt_alpha_prod.flatten()
	lowerCamelCase_  				=						broadcast_to_shape_from_left(a__      ,       original_samples.shape	)

	lowerCamelCase_  				=						(1 - alphas_cumprod[timesteps]) ** 0.5
	lowerCamelCase_  				=						sqrt_one_minus_alpha_prod.flatten()
	lowerCamelCase_  				=						broadcast_to_shape_from_left(a__      ,       original_samples.shape	)

	return sqrt_alpha_prod, sqrt_one_minus_alpha_prod





def __snake_case      (   UpperCAmelCase_		:		List[Any]      ,       UpperCAmelCase_		:		int      ,       UpperCAmelCase_		:		List[Any]      ,       UpperCAmelCase_		:		Union[str, Any]	):
	lowerCamelCase_				,lowerCamelCase_  				=						get_sqrt_alpha_prod(a__      ,       a__      ,       a__      ,       a__	)
	lowerCamelCase_  				=						sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
	return noisy_samples







def __snake_case      (   UpperCAmelCase_		:		Any      ,       UpperCAmelCase_		:		Dict      ,       UpperCAmelCase_		:		str      ,       UpperCAmelCase_		:		str	):
	lowerCamelCase_				,lowerCamelCase_  				=						get_sqrt_alpha_prod(a__      ,       a__      ,       a__      ,       a__	)
	lowerCamelCase_  				=						sqrt_alpha_prod * noise - sqrt_one_minus_alpha_prod * sample
	return velocity







'''simple docstring'''

import json
import pathlib
import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
     import torch

if is_vision_available():
     from PIL import Image

     from transformers import DetrImageProcessor





class   snake_case					(					unittest.TestCase    ):





  """simple docstring"""




  def __init__(					self    ,						UpperCamelCase    ,						UpperCamelCase=7    ,						UpperCamelCase=3    ,						UpperCamelCase=30    ,						UpperCamelCase=400    ,						UpperCamelCase=True    ,						UpperCamelCase=None    ,						UpperCamelCase=True    ,						UpperCamelCase=1 / 255    ,						UpperCamelCase=True    ,						UpperCamelCase=[0.5, 0.5, 0.5]    ,						UpperCamelCase=[0.5, 0.5, 0.5]    ,						UpperCamelCase=True    ,						):







   """simple docstring"""
   # by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
   lowerCamelCase_  				=						size if size is not None else {"shortest_edge": 18, "longest_edge": 1333}
   lowerCamelCase_  				=						parent
   lowerCamelCase_  				=						batch_size
   lowerCamelCase_  				=						num_channels
   lowerCamelCase_  				=						min_resolution
   lowerCamelCase_  				=						max_resolution
   lowerCamelCase_  				=						do_resize
   lowerCamelCase_  				=						size
   lowerCamelCase_  				=						do_rescale
   lowerCamelCase_  				=						rescale_factor
   lowerCamelCase_  				=						do_normalize
   lowerCamelCase_  				=						image_mean
   lowerCamelCase_  				=						image_std
   lowerCamelCase_  				=						do_pad






  def 						snake_case	(					self  ):







   """simple docstring"""
   return {
       "do_resize": self.do_resize,
       "size": self.size,
       "do_rescale": self.do_rescale,
       "rescale_factor": self.rescale_factor,
       "do_normalize": self.do_normalize,
       "image_mean": self.image_mean,
       "image_std": self.image_std,
       "do_pad": self.do_pad,
   }







  def 						snake_case	(					self    ,						UpperCamelCase    ,						UpperCamelCase=False  ):







   """simple docstring"""
   if not batched:
    lowerCamelCase_  				=						image_inputs[0]
    if isinstance(UpperCamelCase    ,						Image.Image  ):
     lowerCamelCase_				,lowerCamelCase_  				=						image.size
    else:
     lowerCamelCase_				,lowerCamelCase_  				=						image.shape[1], image.shape[2]
    if w < h:
     lowerCamelCase_  				=						int(self.size["shortest_edge"] * h / w  )
     lowerCamelCase_  				=						self.size["shortest_edge"]
    elif w > h:
     lowerCamelCase_  				=						self.size["shortest_edge"]
     lowerCamelCase_  				=						int(self.size["shortest_edge"] * w / h  )
    else:
     lowerCamelCase_  				=						self.size["shortest_edge"]
     lowerCamelCase_  				=						self.size["shortest_edge"]

   else:
    lowerCamelCase_  				=						[]
    for image in image_inputs:
     lowerCamelCase_				,lowerCamelCase_  				=						self.get_expected_values([image]  )
     expected_values.append((expected_height, expected_width)  )
    lowerCamelCase_  				=						max(UpperCamelCase    ,						key=lambda UpperCamelCase  : item[0]  )[0]
    lowerCamelCase_  				=						max(UpperCamelCase    ,						key=lambda UpperCamelCase  : item[1]  )[1]

   return expected_height, expected_width





@require_torch
@require_vision
class   snake_case					(					lowercase    ,		unittest.TestCase    ):





  """simple docstring"""




  _lowerCamelCase						=						DetrImageProcessor if is_vision_available() else None






  def 						snake_case	(					self  ):







   """simple docstring"""
   lowerCamelCase_  				=						DetrImageProcessingTester(self  )






  @property
  def 						snake_case	(					self  ):







   """simple docstring"""
   return self.image_processor_tester.prepare_image_processor_dict()






  def 						snake_case	(					self  ):







   """simple docstring"""
   lowerCamelCase_  				=						self.image_processing_class(**self.image_processor_dict  )
   self.assertTrue(hasattr(UpperCamelCase    ,						"image_mean"  )  )
   self.assertTrue(hasattr(UpperCamelCase    ,						"image_std"  )  )
   self.assertTrue(hasattr(UpperCamelCase    ,						"do_normalize"  )  )
   self.assertTrue(hasattr(UpperCamelCase    ,						"do_rescale"  )  )
   self.assertTrue(hasattr(UpperCamelCase    ,						"rescale_factor"  )  )
   self.assertTrue(hasattr(UpperCamelCase    ,						"do_resize"  )  )
   self.assertTrue(hasattr(UpperCamelCase    ,						"size"  )  )
   self.assertTrue(hasattr(UpperCamelCase    ,						"do_pad"  )  )






  def 						snake_case	(					self  ):







   """simple docstring"""
   lowerCamelCase_  				=						self.image_processing_class.from_dict(self.image_processor_dict  )
   self.assertEqual(image_processor.size    ,						{"shortest_edge": 18, "longest_edge": 1333}  )
   self.assertEqual(image_processor.do_pad    ,						UpperCamelCase  )

   lowerCamelCase_  				=						self.image_processing_class.from_dict(
       self.image_processor_dict    ,						size=42    ,						max_size=84    ,						pad_and_return_pixel_mask=UpperCamelCase  )
   self.assertEqual(image_processor.size    ,						{"shortest_edge": 42, "longest_edge": 84}  )
   self.assertEqual(image_processor.do_pad    ,						UpperCamelCase  )






  def 						snake_case	(					self  ):







   """simple docstring"""
   pass






  def 						snake_case	(					self  ):







   """simple docstring"""
   # Initialize image_processing
   lowerCamelCase_  				=						self.image_processing_class(**self.image_processor_dict  )
   # create random PIL images
   lowerCamelCase_  				=						prepare_image_inputs(self.image_processor_tester    ,						equal_resolution=UpperCamelCase  )
   for image in image_inputs:
    self.assertIsInstance(UpperCamelCase    ,						Image.Image  )

   # Test not batched input
   lowerCamelCase_  				=						image_processing(image_inputs[0]    ,						return_tensors="pt"  ).pixel_values

   lowerCamelCase_				,lowerCamelCase_  				=						self.image_processor_tester.get_expected_values(UpperCamelCase  )

   self.assertEqual(
       encoded_images.shape    ,						(1, self.image_processor_tester.num_channels, expected_height, expected_width)    ,						)

   # Test batched
   lowerCamelCase_				,lowerCamelCase_  				=						self.image_processor_tester.get_expected_values(UpperCamelCase    ,						batched=UpperCamelCase  )

   lowerCamelCase_  				=						image_processing(UpperCamelCase    ,						return_tensors="pt"  ).pixel_values
   self.assertEqual(
       encoded_images.shape    ,						(
           self.image_processor_tester.batch_size,
           self.image_processor_tester.num_channels,
           expected_height,
           expected_width,
       )    ,						)






  def 						snake_case	(					self  ):







   """simple docstring"""
   # Initialize image_processing
   lowerCamelCase_  				=						self.image_processing_class(**self.image_processor_dict  )
   # create random numpy tensors
   lowerCamelCase_  				=						prepare_image_inputs(self.image_processor_tester    ,						equal_resolution=UpperCamelCase    ,						numpify=UpperCamelCase  )
   for image in image_inputs:
    self.assertIsInstance(UpperCamelCase    ,						np.ndarray  )

   # Test not batched input
   lowerCamelCase_  				=						image_processing(image_inputs[0]    ,						return_tensors="pt"  ).pixel_values

   lowerCamelCase_				,lowerCamelCase_  				=						self.image_processor_tester.get_expected_values(UpperCamelCase  )

   self.assertEqual(
       encoded_images.shape    ,						(1, self.image_processor_tester.num_channels, expected_height, expected_width)    ,						)

   # Test batched
   lowerCamelCase_  				=						image_processing(UpperCamelCase    ,						return_tensors="pt"  ).pixel_values

   lowerCamelCase_				,lowerCamelCase_  				=						self.image_processor_tester.get_expected_values(UpperCamelCase    ,						batched=UpperCamelCase  )

   self.assertEqual(
       encoded_images.shape    ,						(
           self.image_processor_tester.batch_size,
           self.image_processor_tester.num_channels,
           expected_height,
           expected_width,
       )    ,						)






  def 						snake_case	(					self  ):







   """simple docstring"""
   # Initialize image_processing
   lowerCamelCase_  				=						self.image_processing_class(**self.image_processor_dict  )
   # create random PyTorch tensors
   lowerCamelCase_  				=						prepare_image_inputs(self.image_processor_tester    ,						equal_resolution=UpperCamelCase    ,						torchify=UpperCamelCase  )
   for image in image_inputs:
    self.assertIsInstance(UpperCamelCase    ,						torch.Tensor  )

   # Test not batched input
   lowerCamelCase_  				=						image_processing(image_inputs[0]    ,						return_tensors="pt"  ).pixel_values

   lowerCamelCase_				,lowerCamelCase_  				=						self.image_processor_tester.get_expected_values(UpperCamelCase  )

   self.assertEqual(
       encoded_images.shape    ,						(1, self.image_processor_tester.num_channels, expected_height, expected_width)    ,						)

   # Test batched
   lowerCamelCase_  				=						image_processing(UpperCamelCase    ,						return_tensors="pt"  ).pixel_values

   lowerCamelCase_				,lowerCamelCase_  				=						self.image_processor_tester.get_expected_values(UpperCamelCase    ,						batched=UpperCamelCase  )

   self.assertEqual(
       encoded_images.shape    ,						(
           self.image_processor_tester.batch_size,
           self.image_processor_tester.num_channels,
           expected_height,
           expected_width,
       )    ,						)






  @slow
  def 						snake_case	(					self  ):







   """simple docstring"""
   # prepare image and target
   lowerCamelCase_  				=						Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"  )
   with open("./tests/fixtures/tests_samples/COCO/coco_annotations.txt"    ,						"r"  ) as f:
    lowerCamelCase_  				=						json.loads(f.read()  )

   lowerCamelCase_  				=						{"image_id": 3_9769, "annotations": target}

   # encode them
   lowerCamelCase_  				=						DetrImageProcessor.from_pretrained("facebook/detr-resnet-50"  )
   lowerCamelCase_  				=						image_processing(images=UpperCamelCase    ,						annotations=UpperCamelCase    ,						return_tensors="pt"  )

   # verify pixel values
   lowerCamelCase_  				=						torch.Size([1, 3, 800, 1066]  )
   self.assertEqual(encoding["pixel_values"].shape    ,						UpperCamelCase  )

   lowerCamelCase_  				=						torch.tensor([0.2_796, 0.3_138, 0.3_481]  )
   self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3]    ,						UpperCamelCase    ,						atol=1e-4  )  )

   # verify area
   lowerCamelCase_  				=						torch.tensor([5_887.9_600, 11_250.2_061, 489_353.8_438, 837_122.7_500, 147_967.5_156, 165_732.3_438]  )
   self.assertTrue(torch.allclose(encoding["labels"][0]["area"]    ,						UpperCamelCase  )  )
   # verify boxes
   lowerCamelCase_  				=						torch.Size([6, 4]  )
   self.assertEqual(encoding["labels"][0]["boxes"].shape    ,						UpperCamelCase  )
   lowerCamelCase_  				=						torch.tensor([0.5_503, 0.2_765, 0.0_604, 0.2_215]  )
   self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0]    ,						UpperCamelCase    ,						atol=1e-3  )  )
   # verify image_id
   lowerCamelCase_  				=						torch.tensor([3_9769]  )
   self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]    ,						UpperCamelCase  )  )
   # verify is_crowd
   lowerCamelCase_  				=						torch.tensor([0, 0, 0, 0, 0, 0]  )
   self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]    ,						UpperCamelCase  )  )
   # verify class_labels
   lowerCamelCase_  				=						torch.tensor([75, 75, 63, 65, 17, 17]  )
   self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]    ,						UpperCamelCase  )  )
   # verify orig_size
   lowerCamelCase_  				=						torch.tensor([480, 640]  )
   self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]    ,						UpperCamelCase  )  )
   # verify size
   lowerCamelCase_  				=						torch.tensor([800, 1066]  )
   self.assertTrue(torch.allclose(encoding["labels"][0]["size"]    ,						UpperCamelCase  )  )







  @slow
  def 						snake_case	(					self  ):







   """simple docstring"""
   # prepare image, target and masks_path
   lowerCamelCase_  				=						Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"  )
   with open("./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt"    ,						"r"  ) as f:
    lowerCamelCase_  				=						json.loads(f.read()  )

   lowerCamelCase_  				=						{"file_name": "000000039769.png", "image_id": 3_9769, "segments_info": target}

   lowerCamelCase_  				=						pathlib.Path("./tests/fixtures/tests_samples/COCO/coco_panoptic"  )

   # encode them
   lowerCamelCase_  				=						DetrImageProcessor.from_pretrained("facebook/detr-resnet-50-panoptic"  )
   lowerCamelCase_  				=						image_processing(images=UpperCamelCase    ,						annotations=UpperCamelCase    ,						masks_path=UpperCamelCase    ,						return_tensors="pt"  )

   # verify pixel values
   lowerCamelCase_  				=						torch.Size([1, 3, 800, 1066]  )
   self.assertEqual(encoding["pixel_values"].shape    ,						UpperCamelCase  )

   lowerCamelCase_  				=						torch.tensor([0.2_796, 0.3_138, 0.3_481]  )
   self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3]    ,						UpperCamelCase    ,						atol=1e-4  )  )

   # verify area
   lowerCamelCase_  				=						torch.tensor([147_979.6_875, 165_527.0_469, 484_638.5_938, 11_292.9_375, 5_879.6_562, 7_634.1_147]  )
   self.assertTrue(torch.allclose(encoding["labels"][0]["area"]    ,						UpperCamelCase  )  )
   # verify boxes
   lowerCamelCase_  				=						torch.Size([6, 4]  )
   self.assertEqual(encoding["labels"][0]["boxes"].shape    ,						UpperCamelCase  )
   lowerCamelCase_  				=						torch.tensor([0.2_625, 0.5_437, 0.4_688, 0.8_625]  )
   self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0]    ,						UpperCamelCase    ,						atol=1e-3  )  )
   # verify image_id
   lowerCamelCase_  				=						torch.tensor([3_9769]  )
   self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]    ,						UpperCamelCase  )  )
   # verify is_crowd
   lowerCamelCase_  				=						torch.tensor([0, 0, 0, 0, 0, 0]  )
   self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]    ,						UpperCamelCase  )  )
   # verify class_labels
   lowerCamelCase_  				=						torch.tensor([17, 17, 63, 75, 75, 93]  )
   self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]    ,						UpperCamelCase  )  )
   # verify masks
   lowerCamelCase_  				=						82_2873
   self.assertEqual(encoding["labels"][0]["masks"].sum().item()    ,						UpperCamelCase  )
   # verify orig_size
   lowerCamelCase_  				=						torch.tensor([480, 640]  )
   self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]    ,						UpperCamelCase  )  )
   # verify size
   lowerCamelCase_  				=						torch.tensor([800, 1066]  )
   self.assertTrue(torch.allclose(encoding["labels"][0]["size"]    ,						UpperCamelCase  )  )



