

'''simple docstring'''





import argparse
import json
import os
from collections import OrderedDict

import numpy as np
import tensorflow as tf
import torch


def 					_UpperCAmelCase       (  _SCREAMING_SNAKE_CASE					:		int       ):





    """simple docstring"""


    SCREAMING_SNAKE_CASE_									=						os.path.join(args.tf_model_dir ,   'parameters.json'       )
    SCREAMING_SNAKE_CASE_									=						json.loads(open(snake_case_       ).read()       )
    if not params:
        raise ValueError(
            f"""It seems that the json file at {parameter_file} is empty. Make sure you have a correct json file."""       )
    if not args.output.endswith('.pt'       ):
        SCREAMING_SNAKE_CASE_									=						args.output + """.pt"""
    SCREAMING_SNAKE_CASE_									=						OrderedDict()
    with tf.device('/CPU:0'       ):
        SCREAMING_SNAKE_CASE_									=						tf.train.load_checkpoint(args.tf_model_dir       )
        SCREAMING_SNAKE_CASE_									=						reader.get_variable_to_shape_map()
        for key_name in shapes.keys():
            SCREAMING_SNAKE_CASE_									=						reader.get_tensor(snake_case_       ).astype(np.floataa       )
            if key_name.endswith('/adam_m'       ) or key_name.endswith('/adam_v'       ):
                continue
            if key_name.startswith('pasts/'       ):
                if key_name.startswith('pasts/mlp'       ):
                    SCREAMING_SNAKE_CASE_									=						int(key_name[9]       )
                elif key_name.startswith('pasts/out'       ):
                    SCREAMING_SNAKE_CASE_									=						8
                SCREAMING_SNAKE_CASE_									=						"""model.sqout.%d.weight""" % (player * 2)  # enter to nn.Sequencial with Tanh, so 2 at a time
                SCREAMING_SNAKE_CASE_									=						vnp.transpose([1, 0]       ).copy()  # Mesh-Tensorflow is a diagonal matrix
                SCREAMING_SNAKE_CASE_									=						torch.tensor(snake_case_       )
            elif key_name.startswith('model/moe'       ):
                SCREAMING_SNAKE_CASE_									=						int(key_name[9:].split('/'       )[0]       )
                if key_name.endswith('/switch_gating/kernel'       ):
                    SCREAMING_SNAKE_CASE_									=						"""model.blocks.%d.feed_forward.mlp.router.classifier.weight""" % player
                    SCREAMING_SNAKE_CASE_									=						vnp.transpose([1, 0]       ).copy()  # Mesh-Tensorflow is a diagonal matrix
                    SCREAMING_SNAKE_CASE_									=						torch.tensor(snake_case_       )
                elif key_name.endswith('/softmlp/kernel'       ):
                    SCREAMING_SNAKE_CASE_									=						"""model.blocks.%d.feed_forward.soft_bypass_mlp.weight""" % player
                    SCREAMING_SNAKE_CASE_									=						vnp.transpose([1, 0]       ).copy()  # Mesh-Tensorflow is a diagonal matrix
                    SCREAMING_SNAKE_CASE_									=						torch.tensor(snake_case_       )
                elif key_name.endswith('/wo/kernel'       ) or key_name.endswith('/wi/kernel'       ):
                    SCREAMING_SNAKE_CASE_									=						key_name[-9:-7]
                    for i in range(16       ):
                        SCREAMING_SNAKE_CASE_									=						"""model.blocks.%d.feed_forward.mlp.experts.expert_%d.%s.weight""" % (player, i, nlayer)
                        SCREAMING_SNAKE_CASE_									=						(
                            vnp[i].transpose([1, 0]       ).copy()
                        )  # In Mesh-Tensorflow, it is one array, so it is divided
                        SCREAMING_SNAKE_CASE_									=						torch.tensor(snake_case_       )
            elif key_name.startswith('model/mlp'       ):
                SCREAMING_SNAKE_CASE_									=						int(key_name[9:].split('/'       )[0]       )
                if key_name.endswith('/p1/kernel'       ):
                    SCREAMING_SNAKE_CASE_									=						"""model.blocks.%d.feed_forward.mlp.wi.weight""" % player
                    SCREAMING_SNAKE_CASE_									=						vnp.transpose([1, 0]       ).copy()  # Mesh-Tensorflow is a diagonal matrix
                    SCREAMING_SNAKE_CASE_									=						torch.tensor(snake_case_       )
                elif key_name.endswith('/p1/bias'       ):
                    SCREAMING_SNAKE_CASE_									=						"""model.blocks.%d.feed_forward.mlp.wi.bias""" % player
                    SCREAMING_SNAKE_CASE_									=						vnp.copy()  # same because it is one dimensional
                    SCREAMING_SNAKE_CASE_									=						torch.tensor(snake_case_       )
                elif key_name.endswith('/p2/kernel'       ):
                    SCREAMING_SNAKE_CASE_									=						"""model.blocks.%d.feed_forward.mlp.wo.weight""" % player
                    SCREAMING_SNAKE_CASE_									=						vnp.transpose([1, 0]       ).copy()  # Mesh-Tensorflow is a diagonal matrix
                    SCREAMING_SNAKE_CASE_									=						torch.tensor(snake_case_       )
                elif key_name.endswith('/p2/bias'       ):
                    SCREAMING_SNAKE_CASE_									=						"""model.blocks.%d.feed_forward.mlp.wo.bias""" % player
                    SCREAMING_SNAKE_CASE_									=						vnp.copy()  # same because it is one dimensional
                    SCREAMING_SNAKE_CASE_									=						torch.tensor(snake_case_       )
            elif key_name.startswith('model/ln'       ):
                SCREAMING_SNAKE_CASE_									=						int(key_name[8:].split('/'       )[0]       )
                if key_name.endswith('/b'       ):
                    SCREAMING_SNAKE_CASE_									=						"""model.blocks.%d.feed_forward.norm.bias""" % player
                    SCREAMING_SNAKE_CASE_									=						vnp.copy()  # same because it is one dimensional
                    SCREAMING_SNAKE_CASE_									=						torch.tensor(snake_case_       )
                elif key_name.endswith('/g'       ):
                    SCREAMING_SNAKE_CASE_									=						"""model.blocks.%d.feed_forward.norm.weight""" % player
                    SCREAMING_SNAKE_CASE_									=						vnp.copy()  # same because it is one dimensional
                    SCREAMING_SNAKE_CASE_									=						torch.tensor(snake_case_       )
            elif key_name.startswith('model/att'       ):
                SCREAMING_SNAKE_CASE_									=						int(key_name[9:].split('/'       )[0]       )
                if key_name.endswith('/qkv/kernel'       ):
                    SCREAMING_SNAKE_CASE_									=						vnp.copy()  # Compute same dimension as Mesh-tensorflow using einsum
                    SCREAMING_SNAKE_CASE_									=						state[:, 0, :, :]
                    SCREAMING_SNAKE_CASE_									=						state[:, 1, :, :]
                    SCREAMING_SNAKE_CASE_									=						state[:, 2, :, :]
                    SCREAMING_SNAKE_CASE_									=						(
                        state_q.reshape([state_q.shape[0], state_q.shape[1] * state_q.shape[2]]       )
                        .transpose([1, 0]       )
                        .copy()
                    )  # Mesh-Tensorflow is a diagonal matrix
                    SCREAMING_SNAKE_CASE_									=						(
                        state_k.reshape([state_k.shape[0], state_k.shape[1] * state_k.shape[2]]       )
                        .transpose([1, 0]       )
                        .copy()
                    )  # Mesh-Tensorflow is a diagonal matrix
                    SCREAMING_SNAKE_CASE_									=						(
                        state_v.reshape([state_v.shape[0], state_v.shape[1] * state_v.shape[2]]       )
                        .transpose([1, 0]       )
                        .copy()
                    )  # Mesh-Tensorflow is a diagonal matrix
                    SCREAMING_SNAKE_CASE_									=						"""model.blocks.%d.self_attn.self_attn.q_proj.weight""" % player
                    SCREAMING_SNAKE_CASE_									=						torch.tensor(snake_case_       )
                    SCREAMING_SNAKE_CASE_									=						"""model.blocks.%d.self_attn.self_attn.k_proj.weight""" % player
                    SCREAMING_SNAKE_CASE_									=						torch.tensor(snake_case_       )
                    SCREAMING_SNAKE_CASE_									=						"""model.blocks.%d.self_attn.self_attn.v_proj.weight""" % player
                    SCREAMING_SNAKE_CASE_									=						torch.tensor(snake_case_       )
                elif key_name.endswith('/o/kernel'       ):
                    SCREAMING_SNAKE_CASE_									=						"""model.blocks.%d.self_attn.self_attn.out_proj.weight""" % player
                    SCREAMING_SNAKE_CASE_									=						(
                        vnp.reshape([vnp.shape[0] * vnp.shape[1], vnp.shape[2]]       ).transpose([1, 0]       ).copy()
                    )  # Mesh-Tensorflow is a diagonal matrix
                    SCREAMING_SNAKE_CASE_									=						torch.tensor(snake_case_       )
            elif key_name.startswith('model/an'       ):
                SCREAMING_SNAKE_CASE_									=						int(key_name[8:].split('/'       )[0]       )
                if key_name.endswith('/b'       ):
                    SCREAMING_SNAKE_CASE_									=						"""model.blocks.%d.self_attn.norm.bias""" % player
                    SCREAMING_SNAKE_CASE_									=						vnp.copy()  # same because it is one dimensional
                    SCREAMING_SNAKE_CASE_									=						torch.tensor(snake_case_       )
                elif key_name.endswith('/g'       ):
                    SCREAMING_SNAKE_CASE_									=						"""model.blocks.%d.self_attn.norm.weight""" % player
                    SCREAMING_SNAKE_CASE_									=						vnp.copy()  # same because it is one dimensional
                    SCREAMING_SNAKE_CASE_									=						torch.tensor(snake_case_       )
            elif (
                key_name.startswith('model/wte'       )
                or key_name.startswith('model/wpe'       )
                or key_name.startswith('model/ete'       )
            ):
                SCREAMING_SNAKE_CASE_									=						{"""wte""": """embed_tokens""", """wpe""": """position_embeddings""", """ete""": """extra_position_embeddings"""}[
                    key_name[-3:]
                ]
                SCREAMING_SNAKE_CASE_									=						"""model.%s.weight""" % nlayer
                SCREAMING_SNAKE_CASE_									=						vnp.copy()  # same in embedded
                SCREAMING_SNAKE_CASE_									=						torch.tensor(snake_case_       )
                if key_name.startswith('model/wte'       ):
                    SCREAMING_SNAKE_CASE_									=						"""lm_head.weight"""
                    SCREAMING_SNAKE_CASE_									=						vnp.copy()  # same in embedded
                    SCREAMING_SNAKE_CASE_									=						torch.tensor(snake_case_       )
            elif key_name.startswith('model/wob'       ):
                SCREAMING_SNAKE_CASE_									=						"""final_logits_bias"""
                SCREAMING_SNAKE_CASE_									=						vnp.copy()  # same in embedded
                SCREAMING_SNAKE_CASE_									=						state.reshape((1, -1)       )
                SCREAMING_SNAKE_CASE_									=						torch.tensor(snake_case_       )
            elif key_name == "model/dense/kernel":
                SCREAMING_SNAKE_CASE_									=						"""model.last_project.weight"""
                SCREAMING_SNAKE_CASE_									=						vnp.transpose([1, 0]       ).copy()  # Mesh-Tensorflow is a diagonal matrix
                SCREAMING_SNAKE_CASE_									=						torch.tensor(snake_case_       )
            elif key_name == "model/dense_1/bias":
                SCREAMING_SNAKE_CASE_									=						"""model.last_project.bias"""
                SCREAMING_SNAKE_CASE_									=						vnp.copy()  # same because it is one dimensional
                SCREAMING_SNAKE_CASE_									=						torch.tensor(snake_case_       )
    torch.save(snake_case_ ,   args.output       )


if __name__ == "__main__":
       UpperCamelCase__  :      Union[str, Any]			=    argparse.ArgumentParser(
           description="model converter.", formatter_class=argparse.ArgumentDefaultsHelpFormatter
       )
       parser.add_argument("--tf_model_dir", metavar="PATH", type=str, required=True, help="import model")
       parser.add_argument("--output", metavar="PATH", type=str, required=True, help="output model")
       UpperCamelCase__  :      Dict			=    parser.parse_args()
       convert_tf_gptsan_to_pt(args)





import unittest

import numpy as np
from datasets import load_dataset

from transformers.testing_utils import require_torch, require_vision
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 BeitImageProcessor



class       __snake_case  (    unittest.TestCase       ):

  def __init__(					self						,		_A						,		_A=7						,		_A=3						,		_A=18						,		_A=30						,		_A=400						,		_A=True						,		_A=None						,		_A=True						,		_A=None						,		_A=True						,		_A=[0.5, 0.5, 0.5]						,		_A=[0.5, 0.5, 0.5]						,		_A=False						,		):
      SCREAMING_SNAKE_CASE_									=						size if size is not None else {'height': 20, 'width': 20}
      SCREAMING_SNAKE_CASE_									=						crop_size if crop_size is not None else {'height': 18, 'width': 18}
      SCREAMING_SNAKE_CASE_									=						parent
      SCREAMING_SNAKE_CASE_									=						batch_size
      SCREAMING_SNAKE_CASE_									=						num_channels
      SCREAMING_SNAKE_CASE_									=						image_size
      SCREAMING_SNAKE_CASE_									=						min_resolution
      SCREAMING_SNAKE_CASE_									=						max_resolution
      SCREAMING_SNAKE_CASE_									=						do_resize
      SCREAMING_SNAKE_CASE_									=						size
      SCREAMING_SNAKE_CASE_									=						do_center_crop
      SCREAMING_SNAKE_CASE_									=						crop_size
      SCREAMING_SNAKE_CASE_									=						do_normalize
      SCREAMING_SNAKE_CASE_									=						image_mean
      SCREAMING_SNAKE_CASE_									=						image_std
      SCREAMING_SNAKE_CASE_									=						do_reduce_labels


  def 						lowerCAmelCase__     (					self):
      return {
          "do_resize": self.do_resize,
          "size": self.size,
          "do_center_crop": self.do_center_crop,
          "crop_size": self.crop_size,
          "do_normalize": self.do_normalize,
          "image_mean": self.image_mean,
          "image_std": self.image_std,
          "do_reduce_labels": self.do_reduce_labels,
      }






def 					_UpperCAmelCase       (  ):





    """simple docstring"""


    SCREAMING_SNAKE_CASE_									=						load_dataset('hf-internal-testing/fixtures_ade20k' ,   split='test'       )

    SCREAMING_SNAKE_CASE_									=						Image.open(dataset[0]['file']       )
    SCREAMING_SNAKE_CASE_									=						Image.open(dataset[1]['file']       )

    return image, map


def 					_UpperCAmelCase       (  ):





    """simple docstring"""


    SCREAMING_SNAKE_CASE_									=						load_dataset('hf-internal-testing/fixtures_ade20k' ,   split='test'       )

    SCREAMING_SNAKE_CASE_									=						Image.open(ds[0]['file']       )
    SCREAMING_SNAKE_CASE_									=						Image.open(ds[1]['file']       )
    SCREAMING_SNAKE_CASE_									=						Image.open(ds[2]['file']       )
    SCREAMING_SNAKE_CASE_									=						Image.open(ds[3]['file']       )

    return [imagea, imagea], [mapa, mapa]





@require_torch
@require_vision
class       __snake_case  (    lowerCAmelCase__     , unittest.TestCase       ):
  __lowerCAmelCase     :	Union[str, Any]						 =  BeitImageProcessor if is_vision_available() else None

  def 						lowerCAmelCase__     (					self):
      SCREAMING_SNAKE_CASE_									=						BeitImageProcessingTester(self)

  @property
  def 						lowerCAmelCase__     (					self):
      return self.image_processor_tester.prepare_image_processor_dict()

  def 						lowerCAmelCase__     (					self):
      SCREAMING_SNAKE_CASE_									=						self.image_processing_class(**self.image_processor_dict)
      self.assertTrue(hasattr(_A						,		'do_resize'))
      self.assertTrue(hasattr(_A						,		'size'))
      self.assertTrue(hasattr(_A						,		'do_center_crop'))
      self.assertTrue(hasattr(_A						,		'center_crop'))
      self.assertTrue(hasattr(_A						,		'do_normalize'))
      self.assertTrue(hasattr(_A						,		'image_mean'))
      self.assertTrue(hasattr(_A						,		'image_std'))

  def 						lowerCAmelCase__     (					self):
      SCREAMING_SNAKE_CASE_									=						self.image_processing_class.from_dict(self.image_processor_dict)
      self.assertEqual(image_processor.size						,		{'height': 20, 'width': 20})
      self.assertEqual(image_processor.crop_size						,		{'height': 18, 'width': 18})
      self.assertEqual(image_processor.do_reduce_labels						,		_A)

      SCREAMING_SNAKE_CASE_									=						self.image_processing_class.from_dict(
          self.image_processor_dict						,		size=42						,		crop_size=84						,		reduce_labels=_A)
      self.assertEqual(image_processor.size						,		{'height': 42, 'width': 42})
      self.assertEqual(image_processor.crop_size						,		{'height': 84, 'width': 84})
      self.assertEqual(image_processor.do_reduce_labels						,		_A)

  def 						lowerCAmelCase__     (					self):
      pass

  def 						lowerCAmelCase__     (					self):
      # Initialize image_processing
      SCREAMING_SNAKE_CASE_									=						self.image_processing_class(**self.image_processor_dict)
      # create random PIL images
      SCREAMING_SNAKE_CASE_									=						prepare_image_inputs(self.image_processor_tester						,		equal_resolution=_A)
      for image in image_inputs:
          self.assertIsInstance(_A						,		Image.Image)

      # Test not batched input
      SCREAMING_SNAKE_CASE_									=						image_processing(image_inputs[0]						,		return_tensors='pt').pixel_values
      self.assertEqual(
          encoded_images.shape						,		(
              1,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size['height'],
              self.image_processor_tester.crop_size['width'],
          )						,		)

      # Test batched
      SCREAMING_SNAKE_CASE_									=						image_processing(_A						,		return_tensors='pt').pixel_values
      self.assertEqual(
          encoded_images.shape						,		(
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size['height'],
              self.image_processor_tester.crop_size['width'],
          )						,		)

  def 						lowerCAmelCase__     (					self):
      # Initialize image_processing
      SCREAMING_SNAKE_CASE_									=						self.image_processing_class(**self.image_processor_dict)
      # create random numpy tensors
      SCREAMING_SNAKE_CASE_									=						prepare_image_inputs(self.image_processor_tester						,		equal_resolution=_A						,		numpify=_A)
      for image in image_inputs:
          self.assertIsInstance(_A						,		np.ndarray)

      # Test not batched input
      SCREAMING_SNAKE_CASE_									=						image_processing(image_inputs[0]						,		return_tensors='pt').pixel_values
      self.assertEqual(
          encoded_images.shape						,		(
              1,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size['height'],
              self.image_processor_tester.crop_size['width'],
          )						,		)

      # Test batched
      SCREAMING_SNAKE_CASE_									=						image_processing(_A						,		return_tensors='pt').pixel_values
      self.assertEqual(
          encoded_images.shape						,		(
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size['height'],
              self.image_processor_tester.crop_size['width'],
          )						,		)

  def 						lowerCAmelCase__     (					self):
      # Initialize image_processing
      SCREAMING_SNAKE_CASE_									=						self.image_processing_class(**self.image_processor_dict)
      # create random PyTorch tensors
      SCREAMING_SNAKE_CASE_									=						prepare_image_inputs(self.image_processor_tester						,		equal_resolution=_A						,		torchify=_A)
      for image in image_inputs:
          self.assertIsInstance(_A						,		torch.Tensor)

      # Test not batched input
      SCREAMING_SNAKE_CASE_									=						image_processing(image_inputs[0]						,		return_tensors='pt').pixel_values
      self.assertEqual(
          encoded_images.shape						,		(
              1,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size['height'],
              self.image_processor_tester.crop_size['width'],
          )						,		)

      # Test batched
      SCREAMING_SNAKE_CASE_									=						image_processing(_A						,		return_tensors='pt').pixel_values
      self.assertEqual(
          encoded_images.shape						,		(
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size['height'],
              self.image_processor_tester.crop_size['width'],
          )						,		)

  def 						lowerCAmelCase__     (					self):
      # Initialize image_processing
      SCREAMING_SNAKE_CASE_									=						self.image_processing_class(**self.image_processor_dict)
      # create random PyTorch tensors
      SCREAMING_SNAKE_CASE_									=						prepare_image_inputs(self.image_processor_tester						,		equal_resolution=_A						,		torchify=_A)
      SCREAMING_SNAKE_CASE_									=						[]
      for image in image_inputs:
          self.assertIsInstance(_A						,		torch.Tensor)
          maps.append(torch.zeros(image.shape[-2:]).long())

      # Test not batched input
      SCREAMING_SNAKE_CASE_									=						image_processing(image_inputs[0]						,		maps[0]						,		return_tensors='pt')
      self.assertEqual(
          encoding['pixel_values'].shape						,		(
              1,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size['height'],
              self.image_processor_tester.crop_size['width'],
          )						,		)
      self.assertEqual(
          encoding['labels'].shape						,		(
              1,
              self.image_processor_tester.crop_size['height'],
              self.image_processor_tester.crop_size['width'],
          )						,		)
      self.assertEqual(encoding['labels'].dtype						,		torch.long)
      self.assertTrue(encoding['labels'].min().item() >= 0)
      self.assertTrue(encoding['labels'].max().item() <= 255)

      # Test batched
      SCREAMING_SNAKE_CASE_									=						image_processing(_A						,		_A						,		return_tensors='pt')
      self.assertEqual(
          encoding['pixel_values'].shape						,		(
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size['height'],
              self.image_processor_tester.crop_size['width'],
          )						,		)
      self.assertEqual(
          encoding['labels'].shape						,		(
              self.image_processor_tester.batch_size,
              self.image_processor_tester.crop_size['height'],
              self.image_processor_tester.crop_size['width'],
          )						,		)
      self.assertEqual(encoding['labels'].dtype						,		torch.long)
      self.assertTrue(encoding['labels'].min().item() >= 0)
      self.assertTrue(encoding['labels'].max().item() <= 255)

      # Test not batched input (PIL images)
      SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_									=						prepare_semantic_single_inputs()

      SCREAMING_SNAKE_CASE_									=						image_processing(_A						,		_A						,		return_tensors='pt')
      self.assertEqual(
          encoding['pixel_values'].shape						,		(
              1,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size['height'],
              self.image_processor_tester.crop_size['width'],
          )						,		)
      self.assertEqual(
          encoding['labels'].shape						,		(
              1,
              self.image_processor_tester.crop_size['height'],
              self.image_processor_tester.crop_size['width'],
          )						,		)
      self.assertEqual(encoding['labels'].dtype						,		torch.long)
      self.assertTrue(encoding['labels'].min().item() >= 0)
      self.assertTrue(encoding['labels'].max().item() <= 255)

      # Test batched input (PIL images)
      SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_									=						prepare_semantic_batch_inputs()

      SCREAMING_SNAKE_CASE_									=						image_processing(_A						,		_A						,		return_tensors='pt')
      self.assertEqual(
          encoding['pixel_values'].shape						,		(
              2,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size['height'],
              self.image_processor_tester.crop_size['width'],
          )						,		)
      self.assertEqual(
          encoding['labels'].shape						,		(
              2,
              self.image_processor_tester.crop_size['height'],
              self.image_processor_tester.crop_size['width'],
          )						,		)
      self.assertEqual(encoding['labels'].dtype						,		torch.long)
      self.assertTrue(encoding['labels'].min().item() >= 0)
      self.assertTrue(encoding['labels'].max().item() <= 255)


  def 						lowerCAmelCase__     (					self):
      # Initialize image_processing
      SCREAMING_SNAKE_CASE_									=						self.image_processing_class(**self.image_processor_dict)

      # ADE20k has 150 classes, and the background is included, so labels should be between 0 and 150
      SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_									=						prepare_semantic_single_inputs()
      SCREAMING_SNAKE_CASE_									=						image_processing(_A						,		_A						,		return_tensors='pt')
      self.assertTrue(encoding['labels'].min().item() >= 0)
      self.assertTrue(encoding['labels'].max().item() <= 150)

      SCREAMING_SNAKE_CASE_									=						True
      SCREAMING_SNAKE_CASE_									=						image_processing(_A						,		_A						,		return_tensors='pt')
      self.assertTrue(encoding['labels'].min().item() >= 0)
      self.assertTrue(encoding['labels'].max().item() <= 255)


