




import argparse
import os
import re
import zipfile

import torch

from transformers import AutoTokenizer, GPTaConfig

def 		lowercase_      (		_UpperCAmelCase  ,      _UpperCAmelCase  ,      _UpperCAmelCase=0	):




	"""simple docstring"""






	if name is None:
		A_  :				Union[str, Any]	   =		None
	else:
		A_  :				str	   =		'''.''' * max(0  ,      spaces - 2	) + '''# {:''' + str(50 - spaces	) + '''s}'''
		A_  :				List[Any]	   =		fmt.format(_UpperCAmelCase	)

	# Print and recurse (if needed).
	if isinstance(_UpperCAmelCase  ,      _UpperCAmelCase	):
		if msg is not None:
			print(_UpperCAmelCase	)
		for k in val.keys():
			recursive_print(_UpperCAmelCase  ,      val[k]  ,      spaces + 2	)
	elif isinstance(_UpperCAmelCase  ,      torch.Tensor	):
		print(_UpperCAmelCase  ,      ''':'''  ,      val.size()	)
	else:
		print(_UpperCAmelCase  ,      ''':'''  ,      _UpperCAmelCase	)

def 		lowercase_      (		_UpperCAmelCase  ,      _UpperCAmelCase  ,      _UpperCAmelCase  ,      _UpperCAmelCase  ,      _UpperCAmelCase	):




	"""simple docstring"""






	A_  :				Dict	   =		param.size()
	if checkpoint_version == 1.0:
		# version 1.0 stores [num_heads * hidden_size * num_splits, :]
		A_  :				str	   =		(num_heads, hidden_size, num_splits) + input_shape[1:]
		A_  :				List[str]	   =		param.view(*_UpperCAmelCase	)
		A_  :				List[str]	   =		param.transpose(0  ,      2	)
		A_  :				int	   =		param.transpose(1  ,      2	).contiguous()
	elif checkpoint_version >= 2.0:
		# other versions store [num_heads * num_splits * hidden_size, :]
		A_  :				Tuple	   =		(num_heads, num_splits, hidden_size) + input_shape[1:]
		A_  :				Tuple	   =		param.view(*_UpperCAmelCase	)
		A_  :				Union[str, Any]	   =		param.transpose(0  ,      1	).contiguous()
	A_  :				Union[str, Any]	   =		param.view(*_UpperCAmelCase	)
	return param

def 		lowercase_      (		_UpperCAmelCase  ,      _UpperCAmelCase  ,      _UpperCAmelCase	):




	"""simple docstring"""






	A_  :				Union[str, Any]	   =		{}

	# old versions did not store training args
	A_  :				int	   =		input_state_dict.get('''args'''  ,      _UpperCAmelCase	)
	if ds_args is not None:
		# do not make the user write a config file when the exact dimensions/sizes are already in the checkpoint
		# from pprint import pprint
		# pprint(vars(ds_args))

		A_  :				Dict	   =		ds_args.padded_vocab_size
		A_  :				Optional[Any]	   =		ds_args.max_position_embeddings
		A_  :				int	   =		ds_args.hidden_size
		A_  :				Dict	   =		ds_args.num_layers
		A_  :				Dict	   =		ds_args.num_attention_heads
		A_  :				List[str]	   =		ds_args.ffn_hidden_size
		# pprint(config)

	# The number of heads.
	A_  :				Union[str, Any]	   =		config.n_head
	# The hidden_size per head.
	A_  :				Dict	   =		config.n_embd // config.n_head
	# Megatron-LM checkpoint version
	if "checkpoint_version" in input_state_dict.keys():
		A_  :				str	   =		input_state_dict['''checkpoint_version''']
	else:
		A_  :				Dict	   =		0.0

	# The model.
	A_  :				Any	   =		input_state_dict['''model''']
	# The language model.
	A_  :				List[str]	   =		model['''language_model''']
	# The embeddings.
	A_  :				int	   =		lm['''embedding''']

	# The word embeddings.
	A_  :				List[Any]	   =		embeddings['''word_embeddings''']['''weight''']
	# Truncate the embedding table to vocab_size rows.
	A_  :				List[str]	   =		word_embeddings[: config.vocab_size, :]
	A_  :				str	   =		word_embeddings

	# The position embeddings.
	A_  :				str	   =		embeddings['''position_embeddings''']['''weight''']
	# Read the causal mask dimension (seqlen). [max_sequence_length, hidden_size]
	A_  :				Optional[Any]	   =		pos_embeddings.size(0	)
	if n_positions != config.n_positions:
		raise ValueError(
		    f"""pos_embeddings.max_sequence_length={n_positions} and config.n_positions={config.n_positions} don't match"""	)
	# Store the position embeddings.
	A_  :				Dict	   =		pos_embeddings

	# The transformer.
	A_  :				Optional[Any]	   =		lm['''transformer'''] if '''transformer''' in lm.keys() else lm['''encoder''']

	# The regex to extract layer names.
	A_  :				Optional[int]	   =		re.compile(r'''layers\.(\d+)\.([a-z0-9_.]+)\.([a-z]+)'''	)

	# The simple map of names for "automated" rules.
	A_  :				Dict	   =		{
	    '''attention.dense''': '''.attn.c_proj.''',
	    '''self_attention.dense''': '''.attn.c_proj.''',
	    '''mlp.dense_h_to_4h''': '''.mlp.c_fc.''',
	    '''mlp.dense_4h_to_h''': '''.mlp.c_proj.''',
	}

	# Extract the layers.
	for key, val in transformer.items():
		# Match the name.
		A_  :				Tuple	   =		layer_re.match(_UpperCAmelCase	)

		# Stop if that's not a layer
		if m is None:
			break

		# The index of the layer.
		A_  :				List[Any]	   =		int(m.group(1	)	)
		# The name of the operation.
		A_  :				Dict	   =		m.group(2	)
		# Is it a weight or a bias?
		A_  :				List[Any]	   =		m.group(3	)

		# The name of the layer.
		A_  :				List[str]	   =		f"""transformer.h.{layer_idx}"""

		# For layernorm(s), simply store the layer norm.
		if op_name.endswith('''layernorm'''	):
			A_  :				Optional[Any]	   =		'''ln_1''' if op_name.startswith('''input'''	) else '''ln_2'''
			A_  :				List[str]	   =		val

		# Transpose the QKV matrix.
		elif (
		    op_name == "attention.query_key_value" or op_name == "self_attention.query_key_value"
		) and weight_or_bias == "weight":
			# Insert a tensor of 1x1xDxD bias.
			A_  :				Optional[Any]	   =		torch.tril(torch.ones((n_positions, n_positions)  ,      dtype=torch.floataa	)	).view(
			    1  ,      1  ,      _UpperCAmelCase  ,      _UpperCAmelCase	)
			A_  :				List[Any]	   =		causal_mask

			# Insert a "dummy" tensor for masked_bias.
			A_  :				Optional[int]	   =		torch.tensor(-1E4  ,      dtype=torch.floataa	)
			A_  :				Optional[Any]	   =		masked_bias

			A_  :				List[Any]	   =		fix_query_key_value_ordering(_UpperCAmelCase  ,      _UpperCAmelCase  ,      3  ,      _UpperCAmelCase  ,      _UpperCAmelCase	)
			# Megatron stores (3*D) x D but transformers-GPT2 expects D x 3*D.
			A_  :				str	   =		out_val.transpose(0  ,      1	).contiguous()
			# Store.
			A_  :				Optional[Any]	   =		out_val

		# Transpose the bias.
		elif (
		    op_name == "attention.query_key_value" or op_name == "self_attention.query_key_value"
		) and weight_or_bias == "bias":
			A_  :				Any	   =		fix_query_key_value_ordering(_UpperCAmelCase  ,      _UpperCAmelCase  ,      3  ,      _UpperCAmelCase  ,      _UpperCAmelCase	)
			# Store. No change of shape.
			A_  :				List[str]	   =		out_val

		# Transpose the weights.
		elif weight_or_bias == "weight":
			A_  :				Dict	   =		megatron_to_transformers[op_name]
			A_  :				List[str]	   =		val.transpose(0  ,      1	)

		# Copy the bias.
		elif weight_or_bias == "bias":
			A_  :				Tuple	   =		megatron_to_transformers[op_name]
			A_  :				Any	   =		val

    # DEBUG.
	assert config.n_layer == layer_idx + 1

	# The final layernorm.
	A_  :				Any	   =		transformer['''final_layernorm.weight''']
	A_  :				int	   =		transformer['''final_layernorm.bias''']

	# For LM head, transformers' wants the matrix to weight embeddings.
	A_  :				Union[str, Any]	   =		word_embeddings

	# It should be done!
	return output_state_dict

def 		lowercase_      (		):




	"""simple docstring"""






	A_  :				Tuple	   =		argparse.ArgumentParser()
	parser.add_argument('''--print-checkpoint-structure'''  ,      action='''store_true'''	)
	parser.add_argument(
	    '''path_to_checkpoint'''  ,      type=_UpperCAmelCase  ,      help='''Path to the checkpoint file (.zip archive or direct .pt file)'''  ,      )
	parser.add_argument(
	    '''--config_file'''  ,      default=''''''  ,      type=_UpperCAmelCase  ,      help='''An optional config json file describing the pre-trained model.'''  ,      )
	A_  :				Tuple	   =		parser.parse_args()

	# Extract the basename.
	A_  :				Union[str, Any]	   =		os.path.dirname(args.path_to_checkpoint	)

	# Load the model.
	# the .zip is very optional, let's keep it for backward compatibility
	print(f"""Extracting PyTorch state dictionary from {args.path_to_checkpoint}"""	)
	if args.path_to_checkpoint.endswith('''.zip'''	):
		with zipfile.ZipFile(args.path_to_checkpoint  ,      '''r'''	) as checkpoint:
			with checkpoint.open('''release/mp_rank_00/model_optim_rng.pt'''	) as pytorch_dict:
				A_  :				Tuple	   =		torch.load(_UpperCAmelCase  ,      map_location='''cpu'''	)
	else:
		A_  :				Tuple	   =		torch.load(args.path_to_checkpoint  ,      map_location='''cpu'''	)

	A_  :				str	   =		input_state_dict.get('''args'''  ,      _UpperCAmelCase	)

	# Read the config, or default to the model released by NVIDIA.
	if args.config_file == "":
		if ds_args is not None:
			if ds_args.bias_gelu_fusion:
				A_  :				int	   =		'''gelu_fast'''
			elif ds_args.openai_gelu:
				A_  :				Union[str, Any]	   =		'''gelu_new'''
			else:
				A_  :				Union[str, Any]	   =		'''gelu'''
		else:
			# in the very early days this used to be "gelu_new"
			A_  :				Any	   =		'''gelu_new'''

		# Spell out all parameters in case the defaults change.
		A_  :				Dict	   =		GPTaConfig(
		    vocab_size=5_0257  ,      n_positions=1024  ,      n_embd=1024  ,      n_layer=24  ,      n_head=16  ,      n_inner=4096  ,      activation_function=_UpperCAmelCase  ,      resid_pdrop=0.1  ,      embd_pdrop=0.1  ,      attn_pdrop=0.1  ,      layer_norm_epsilon=1E-5  ,      initializer_range=0.02  ,      summary_type='''cls_index'''  ,      summary_use_proj=_UpperCAmelCase  ,      summary_activation=_UpperCAmelCase  ,      summary_proj_to_labels=_UpperCAmelCase  ,      summary_first_dropout=0.1  ,      scale_attn_weights=_UpperCAmelCase  ,      use_cache=_UpperCAmelCase  ,      bos_token_id=5_0256  ,      eos_token_id=5_0256  ,      )
	else:
		A_  :				Tuple	   =		GPTaConfig.from_json_file(args.config_file	)

	A_  :				Any	   =		['''GPT2LMHeadModel''']

	# Convert.
	print('''Converting'''	)
	A_  :				Optional[Any]	   =		convert_megatron_checkpoint(_UpperCAmelCase  ,      _UpperCAmelCase  ,      _UpperCAmelCase	)

	# Print the structure of converted state dict.
	if args.print_checkpoint_structure:
		recursive_print(_UpperCAmelCase  ,      _UpperCAmelCase	)

	# Add tokenizer class info to config
	# see https://github.com/huggingface/transformers/issues/13906)
	if ds_args is not None:
		A_  :				str	   =		ds_args.tokenizer_type
		if tokenizer_type == "GPT2BPETokenizer":
			A_  :				List[Any]	   =		'''gpt2'''
		elif tokenizer_type == "PretrainedFromHF":
			A_  :				Union[str, Any]	   =		ds_args.tokenizer_name_or_path
		else:
			raise ValueError(f"""Unrecognized tokenizer_type {tokenizer_type}"""	)
	else:
		A_  :				Any	   =		'''gpt2'''

	A_  :				List[str]	   =		AutoTokenizer.from_pretrained(_UpperCAmelCase	)
	A_  :				Union[str, Any]	   =		type(_UpperCAmelCase	).__name__
	A_  :				Tuple	   =		tokenizer_class

	# Store the config to file.
	print('''Saving config'''	)
	config.save_pretrained(_UpperCAmelCase	)

	# Save tokenizer based on args
	print(f"""Adding {tokenizer_class} tokenizer files"""	)
	tokenizer.save_pretrained(_UpperCAmelCase	)

	# Store the state_dict to file.
	A_  :				Optional[int]	   =		os.path.join(_UpperCAmelCase  ,      '''pytorch_model.bin'''	)
	print(f"""Saving checkpoint to \"{output_checkpoint_file}\""""	)
	torch.save(_UpperCAmelCase  ,      _UpperCAmelCase	)


####################################################################################################

if __name__ == "__main__":
		main()

####################################################################################################











"""simple docstring"""

import gc
import random
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DiffusionPipeline,
    EulerDiscreteScheduler,
    StableDiffusionXLImgaImgPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import floats_tensor, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import (
    IMAGE_TO_IMAGE_IMAGE_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()

class        lowercase      (    __UpperCAmelCase     ,	__UpperCAmelCase     ,	unittest.TestCase):
				__lowerCAmelCase    :    int         =   StableDiffusionXLImgaImgPipeline
				__lowerCAmelCase    :    List[Any]         =   TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"""height""", """width"""}
				__lowerCAmelCase    :    Optional[int]         =   PipelineTesterMixin.required_optional_params - {"""latents"""}
				__lowerCAmelCase    :    Any         =   TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
				__lowerCAmelCase    :    Optional[Any]         =   IMAGE_TO_IMAGE_IMAGE_PARAMS
				__lowerCAmelCase    :    List[str]         =   IMAGE_TO_IMAGE_IMAGE_PARAMS







				def 					a_  (							self   :					Any       ):



					"""simple docstring"""

					torch.manual_seed(0       )
					A_  :				int	   =		UNetaDConditionModel(
					    block_out_channels=(32, 64)      ,     layers_per_block=2      ,     sample_size=32      ,     in_channels=4      ,     out_channels=4      ,     down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')      ,     up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')      ,     attention_head_dim=(2, 4)      ,     use_linear_projection=_lowerCamelCase      ,     addition_embed_type='''text_time'''      ,     addition_time_embed_dim=8      ,     transformer_layers_per_block=(1, 2)      ,     projection_class_embeddings_input_dim=80      ,     cross_attention_dim=64      ,     )
					A_  :				Tuple	   =		EulerDiscreteScheduler(
					    beta_start=0.00085      ,     beta_end=0.012      ,     steps_offset=1      ,     beta_schedule='''scaled_linear'''      ,     timestep_spacing='''leading'''      ,     )
					torch.manual_seed(0       )
					A_  :				Optional[Any]	   =		AutoencoderKL(
					    block_out_channels=[32, 64]      ,     in_channels=3      ,     out_channels=3      ,     down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''']      ,     up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''']      ,     latent_channels=4      ,     sample_size=1_28      ,     )
					torch.manual_seed(0       )
					A_  :				Dict	   =		CLIPTextConfig(
					    bos_token_id=0      ,     eos_token_id=2      ,     hidden_size=32      ,     intermediate_size=37      ,     layer_norm_eps=1E-05      ,     num_attention_heads=4      ,     num_hidden_layers=5      ,     pad_token_id=1      ,     vocab_size=10_00      ,     hidden_act='''gelu'''      ,     projection_dim=32      ,     )
					A_  :				Any	   =		CLIPTextModel(_lowerCamelCase       )
					A_  :				List[Any]	   =		CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''      ,     local_files_only=_lowerCamelCase       )

					A_  :				int	   =		CLIPTextModelWithProjection(_lowerCamelCase       )
					A_  :				Any	   =		CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''      ,     local_files_only=_lowerCamelCase       )

					A_  :				Union[str, Any]	   =		{
					    '''unet''': unet,
					    '''scheduler''': scheduler,
					    '''vae''': vae,
					    '''text_encoder''': text_encoder,
					    '''tokenizer''': tokenizer,
					    '''text_encoder_2''': text_encoder_a,
					    '''tokenizer_2''': tokenizer_a,
					    # "safety_checker": None,
					    # "feature_extractor": None,
					}
					return components







				def 					a_  (							self   :					Tuple      ,     _lowerCamelCase   :					int      ,     _lowerCamelCase   :					List[Any]=0       ):



					"""simple docstring"""

					A_  :				Dict	   =		floats_tensor((1, 3, 32, 32)      ,     rng=random.Random(_lowerCamelCase       )       ).to(_lowerCamelCase       )
					A_  :				Optional[Any]	   =		image / 2 + 0.5
					if str(_lowerCamelCase       ).startswith('''mps'''       ):
						A_  :				str	   =		torch.manual_seed(_lowerCamelCase       )
					else:
						A_  :				Any	   =		torch.Generator(device=_lowerCamelCase       ).manual_seed(_lowerCamelCase       )
					A_  :				Any	   =		{
					    '''prompt''': '''A painting of a squirrel eating a burger''',
					    '''image''': image,
					    '''generator''': generator,
					    '''num_inference_steps''': 2,
					    '''guidance_scale''': 5.0,
					    '''output_type''': '''numpy''',
					    '''strength''': 0.75,
					}
					return inputs







				def 					a_  (							self   :					List[Any]       ):



					"""simple docstring"""

					A_  :				List[str]	   =		'''cpu'''  # ensure determinism for the device-dependent torch.Generator
					A_  :				Dict	   =		self.get_dummy_components()
					A_  :				Dict	   =		StableDiffusionXLImgaImgPipeline(**_lowerCamelCase       )
					A_  :				int	   =		sd_pipe.to(_lowerCamelCase       )
					sd_pipe.set_progress_bar_config(disable=_lowerCamelCase       )

					A_  :				Union[str, Any]	   =		self.get_dummy_inputs(_lowerCamelCase       )
					A_  :				Tuple	   =		sd_pipe(**_lowerCamelCase       ).images
					A_  :				Dict	   =		image[0, -3:, -3:, -1]

					assert image.shape == (1, 32, 32, 3)

					A_  :				Union[str, Any]	   =		np.array([0.4656, 0.4840, 0.4439, 0.6698, 0.5574, 0.4524, 0.5799, 0.5943, 0.5165]       )

					assert np.abs(image_slice.flatten() - expected_slice       ).max() < 1E-2







				def 					a_  (							self   :					Optional[int]       ):



					"""simple docstring"""

					super().test_attention_slicing_forward_pass(expected_max_diff=3E-3       )







				def 					a_  (							self   :					Dict       ):



					"""simple docstring"""

					super().test_inference_batch_single_identical(expected_max_diff=3E-3       )







				def 					a_  (							self   :					List[Any]       ):



					"""simple docstring"""

					pass







				def 					a_  (							self   :					List[str]       ):



					"""simple docstring"""

					A_  :				Optional[Any]	   =		self.get_dummy_components()
					A_  :				int	   =		StableDiffusionXLImgaImgPipeline(**_lowerCamelCase       )
					A_  :				Dict	   =		sd_pipe.to(_lowerCamelCase       )
					A_  :				List[Any]	   =		sd_pipe.to(_lowerCamelCase       )
					sd_pipe.set_progress_bar_config(disable=_lowerCamelCase       )

					# forward without prompt embeds
					A_  :				Any	   =		self.get_dummy_inputs(_lowerCamelCase       )
					A_  :				Union[str, Any]	   =		3 * ['''this is a negative prompt''']
					A_  :				Any	   =		negative_prompt
					A_  :				Any	   =		3 * [inputs['''prompt''']]

					A_  :				Union[str, Any]	   =		sd_pipe(**_lowerCamelCase       )
					A_  :				List[str]	   =		output.images[0, -3:, -3:, -1]

					# forward with prompt embeds
					A_  :				Optional[Any]	   =		self.get_dummy_inputs(_lowerCamelCase       )
					A_  :				str	   =		3 * ['''this is a negative prompt''']
					A_  :				List[str]	   =		3 * [inputs.pop('''prompt'''       )]

					(
					    (
					    A_
					)   , (
					    A_
					)   , (
					    A_
					)   , (
					    A_
					)   , 
					)  :				List[str]	   =		sd_pipe.encode_prompt(_lowerCamelCase      ,     negative_prompt=_lowerCamelCase       )

					A_  :				str	   =		sd_pipe(
					    **_lowerCamelCase      ,     prompt_embeds=_lowerCamelCase      ,     negative_prompt_embeds=_lowerCamelCase      ,     pooled_prompt_embeds=_lowerCamelCase      ,     negative_pooled_prompt_embeds=_lowerCamelCase      ,     )
					A_  :				Any	   =		output.images[0, -3:, -3:, -1]

					# make sure that it's equal
					assert np.abs(image_slice_a.flatten() - image_slice_a.flatten()       ).max() < 1E-4

@slow
@require_torch_gpu
class        lowercase      (    unittest.TestCase):







				def 					a_  (							self   :					Dict       ):



					"""simple docstring"""

					super().tearDown()
					gc.collect()
					torch.cuda.empty_cache()







				def 					a_  (							self   :					Tuple      ,     _lowerCamelCase   :					List[str]      ,     _lowerCamelCase   :					Union[str, Any]="cpu"      ,     _lowerCamelCase   :					Optional[Any]=torch.floataa      ,     _lowerCamelCase   :					Optional[int]=0       ):



					"""simple docstring"""

					A_  :				int	   =		torch.Generator(device=_lowerCamelCase       ).manual_seed(_lowerCamelCase       )
					A_  :				List[Any]	   =		np.random.RandomState(_lowerCamelCase       ).standard_normal((1, 4, 64, 64)       )
					A_  :				Union[str, Any]	   =		torch.from_numpy(_lowerCamelCase       ).to(device=_lowerCamelCase      ,     dtype=_lowerCamelCase       )
					A_  :				Optional[Any]	   =		{
					    '''prompt''': '''a photograph of an astronaut riding a horse''',
					    '''latents''': latents,
					    '''generator''': generator,
					    '''num_inference_steps''': 3,
					    '''guidance_scale''': 7.5,
					    '''output_type''': '''numpy''',
					}
					return inputs







				def 					a_  (							self   :					Tuple       ):



					"""simple docstring"""

					A_  :				str	   =		DiffusionPipeline.from_pretrained('''stabilityai/stable-diffusion-2-base'''       )
					pipe.to(_lowerCamelCase       )
					pipe.set_progress_bar_config(disable=_lowerCamelCase       )

					A_  :				Optional[Any]	   =		self.get_inputs(_lowerCamelCase       )
					A_  :				Union[str, Any]	   =		pipe(**_lowerCamelCase       ).images
					A_  :				List[Any]	   =		image[0, -3:, -3:, -1].flatten()

					assert image.shape == (1, 5_12, 5_12, 3)
					A_  :				int	   =		np.array([0.49493, 0.47896, 0.40798, 0.54214, 0.53212, 0.48202, 0.47656, 0.46329, 0.48506]       )
					assert np.abs(image_slice - expected_slice       ).max() < 7E-3





