





'''simple docstring'''




import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    EulerAncestralDiscreteScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
    StableDiffusionInstructPixaPixPipeline,
    UNetaDConditionModel,
)
from diffusers.image_processor import VaeImageProcessor
from diffusers.utils import floats_tensor, load_image, 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_INPAINTING_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()






class 				__snake_case     (					__magic_name__       ,       __magic_name__       ,       __magic_name__       ,       unittest.TestCase							):
	__lowerCAmelCase       						=  StableDiffusionInstructPixaPixPipeline
	__lowerCAmelCase       						=  TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'''height''', '''width''', '''cross_attention_kwargs'''}
	__lowerCAmelCase       						=  TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
	__lowerCAmelCase       						=  IMAGE_TO_IMAGE_IMAGE_PARAMS
	__lowerCAmelCase       						=  IMAGE_TO_IMAGE_IMAGE_PARAMS





	def   _snake_case   (		self		)      ->		str:
		torch.manual_seed(0		)
		snake_case__	       =   UNetaDConditionModel(
		    block_out_channels=(32, 64)      ,       layers_per_block=2      ,       sample_size=32      ,       in_channels=8      ,       out_channels=4      ,       down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')      ,       up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D')      ,       cross_attention_dim=32      ,       )
		snake_case__	       =   PNDMScheduler(skip_prk_steps=UpperCamelCase_		)
		torch.manual_seed(0		)
		snake_case__	       =   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      ,       )
		torch.manual_seed(0		)
		snake_case__	       =   CLIPTextConfig(
		    bos_token_id=0      ,       eos_token_id=2      ,       hidden_size=32      ,       intermediate_size=37      ,       layer_norm_eps=1E-0_5      ,       num_attention_heads=4      ,       num_hidden_layers=5      ,       pad_token_id=1      ,       vocab_size=1000      ,       )
		snake_case__	       =   CLIPTextModel(UpperCamelCase_		)
		snake_case__	       =   CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'		)

		snake_case__	       =   {
		    'unet': unet,
		    'scheduler': scheduler,
		    'vae': vae,
		    'text_encoder': text_encoder,
		    'tokenizer': tokenizer,
		    'safety_checker': None,
		    'feature_extractor': None,
		}
		return components





	def   _snake_case   (		self      ,       UpperCamelCase_      ,       UpperCamelCase_=0		)      ->		Union[str, Any]:
		snake_case__	       =   floats_tensor((1, 3, 32, 32)      ,       rng=random.Random(UpperCamelCase_		)		).to(UpperCamelCase_		)
		snake_case__	       =   image.cpu().permute(0      ,       2      ,       3      ,       1		)[0]
		snake_case__	       =   Image.fromarray(np.uinta(UpperCamelCase_		)		).convert('RGB'		)
		if str(UpperCamelCase_		).startswith('mps'		):
			snake_case__	       =   torch.manual_seed(UpperCamelCase_		)
		else:
			snake_case__	       =   torch.Generator(device=UpperCamelCase_		).manual_seed(UpperCamelCase_		)
		snake_case__	       =   {
		    'prompt': 'A painting of a squirrel eating a burger',
		    'image': image,
		    'generator': generator,
		    'num_inference_steps': 2,
		    'guidance_scale': 6.0,
		    'image_guidance_scale': 1,
		    'output_type': 'numpy',
		}
		return inputs





	def   _snake_case   (		self		)      ->		int:
		snake_case__	       =   'cpu'  # ensure determinism for the device-dependent torch.Generator
		snake_case__	       =   self.get_dummy_components()
		snake_case__	       =   StableDiffusionInstructPixaPixPipeline(**UpperCamelCase_		)
		snake_case__	       =   sd_pipe.to(UpperCamelCase_		)
		sd_pipe.set_progress_bar_config(disable=UpperCamelCase_		)

		snake_case__	       =   self.get_dummy_inputs(UpperCamelCase_		)
		snake_case__	       =   sd_pipe(**UpperCamelCase_		).images
		snake_case__	       =   image[0, -3:, -3:, -1]
		assert image.shape == (1, 32, 32, 3)
		snake_case__	       =   np.array([0.7_5_2_6, 0.3_7_5_0, 0.4_5_4_7, 0.6_1_1_7, 0.5_8_6_6, 0.5_0_1_6, 0.4_3_2_7, 0.5_6_4_2, 0.4_8_1_5]		)

		assert np.abs(image_slice.flatten() - expected_slice		).max() < 1E-3





	def   _snake_case   (		self		)      ->		Union[str, Any]:
		snake_case__	       =   'cpu'  # ensure determinism for the device-dependent torch.Generator
		snake_case__	       =   self.get_dummy_components()
		snake_case__	       =   StableDiffusionInstructPixaPixPipeline(**UpperCamelCase_		)
		snake_case__	       =   sd_pipe.to(UpperCamelCase_		)
		sd_pipe.set_progress_bar_config(disable=UpperCamelCase_		)

		snake_case__	       =   self.get_dummy_inputs(UpperCamelCase_		)
		snake_case__	       =   'french fries'
		snake_case__	       =   sd_pipe(**UpperCamelCase_      ,       negative_prompt=UpperCamelCase_		)
		snake_case__	       =   output.images
		snake_case__	       =   image[0, -3:, -3:, -1]

		assert image.shape == (1, 32, 32, 3)
		snake_case__	       =   np.array([0.7_5_1_1, 0.3_6_4_2, 0.4_5_5_3, 0.6_2_3_6, 0.5_7_9_7, 0.5_0_1_3, 0.4_3_4_3, 0.5_6_1_1, 0.4_8_3_1]		)

		assert np.abs(image_slice.flatten() - expected_slice		).max() < 1E-3





	def   _snake_case   (		self		)      ->		int:
		snake_case__	       =   'cpu'  # ensure determinism for the device-dependent torch.Generator
		snake_case__	       =   self.get_dummy_components()
		snake_case__	       =   StableDiffusionInstructPixaPixPipeline(**UpperCamelCase_		)
		snake_case__	       =   sd_pipe.to(UpperCamelCase_		)
		sd_pipe.set_progress_bar_config(disable=UpperCamelCase_		)

		snake_case__	       =   self.get_dummy_inputs(UpperCamelCase_		)
		snake_case__	       =   [inputs['prompt']] * 2

		snake_case__	       =   np.array(inputs['image']		).astype(np.floataa		) / 2_5_5.0
		snake_case__	       =   torch.from_numpy(UpperCamelCase_		).unsqueeze(0		).to(UpperCamelCase_		)
		snake_case__	       =   image / 2 + 0.5
		snake_case__	       =   image.permute(0      ,       3      ,       1      ,       2		)
		snake_case__	       =   image.repeat(2      ,       1      ,       1      ,       1		)

		snake_case__	       =   sd_pipe(**UpperCamelCase_		).images
		snake_case__	       =   image[-1, -3:, -3:, -1]

		assert image.shape == (2, 32, 32, 3)
		snake_case__	       =   np.array([0.5_8_1_2, 0.5_7_4_8, 0.5_2_2_2, 0.5_9_0_8, 0.5_6_9_5, 0.7_1_7_4, 0.6_8_0_4, 0.5_5_2_3, 0.5_5_7_9]		)

		assert np.abs(image_slice.flatten() - expected_slice		).max() < 1E-3





	def   _snake_case   (		self		)      ->		int:
		snake_case__	       =   'cpu'  # ensure determinism for the device-dependent torch.Generator
		snake_case__	       =   self.get_dummy_components()
		snake_case__	       =   EulerAncestralDiscreteScheduler(
		    beta_start=0.0_0_0_8_5      ,       beta_end=0.0_1_2      ,       beta_schedule='scaled_linear'		)
		snake_case__	       =   StableDiffusionInstructPixaPixPipeline(**UpperCamelCase_		)
		snake_case__	       =   sd_pipe.to(UpperCamelCase_		)
		sd_pipe.set_progress_bar_config(disable=UpperCamelCase_		)

		snake_case__	       =   self.get_dummy_inputs(UpperCamelCase_		)
		snake_case__	       =   sd_pipe(**UpperCamelCase_		).images
		snake_case__	       =   image[0, -3:, -3:, -1]

		snake_case__	       =   [round(UpperCamelCase_      ,       4		) for x in image_slice.flatten().tolist()]
		print(','.join([str(UpperCamelCase_		) for x in slice]		)		)

		assert image.shape == (1, 32, 32, 3)
		snake_case__	       =   np.array([0.7_4_1_7, 0.3_8_4_2, 0.4_7_3_2, 0.5_7_7_6, 0.5_8_9_1, 0.5_1_3_9, 0.4_0_5_2, 0.5_6_7_3, 0.4_9_8_6]		)

		assert np.abs(image_slice.flatten() - expected_slice		).max() < 1E-3





	def   _snake_case   (		self		)      ->		Optional[int]:
		super().test_inference_batch_single_identical(expected_max_diff=3E-3		)





	def   _snake_case   (		self		)      ->		List[str]:
		snake_case__	       =   self.get_dummy_components()
		snake_case__	       =   StableDiffusionInstructPixaPixPipeline(**UpperCamelCase_		)
		snake_case__	       =   VaeImageProcessor(do_resize=UpperCamelCase_      ,       do_normalize=UpperCamelCase_		)
		snake_case__	       =   pipe.to(UpperCamelCase_		)
		pipe.set_progress_bar_config(disable=UpperCamelCase_		)

		snake_case__	       =   pipe(**self.get_dummy_inputs_by_type(UpperCamelCase_      ,       input_image_type='pt'		)		)[0]

		snake_case__	       =   components['vae']
		snake_case__	       =   self.get_dummy_inputs_by_type(UpperCamelCase_      ,       input_image_type='pt'		)

		for image_param in self.image_latents_params:
			if image_param in inputs.keys():
				snake_case__	       =   vae.encode(inputs[image_param]		).latent_dist.mode()

		snake_case__	       =   pipe(**UpperCamelCase_		)[0]

		snake_case__	       =   np.abs(out - out_latents_inputs		).max()
		self.assertLess(UpperCamelCase_      ,       1E-4      ,       'passing latents as image input generate different result from passing image'		)






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





	def   _snake_case   (		self		)      ->		str:
		super().tearDown()
		gc.collect()
		torch.cuda.empty_cache()





	def   _snake_case   (		self      ,       UpperCamelCase_=0		)      ->		List[str]:
		snake_case__	       =   torch.manual_seed(UpperCamelCase_		)
		snake_case__	       =   load_image(
		    'https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_pix2pix/example.jpg'		)
		snake_case__	       =   {
		    'prompt': 'turn him into a cyborg',
		    'image': image,
		    'generator': generator,
		    'num_inference_steps': 3,
		    'guidance_scale': 7.5,
		    'image_guidance_scale': 1.0,
		    'output_type': 'numpy',
		}
		return inputs





	def   _snake_case   (		self		)      ->		str:
		snake_case__	       =   StableDiffusionInstructPixaPixPipeline.from_pretrained(
		    'timbrooks/instruct-pix2pix'      ,       safety_checker=UpperCamelCase_		)
		pipe.to(UpperCamelCase_		)
		pipe.set_progress_bar_config(disable=UpperCamelCase_		)
		pipe.enable_attention_slicing()

		snake_case__	       =   self.get_inputs()
		snake_case__	       =   pipe(**UpperCamelCase_		).images
		snake_case__	       =   image[0, -3:, -3:, -1].flatten()

		assert image.shape == (1, 512, 512, 3)
		snake_case__	       =   np.array([0.5_9_0_2, 0.6_0_1_5, 0.6_0_2_7, 0.5_9_8_3, 0.6_0_9_2, 0.6_0_6_1, 0.5_7_6_5, 0.5_7_8_5, 0.5_5_5_5]		)

		assert np.abs(expected_slice - image_slice		).max() < 1E-3





	def   _snake_case   (		self		)      ->		Any:
		snake_case__	       =   StableDiffusionInstructPixaPixPipeline.from_pretrained(
		    'timbrooks/instruct-pix2pix'      ,       safety_checker=UpperCamelCase_		)
		snake_case__	       =   LMSDiscreteScheduler.from_config(pipe.scheduler.config		)
		pipe.to(UpperCamelCase_		)
		pipe.set_progress_bar_config(disable=UpperCamelCase_		)
		pipe.enable_attention_slicing()

		snake_case__	       =   self.get_inputs()
		snake_case__	       =   pipe(**UpperCamelCase_		).images
		snake_case__	       =   image[0, -3:, -3:, -1].flatten()

		assert image.shape == (1, 512, 512, 3)
		snake_case__	       =   np.array([0.6_5_7_8, 0.6_8_1_7, 0.6_9_7_2, 0.6_7_6_1, 0.6_8_5_6, 0.6_9_1_6, 0.6_4_2_8, 0.6_5_1_6, 0.6_3_0_1]		)

		assert np.abs(expected_slice - image_slice		).max() < 1E-3





	def   _snake_case   (		self		)      ->		Optional[int]:
		snake_case__	       =   StableDiffusionInstructPixaPixPipeline.from_pretrained(
		    'timbrooks/instruct-pix2pix'      ,       safety_checker=UpperCamelCase_		)
		snake_case__	       =   DDIMScheduler.from_config(pipe.scheduler.config		)
		pipe.to(UpperCamelCase_		)
		pipe.set_progress_bar_config(disable=UpperCamelCase_		)
		pipe.enable_attention_slicing()

		snake_case__	       =   self.get_inputs()
		snake_case__	       =   pipe(**UpperCamelCase_		).images
		snake_case__	       =   image[0, -3:, -3:, -1].flatten()

		assert image.shape == (1, 512, 512, 3)
		snake_case__	       =   np.array([0.3_8_2_8, 0.3_8_3_4, 0.3_8_1_8, 0.3_7_9_2, 0.3_8_6_5, 0.3_7_5_2, 0.3_7_9_2, 0.3_8_4_7, 0.3_7_5_3]		)

		assert np.abs(expected_slice - image_slice		).max() < 1E-3





	def   _snake_case   (		self		)      ->		List[str]:
		snake_case__	       =   0

		def callback_fn(UpperCamelCase_      ,       UpperCamelCase_      ,       UpperCamelCase_		) -> None:
			snake_case__	       =   True
			nonlocal number_of_steps
			number_of_steps += 1
			if step == 1:
				snake_case__	       =   latents.detach().cpu().numpy()
				assert latents.shape == (1, 4, 64, 64)
				snake_case__	       =   latents[0, -3:, -3:, -1]
				snake_case__	       =   np.array([-0.2_4_6_3, -0.4_6_4_4, -0.9_7_5_6, 1.5_1_7_6, 1.4_4_1_4, 0.7_8_6_6, 0.9_8_9_7, 0.8_5_2_1, 0.7_9_8_3]		)

				assert np.abs(latents_slice.flatten() - expected_slice		).max() < 5E-2
			elif step == 2:
				snake_case__	       =   latents.detach().cpu().numpy()
				assert latents.shape == (1, 4, 64, 64)
				snake_case__	       =   latents[0, -3:, -3:, -1]
				snake_case__	       =   np.array([-0.2_6_4_4, -0.4_6_2_6, -0.9_6_5_3, 1.5_1_7_6, 1.4_5_5_1, 0.7_6_8_6, 0.9_8_0_5, 0.8_4_5_2, 0.8_1_1_5]		)

				assert np.abs(latents_slice.flatten() - expected_slice		).max() < 5E-2

		snake_case__	       =   False

		snake_case__	       =   StableDiffusionInstructPixaPixPipeline.from_pretrained(
		    'timbrooks/instruct-pix2pix'      ,       safety_checker=UpperCamelCase_      ,       torch_dtype=torch.floataa		)
		snake_case__	       =   pipe.to(UpperCamelCase_		)
		pipe.set_progress_bar_config(disable=UpperCamelCase_		)
		pipe.enable_attention_slicing()

		snake_case__	       =   self.get_inputs()
		pipe(**UpperCamelCase_      ,       callback=UpperCamelCase_      ,       callback_steps=1		)
		assert callback_fn.has_been_called
		assert number_of_steps == 3





	def   _snake_case   (		self		)      ->		Dict:
		torch.cuda.empty_cache()
		torch.cuda.reset_max_memory_allocated()
		torch.cuda.reset_peak_memory_stats()

		snake_case__	       =   StableDiffusionInstructPixaPixPipeline.from_pretrained(
		    'timbrooks/instruct-pix2pix'      ,       safety_checker=UpperCamelCase_      ,       torch_dtype=torch.floataa		)
		snake_case__	       =   pipe.to(UpperCamelCase_		)
		pipe.set_progress_bar_config(disable=UpperCamelCase_		)
		pipe.enable_attention_slicing(1		)
		pipe.enable_sequential_cpu_offload()

		snake_case__	       =   self.get_inputs()
		snake_case__	       =   pipe(**UpperCamelCase_		)

		snake_case__	       =   torch.cuda.max_memory_allocated()
		# make sure that less than 2.2 GB is allocated
		assert mem_bytes < 2.2 * 10**9





	def   _snake_case   (		self		)      ->		Optional[int]:
		snake_case__	       =   self.get_inputs()
		# resize to resolution that is divisible by 8 but not 16 or 32
		snake_case__	       =   inputs['image'].resize((504, 504)		)

		snake_case__	       =   'timbrooks/instruct-pix2pix'
		snake_case__	       =   StableDiffusionInstructPixaPixPipeline.from_pretrained(
		    UpperCamelCase_      ,       safety_checker=UpperCamelCase_      ,       )
		pipe.to(UpperCamelCase_		)
		pipe.set_progress_bar_config(disable=UpperCamelCase_		)
		pipe.enable_attention_slicing()

		snake_case__	       =   pipe(**UpperCamelCase_		)
		snake_case__	       =   output.images[0]

		snake_case__	       =   image[255:258, 383:386, -1]

		assert image.shape == (504, 504, 3)
		snake_case__	       =   np.array([0.2_7_2_6, 0.2_5_2_9, 0.2_6_6_4, 0.2_6_5_5, 0.2_6_4_1, 0.2_6_4_2, 0.2_5_9_1, 0.2_6_4_9, 0.2_5_9_0]		)

		assert np.abs(image_slice.flatten() - expected_slice		).max() < 5E-3










'''simple docstring'''




import argparse
import pickle

import numpy as np
import torch
from torch import nn

from transformers import ReformerConfig, ReformerModelWithLMHead
from transformers.utils import logging


logging.set_verbosity_info()




def 						__lowerCamelCase			(	UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_=None							)   ->List[str]:
	# set parameter of one layer
	assert torch_layer.weight.shape == weight.shape, f'''{torch_layer} layer.weight does not match'''
	snake_case__	       =   nn.Parameter(UpperCAmelCase_							)
	if bias is not None:
		assert torch_layer.bias.shape == bias.shape, f'''{torch_layer} layer.bias does not match'''
		snake_case__	       =   nn.Parameter(UpperCAmelCase_							)




def 						__lowerCamelCase			(	UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_							)   ->List[Any]:
	# set torch weights for 1-to-1 comparison
	snake_case__	       =   np.asarray(weights[0]							)
	snake_case__	       =   np.asarray(weights[1]							)
	snake_case__	       =   np.asarray(weights[2]							)

	set_param(
	    torch_layer.self_attention.query_key					,				torch.tensor(UpperCAmelCase_							).transpose(1					,				2							).contiguous().view(-1					,				UpperCAmelCase_							)					,				)
	set_param(
	    torch_layer.self_attention.value					,				torch.tensor(UpperCAmelCase_							).transpose(1					,				2							).contiguous().view(-1					,				UpperCAmelCase_							)					,				)
	set_param(
	    torch_layer.output.dense					,				torch.tensor(UpperCAmelCase_							).view(-1					,				UpperCAmelCase_							).contiguous().transpose(0					,				1							)					,				)




def 						__lowerCamelCase			(	UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_							)   ->List[Any]:
	# set torch weights for 1-to-1 comparison
	snake_case__	       =   np.asarray(weights[0]							)
	snake_case__	       =   np.asarray(weights[1]							)
	snake_case__	       =   np.asarray(weights[2]							)
	snake_case__	       =   np.asarray(weights[3]							)

	set_param(
	    torch_layer.self_attention.query					,				torch.tensor(UpperCAmelCase_							).transpose(1					,				2							).contiguous().view(-1					,				UpperCAmelCase_							)					,				)
	set_param(
	    torch_layer.self_attention.key					,				torch.tensor(UpperCAmelCase_							).transpose(1					,				2							).contiguous().view(-1					,				UpperCAmelCase_							)					,				)
	set_param(
	    torch_layer.self_attention.value					,				torch.tensor(UpperCAmelCase_							).transpose(1					,				2							).contiguous().view(-1					,				UpperCAmelCase_							)					,				)
	set_param(
	    torch_layer.output.dense					,				torch.tensor(UpperCAmelCase_							).view(-1					,				UpperCAmelCase_							).contiguous().transpose(0					,				1							)					,				)




def 						__lowerCamelCase			(	UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_							)   ->Dict:
	# layernorm 1
	snake_case__	       =   weights[0][0][0]
	snake_case__	       =   np.asarray(layer_norm_a[0]							)
	snake_case__	       =   np.asarray(layer_norm_a[1]							)
	set_param(
	    torch_block.attention.layer_norm					,				torch.tensor(UpperCAmelCase_							)					,				torch.tensor(UpperCAmelCase_							)					,				)

	# lsh weights + output
	snake_case__	       =   weights[0][1]
	if len(UpperCAmelCase_							) < 4:
		set_layer_weights_in_torch_lsh(UpperCAmelCase_					,				torch_block.attention					,				UpperCAmelCase_							)
	else:
		set_layer_weights_in_torch_local(UpperCAmelCase_					,				torch_block.attention					,				UpperCAmelCase_							)

	# intermediate weighs
	snake_case__	       =   weights[2][0][1][2]

	# Chunked Feed Forward
	if len(UpperCAmelCase_							) == 4:
		snake_case__	       =   intermediate_weights[2]

	# layernorm 2
	snake_case__	       =   np.asarray(intermediate_weights[0][0]							)
	snake_case__	       =   np.asarray(intermediate_weights[0][1]							)
	set_param(
	    torch_block.feed_forward.layer_norm					,				torch.tensor(UpperCAmelCase_							)					,				torch.tensor(UpperCAmelCase_							)					,				)

	# intermediate dense
	snake_case__	       =   np.asarray(intermediate_weights[1][0]							)
	snake_case__	       =   np.asarray(intermediate_weights[1][1]							)
	set_param(
	    torch_block.feed_forward.dense.dense					,				torch.tensor(UpperCAmelCase_							).transpose(0					,				1							).contiguous()					,				torch.tensor(UpperCAmelCase_							)					,				)

	# intermediate out
	snake_case__	       =   np.asarray(intermediate_weights[4][0]							)
	snake_case__	       =   np.asarray(intermediate_weights[4][1]							)
	set_param(
	    torch_block.feed_forward.output.dense					,				torch.tensor(UpperCAmelCase_							).transpose(0					,				1							).contiguous()					,				torch.tensor(UpperCAmelCase_							)					,				)




def 						__lowerCamelCase			(	UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_							)   ->Union[str, Any]:
	# reformer model
	snake_case__	       =   torch_model.reformer

	# word embeds
	snake_case__	       =   np.asarray(weights[1]							)
	set_param(
	    torch_model_reformer.embeddings.word_embeddings					,				torch.tensor(UpperCAmelCase_							)					,				)

	if isinstance(weights[3]					,				UpperCAmelCase_							):
		snake_case__	       =   torch_model_reformer.embeddings.position_embeddings
		for emb_idx in range(len(position_embeddings.weights							)							):
			snake_case__	       =   np.asarray(weights[3][emb_idx][0]							)
			assert (
			    position_embeddings.weights[emb_idx].shape == emb_weights.shape
			), f'''{position_embeddings[emb_idx]} emb does not match'''
			snake_case__	       =   nn.Parameter(torch.tensor(UpperCAmelCase_							)							)

	snake_case__	       =   weights[5]
	assert len(torch_model_reformer.encoder.layers							) * 4 == len(
	    UpperCAmelCase_							), "HF and trax model do not have the same number of layers"
	for layer_idx, layer in enumerate(torch_model_reformer.encoder.layers							):
		snake_case__	       =   trax_layer_weights[4 * layer_idx : 4 * (layer_idx + 1)]
		set_block_weights_in_torch(UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_							)

	# output layer norm
	snake_case__	       =   np.asarray(weights[7][0]							)
	snake_case__	       =   np.asarray(weights[7][1]							)
	set_param(
	    torch_model_reformer.encoder.layer_norm					,				torch.tensor(UpperCAmelCase_							)					,				torch.tensor(UpperCAmelCase_							)					,				)

	# output embeddings
	snake_case__	       =   np.asarray(weights[9][0]							)
	snake_case__	       =   np.asarray(weights[9][1]							)
	set_param(
	    torch_model.lm_head.decoder					,				torch.tensor(UpperCAmelCase_							).transpose(0					,				1							).contiguous()					,				torch.tensor(UpperCAmelCase_							)					,				)




def 						__lowerCamelCase			(	UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_							)   ->List[Any]:
	# Initialise PyTorch model
	snake_case__	       =   ReformerConfig.from_json_file(UpperCAmelCase_							)
	print(f'''Building PyTorch model from configuration: {config}'''							)
	snake_case__	       =   ReformerModelWithLMHead(UpperCAmelCase_							)

	with open(UpperCAmelCase_					,				'rb'							) as f:
		snake_case__	       =   pickle.load(UpperCAmelCase_							)['weights']

	set_model_weights_in_torch(UpperCAmelCase_					,				UpperCAmelCase_					,				config.hidden_size							)

	# Save pytorch-model
	print(f'''Save PyTorch model to {pytorch_dump_path}'''							)
	torch.save(model.state_dict()					,				UpperCAmelCase_							)


if __name__ == "__main__":
			a__					:			Optional[int]          =     argparse.ArgumentParser()
			# Required parameters
			parser.add_argument(
			    '''--trax_model_pkl_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
			)
			parser.add_argument(
			    '''--config_file''',
			    default=None,
			    type=str,
			    required=True,
			    help=(
			        '''The config json file corresponding to the pre-trained Reformer model. \n'''
			        '''This specifies the model architecture.'''
			    ),
			)
			parser.add_argument(
			    '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
			)
			a__					:			Dict          =     parser.parse_args()
			convert_trax_checkpoint_to_pytorch(args.trax_model_pkl_path, args.config_file, args.pytorch_dump_path)



