
from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCamelCase_  :	Dict 				=					logging.get_logger(__name__)

lowerCamelCase_  :	List[str] 				=					{
    """google/switch-base-8""": """https://huggingface.co/google/switch-base-8/blob/main/config.json""",
}

class     _UpperCamelCase			(   _A					):






			'''simple docstring'''

			__UpperCamelCase :  Optional[Any]    =	"""switch_transformers"""
			__UpperCamelCase :  List[Any]    =	["""past_key_values"""]
			__UpperCamelCase :  List[Any]    =	{"""hidden_size""": """d_model""", """num_attention_heads""": """num_heads""", """num_hidden_layers""": """num_layers"""}

			def __init__(    self	:    Union[str, Any]  ,		snake_case_	:    List[str]=3_2128  ,		snake_case_	:    int=768  ,		snake_case_	:    Dict=64  ,		snake_case_	:    Optional[Any]=2048  ,		snake_case_	:    Union[str, Any]=64  ,		snake_case_	:    List[Any]=12  ,		snake_case_	:    Optional[int]=3  ,		snake_case_	:    Tuple=12  ,		snake_case_	:    List[Any]=3  ,		snake_case_	:    List[str]=12  ,		snake_case_	:    Dict=8  ,		snake_case_	:    Union[str, Any]=False  ,		snake_case_	:    str=0.01  ,		snake_case_	:    str="float32"  ,		snake_case_	:    List[str]=False  ,		snake_case_	:    Optional[Any]=32  ,		snake_case_	:    Any=128  ,		snake_case_	:    Optional[int]=0.1  ,		snake_case_	:    Any=1e-6  ,		snake_case_	:    str=0.001  ,		snake_case_	:    Any=0.001  ,		snake_case_	:    Tuple=1.0  ,		snake_case_	:    Optional[Any]="relu"  ,		snake_case_	:    Tuple=True  ,		snake_case_	:    int=False  ,		snake_case_	:    Union[str, Any]=True  ,		snake_case_	:    Dict=0  ,		snake_case_	:    int=1  ,		**snake_case_	:    Tuple  ,		):
							UpperCamelCase_:							Tuple		=  vocab_size
							UpperCamelCase_:							List[Any]		=  d_model
							UpperCamelCase_:							List[Any]		=  d_kv
							UpperCamelCase_:							str		=  d_ff

							UpperCamelCase_:							Dict		=  num_sparse_encoder_layers

							UpperCamelCase_:							Optional[Any]		=  num_layers
							UpperCamelCase_:							str		=  (
							    num_decoder_layers if num_decoder_layers is not None else self.num_layers
							)  # default = symmetry
							UpperCamelCase_:							Tuple		=  num_sparse_decoder_layers

							# This tells us, each how many encoder layer we'll have to set a sparse layer.
							if self.num_sparse_encoder_layers > 0:
											UpperCamelCase_:							Tuple		=  self.num_layers // self.num_sparse_encoder_layers
							else:
											UpperCamelCase_:							str		=  self.num_layers  # HACK: this will create 0 sparse layers

							# This tells us, each how many encoder layer we'll have to set a sparse layer.
							if self.num_sparse_decoder_layers > 0:
											UpperCamelCase_:							Optional[int]		=  self.num_decoder_layers // self.num_sparse_decoder_layers
							else:
											UpperCamelCase_:							Optional[Any]		=  self.num_decoder_layers  # HACK: this will create 0 sparse layers

							UpperCamelCase_:							List[Any]		=  num_heads
							UpperCamelCase_:							List[Any]		=  num_experts
							UpperCamelCase_:							int		=  expert_capacity
							UpperCamelCase_:							List[str]		=  router_bias
							UpperCamelCase_:							Optional[int]		=  router_jitter_noise
							if router_dtype not in ["float32", "float16", "bfloat16"]:
											raise ValueError(f'''`router_dtype` must be one of \'float32\', \'float16\' or \'bfloat16\', got {router_dtype}''' )
							UpperCamelCase_:							int		=  router_dtype

							UpperCamelCase_:							List[Any]		=  router_ignore_padding_tokens
							UpperCamelCase_:							List[Any]		=  relative_attention_num_buckets
							UpperCamelCase_:							Tuple		=  relative_attention_max_distance

							UpperCamelCase_:							int		=  dropout_rate
							UpperCamelCase_:							str		=  layer_norm_epsilon
							UpperCamelCase_:							Union[str, Any]		=  initializer_factor
							UpperCamelCase_:							Tuple		=  feed_forward_proj
							UpperCamelCase_:							Tuple		=  use_cache
							UpperCamelCase_:							Union[str, Any]		=  add_router_probs

							UpperCamelCase_:							Any		=  router_z_loss_coef
							UpperCamelCase_:							Dict		=  router_aux_loss_coef

							UpperCamelCase_:							Dict		=  self.feed_forward_proj.split("""-""" )
							UpperCamelCase_:							List[str]		=  act_info[-1]
							UpperCamelCase_:							Union[str, Any]		=  act_info[0] == """gated"""

							if len(snake_case_ ) > 1 and act_info[0] != "gated" or len(snake_case_ ) > 2:
											raise ValueError(
											    f'''`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer.'''
											    """Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. """
											    """'gated-gelu' or 'relu'""" )

							# for backwards compatibility
							if feed_forward_proj == "gated-gelu":
											UpperCamelCase_:							Dict		=  """gelu_new"""

							super().__init__(
							    pad_token_id=snake_case_  ,		eos_token_id=snake_case_  ,		is_encoder_decoder=snake_case_  ,		**snake_case_  ,		)








import json
import os
import tempfile
import unittest

import numpy as np
from datasets import load_dataset

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


if is_torch_available():
							import torch

if is_vision_available():
							from PIL import Image

							from transformers import ImageGPTImageProcessor

class     _UpperCamelCase			(   unittest.TestCase					):






			'''simple docstring'''

			def __init__(    self	:    Optional[int]  ,		snake_case_	:    Dict  ,		snake_case_	:    Tuple=7  ,		snake_case_	:    Optional[Any]=3  ,		snake_case_	:    Dict=18  ,		snake_case_	:    Dict=30  ,		snake_case_	:    Union[str, Any]=400  ,		snake_case_	:    List[Any]=True  ,		snake_case_	:    Any=None  ,		snake_case_	:    List[str]=True  ,		):
							UpperCamelCase_:							Dict		=  size if size is not None else {"""height""": 18, """width""": 18}
							UpperCamelCase_:							Union[str, Any]		=  parent
							UpperCamelCase_:							Tuple		=  batch_size
							UpperCamelCase_:							List[str]		=  num_channels
							UpperCamelCase_:							Optional[int]		=  image_size
							UpperCamelCase_:							Dict		=  min_resolution
							UpperCamelCase_:							Optional[int]		=  max_resolution
							UpperCamelCase_:							str		=  do_resize
							UpperCamelCase_:							Tuple		=  size
							UpperCamelCase_:							Dict		=  do_normalize




			def  lowerCAmelCase__		(    self	:    str ):
							return {
							    # here we create 2 clusters for the sake of simplicity
							    "clusters": np.asarray(
							        [
							            [0.8866_4436_3403_3203, 0.6618_8293_6954_4983, 0.3891_7464_0178_6804],
							            [-0.6042_5591_4688_1104, -0.0_2295_0088_6052_8469, 0.5423_7973_6900_3296],
							        ] ),
							    "do_resize": self.do_resize,
							    "size": self.size,
							    "do_normalize": self.do_normalize,
							}




@require_torch
@require_vision
class     _UpperCamelCase			(   _A       ,    unittest.TestCase					):






			'''simple docstring'''

			__UpperCamelCase :  Optional[int]    =	ImageGPTImageProcessor if is_vision_available() else None

			def  lowerCAmelCase__		(    self	:    List[str] ):
							UpperCamelCase_:							Any		=  ImageGPTImageProcessingTester(self )

			@property
			def  lowerCAmelCase__		(    self	:    Optional[int] ):
							return self.image_processor_tester.prepare_image_processor_dict()

			def  lowerCAmelCase__		(    self	:    Optional[int] ):
							UpperCamelCase_:							List[Any]		=  self.image_processing_class(**self.image_processor_dict )
							self.assertTrue(hasattr(snake_case_  ,		"""clusters""" ) )
							self.assertTrue(hasattr(snake_case_  ,		"""do_resize""" ) )
							self.assertTrue(hasattr(snake_case_  ,		"""size""" ) )
							self.assertTrue(hasattr(snake_case_  ,		"""do_normalize""" ) )

			def  lowerCAmelCase__		(    self	:    int ):
							UpperCamelCase_:							List[Any]		=  self.image_processing_class.from_dict(self.image_processor_dict )
							self.assertEqual(image_processor.size  ,		{"""height""": 18, """width""": 18} )

							UpperCamelCase_:							Tuple		=  self.image_processing_class.from_dict(self.image_processor_dict  ,		size=42 )
							self.assertEqual(image_processor.size  ,		{"""height""": 42, """width""": 42} )

			def  lowerCAmelCase__		(    self	:    Optional[Any] ):
							UpperCamelCase_:							int		=  self.image_processing_class(**self.image_processor_dict )
							UpperCamelCase_:							Optional[int]		=  json.loads(image_processor.to_json_string() )
							for key, value in self.image_processor_dict.items():
											if key == "clusters":
															self.assertTrue(np.array_equal(snake_case_  ,		obj[key] ) )
											else:
															self.assertEqual(obj[key]  ,		snake_case_ )

			def  lowerCAmelCase__		(    self	:    List[Any] ):
							UpperCamelCase_:							Dict		=  self.image_processing_class(**self.image_processor_dict )

							with tempfile.TemporaryDirectory() as tmpdirname:
											UpperCamelCase_:							int		=  os.path.join(snake_case_  ,		"""image_processor.json""" )
											image_processor_first.to_json_file(snake_case_ )
											UpperCamelCase_:							Any		=  self.image_processing_class.from_json_file(snake_case_ ).to_dict()

							UpperCamelCase_:							str		=  image_processor_first.to_dict()
							for key, value in image_processor_first.items():
											if key == "clusters":
															self.assertTrue(np.array_equal(snake_case_  ,		image_processor_second[key] ) )
											else:
															self.assertEqual(image_processor_first[key]  ,		snake_case_ )

			def  lowerCAmelCase__		(    self	:    Union[str, Any] ):
							UpperCamelCase_:							Optional[Any]		=  self.image_processing_class(**self.image_processor_dict )

							with tempfile.TemporaryDirectory() as tmpdirname:
											image_processor_first.save_pretrained(snake_case_ )
											UpperCamelCase_:							Optional[int]		=  self.image_processing_class.from_pretrained(snake_case_ ).to_dict()

							UpperCamelCase_:							Union[str, Any]		=  image_processor_first.to_dict()
							for key, value in image_processor_first.items():
											if key == "clusters":
															self.assertTrue(np.array_equal(snake_case_  ,		image_processor_second[key] ) )
											else:
															self.assertEqual(image_processor_first[key]  ,		snake_case_ )




			@unittest.skip("""ImageGPT requires clusters at initialization""" )
			def  lowerCAmelCase__		(    self	:    List[Any] ):
							pass







def     A__   (  )					->		Optional[int]:
				UpperCamelCase_:							Optional[int]		=  load_dataset("""hf-internal-testing/fixtures_image_utils"""       ,      split="""test"""  )

				UpperCamelCase_:							Tuple		=  Image.open(dataset[4]["""file"""]  )
				UpperCamelCase_:							Union[str, Any]		=  Image.open(dataset[5]["""file"""]  )

				UpperCamelCase_:							List[str]		=  [imagea, imagea]

				return images





@require_vision
@require_torch
class     _UpperCamelCase			(   unittest.TestCase					):






			'''simple docstring'''

			@slow
			def  lowerCAmelCase__		(    self	:    List[str] ):
							UpperCamelCase_:							List[Any]		=  ImageGPTImageProcessor.from_pretrained("""openai/imagegpt-small""" )

							UpperCamelCase_:							List[str]		=  prepare_images()

							# test non-batched
							UpperCamelCase_:							List[str]		=  image_processing(images[0]  ,		return_tensors="""pt""" )

							self.assertIsInstance(encoding.input_ids  ,		torch.LongTensor )
							self.assertEqual(encoding.input_ids.shape  ,		(1, 1024) )

							UpperCamelCase_:							Union[str, Any]		=  [306, 191, 191]
							self.assertEqual(encoding.input_ids[0, :3].tolist()  ,		snake_case_ )

							# test batched
							UpperCamelCase_:							Optional[int]		=  image_processing(snake_case_  ,		return_tensors="""pt""" )

							self.assertIsInstance(encoding.input_ids  ,		torch.LongTensor )
							self.assertEqual(encoding.input_ids.shape  ,		(2, 1024) )

							UpperCamelCase_:							str		=  [303, 13, 13]
							self.assertEqual(encoding.input_ids[1, -3:].tolist()  ,		snake_case_ )






