import gc
import random
import tempfile
import unittest

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

from diffusers import AutoencoderKL, DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion_safe import StableDiffusionPipelineSafe as StableDiffusionPipeline
from diffusers.utils import floats_tensor, nightly, torch_device
from diffusers.utils.testing_utils import require_torch_gpu


class 			lowerCamelCase_  (     unittest.TestCase				):






    '''simple docstring'''






    def     UpperCamelCase__     (			self) ->			int:
        # clean up the VRAM after each test
        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()







    @property
    def     UpperCamelCase__     (			self) ->			int:
        __UpperCamelCase  :Optional[int]    						=	1
        __UpperCamelCase  :Any    						=	3
        __UpperCamelCase  :Any    						=	(32, 32)

        __UpperCamelCase  :str    						=	floats_tensor((batch_size, num_channels) + sizes				,				rng=random.Random(0)).to(__lowercase)
        return image







    @property
    def     UpperCamelCase__     (			self) ->			Tuple:
        torch.manual_seed(0)
        __UpperCamelCase  :Union[str, Any]    						=	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''')				,				cross_attention_dim=32				,				)
        return model







    @property
    def     UpperCamelCase__     (			self) ->			Optional[Any]:
        torch.manual_seed(0)
        __UpperCamelCase  :str    						=	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				,				)
        return model







    @property
    def     UpperCamelCase__     (			self) ->			List[Any]:
        torch.manual_seed(0)
        __UpperCamelCase  :Optional[int]    						=	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=1_000				,				)
        return CLIPTextModel(__lowercase)







    @property
    def     UpperCamelCase__     (			self) ->			Union[str, Any]:
        def extract(*__lowercase				,				**__lowercase):


            class 			lowerCamelCase_  :






                '''simple docstring'''






                def __init__(			self) ->			Tuple:
                    __UpperCamelCase  :int    						=	torch.ones([0])







                def     UpperCamelCase__     (			self				,				__lowercase) ->			int:
                    self.pixel_values.to(__lowercase)
                    return self
            return Out()

        return extract







    def     UpperCamelCase__     (			self) ->			List[Any]:
        __UpperCamelCase  :Dict    						=	'''cpu'''  # ensure determinism for the device-dependent torch.Generator
        __UpperCamelCase  :Tuple    						=	self.dummy_cond_unet
        __UpperCamelCase  :Union[str, Any]    						=	DDIMScheduler(
            beta_start=0.0_00_85				,				beta_end=0.0_12				,				beta_schedule='''scaled_linear'''				,				clip_sample=__lowercase				,				set_alpha_to_one=__lowercase				,				)

        __UpperCamelCase  :int    						=	self.dummy_vae
        __UpperCamelCase  :Tuple    						=	self.dummy_text_encoder
        __UpperCamelCase  :Dict    						=	CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip''')

        # make sure here that pndm scheduler skips prk
        __UpperCamelCase  :str    						=	StableDiffusionPipeline(
            unet=__lowercase				,				scheduler=__lowercase				,				vae=__lowercase				,				text_encoder=__lowercase				,				tokenizer=__lowercase				,				safety_checker=__lowercase				,				feature_extractor=self.dummy_extractor				,				)
        __UpperCamelCase  :List[Any]    						=	sd_pipe.to(__lowercase)
        sd_pipe.set_progress_bar_config(disable=__lowercase)

        __UpperCamelCase  :str    						=	'''A painting of a squirrel eating a burger'''

        __UpperCamelCase  :Any    						=	torch.Generator(device=__lowercase).manual_seed(0)
        __UpperCamelCase  :List[Any]    						=	sd_pipe([prompt]				,				generator=__lowercase				,				guidance_scale=6.0				,				num_inference_steps=2				,				output_type='''np''')
        __UpperCamelCase  :Any    						=	output.images

        __UpperCamelCase  :str    						=	torch.Generator(device=__lowercase).manual_seed(0)
        __UpperCamelCase  :Union[str, Any]    						=	sd_pipe(
            [prompt]				,				generator=__lowercase				,				guidance_scale=6.0				,				num_inference_steps=2				,				output_type='''np'''				,				return_dict=__lowercase				,				)[0]

        __UpperCamelCase  :List[str]    						=	image[0, -3:, -3:, -1]
        __UpperCamelCase  :Tuple    						=	image_from_tuple[0, -3:, -3:, -1]

        assert image.shape == (1, 64, 64, 3)
        __UpperCamelCase  :Any    						=	np.array([0.57_56, 0.61_18, 0.50_05, 0.50_41, 0.54_71, 0.47_26, 0.49_76, 0.48_65, 0.48_64])

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







    def     UpperCamelCase__     (			self) ->			Optional[int]:
        __UpperCamelCase  :str    						=	'''cpu'''  # ensure determinism for the device-dependent torch.Generator
        __UpperCamelCase  :List[str]    						=	self.dummy_cond_unet
        __UpperCamelCase  :int    						=	PNDMScheduler(skip_prk_steps=__lowercase)
        __UpperCamelCase  :Any    						=	self.dummy_vae
        __UpperCamelCase  :List[Any]    						=	self.dummy_text_encoder
        __UpperCamelCase  :Optional[Any]    						=	CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip''')

        # make sure here that pndm scheduler skips prk
        __UpperCamelCase  :Optional[int]    						=	StableDiffusionPipeline(
            unet=__lowercase				,				scheduler=__lowercase				,				vae=__lowercase				,				text_encoder=__lowercase				,				tokenizer=__lowercase				,				safety_checker=__lowercase				,				feature_extractor=self.dummy_extractor				,				)
        __UpperCamelCase  :int    						=	sd_pipe.to(__lowercase)
        sd_pipe.set_progress_bar_config(disable=__lowercase)

        __UpperCamelCase  :Union[str, Any]    						=	'''A painting of a squirrel eating a burger'''
        __UpperCamelCase  :Union[str, Any]    						=	torch.Generator(device=__lowercase).manual_seed(0)
        __UpperCamelCase  :Optional[int]    						=	sd_pipe([prompt]				,				generator=__lowercase				,				guidance_scale=6.0				,				num_inference_steps=2				,				output_type='''np''')

        __UpperCamelCase  :Optional[Any]    						=	output.images

        __UpperCamelCase  :Any    						=	torch.Generator(device=__lowercase).manual_seed(0)
        __UpperCamelCase  :Any    						=	sd_pipe(
            [prompt]				,				generator=__lowercase				,				guidance_scale=6.0				,				num_inference_steps=2				,				output_type='''np'''				,				return_dict=__lowercase				,				)[0]

        __UpperCamelCase  :List[str]    						=	image[0, -3:, -3:, -1]
        __UpperCamelCase  :Tuple    						=	image_from_tuple[0, -3:, -3:, -1]

        assert image.shape == (1, 64, 64, 3)
        __UpperCamelCase  :str    						=	np.array([0.51_25, 0.57_16, 0.48_28, 0.50_60, 0.56_50, 0.47_68, 0.51_85, 0.48_95, 0.49_93])

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







    def     UpperCamelCase__     (			self) ->			List[Any]:
        __UpperCamelCase  :Dict    						=	StableDiffusionPipeline.from_pretrained(
            '''hf-internal-testing/tiny-stable-diffusion-lms-pipe'''				,				safety_checker=__lowercase)
        assert isinstance(__lowercase				,				__lowercase)
        assert isinstance(pipe.scheduler				,				__lowercase)
        assert pipe.safety_checker is None

        __UpperCamelCase  :List[Any]    						=	pipe('''example prompt'''				,				num_inference_steps=2).images[0]
        assert image is not None

        # check that there's no error when saving a pipeline with one of the models being None
        with tempfile.TemporaryDirectory() as tmpdirname:
            pipe.save_pretrained(__lowercase)
            __UpperCamelCase  :int    						=	StableDiffusionPipeline.from_pretrained(__lowercase)

        # sanity check that the pipeline still works
        assert pipe.safety_checker is None
        __UpperCamelCase  :Optional[Any]    						=	pipe('''example prompt'''				,				num_inference_steps=2).images[0]
        assert image is not None







    @unittest.skipIf(torch_device != '''cuda'''				,				'''This test requires a GPU''')
    def     UpperCamelCase__     (			self) ->			Optional[int]:
        __UpperCamelCase  :Optional[Any]    						=	self.dummy_cond_unet
        __UpperCamelCase  :str    						=	PNDMScheduler(skip_prk_steps=__lowercase)
        __UpperCamelCase  :Optional[Any]    						=	self.dummy_vae
        __UpperCamelCase  :Any    						=	self.dummy_text_encoder
        __UpperCamelCase  :Any    						=	CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip''')

        # put models in fp16
        __UpperCamelCase  :List[str]    						=	unet.half()
        __UpperCamelCase  :Optional[int]    						=	vae.half()
        __UpperCamelCase  :Any    						=	bert.half()

        # make sure here that pndm scheduler skips prk
        __UpperCamelCase  :int    						=	StableDiffusionPipeline(
            unet=__lowercase				,				scheduler=__lowercase				,				vae=__lowercase				,				text_encoder=__lowercase				,				tokenizer=__lowercase				,				safety_checker=__lowercase				,				feature_extractor=self.dummy_extractor				,				)
        __UpperCamelCase  :List[Any]    						=	sd_pipe.to(__lowercase)
        sd_pipe.set_progress_bar_config(disable=__lowercase)

        __UpperCamelCase  :str    						=	'''A painting of a squirrel eating a burger'''
        __UpperCamelCase  :Any    						=	sd_pipe([prompt]				,				num_inference_steps=2				,				output_type='''np''').images

        assert image.shape == (1, 64, 64, 3)


@nightly
@require_torch_gpu
class 			lowerCamelCase_  (     unittest.TestCase				):






    '''simple docstring'''






    def     UpperCamelCase__     (			self) ->			Optional[int]:
        # clean up the VRAM after each test
        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()







    def     UpperCamelCase__     (			self) ->			Optional[int]:
        __UpperCamelCase  :List[Any]    						=	StableDiffusionPipeline.from_pretrained('''runwayml/stable-diffusion-v1-5'''				,				safety_checker=__lowercase)
        __UpperCamelCase  :Tuple    						=	LMSDiscreteScheduler.from_config(sd_pipe.scheduler.config)
        __UpperCamelCase  :Optional[int]    						=	sd_pipe.to(__lowercase)
        sd_pipe.set_progress_bar_config(disable=__lowercase)

        __UpperCamelCase  :int    						=	(
            '''portrait of girl with smokey eyes makeup in abandoned hotel, grange clothes, redshift, wide high angle'''
            ''' coloured polaroid photograph with flash, kodak film, hyper real, stunning moody cinematography, with'''
            ''' anamorphic lenses, by maripol, fallen angels by wong kar - wai, style of suspiria and neon demon and'''
            ''' children from bahnhof zoo, detailed '''
        )
        __UpperCamelCase  :str    						=	4_003_660_346
        __UpperCamelCase  :Union[str, Any]    						=	7

        # without safety guidance (sld_guidance_scale = 0)
        __UpperCamelCase  :Dict    						=	torch.manual_seed(__lowercase)
        __UpperCamelCase  :Any    						=	sd_pipe(
            [prompt]				,				generator=__lowercase				,				guidance_scale=__lowercase				,				num_inference_steps=50				,				output_type='''np'''				,				width=512				,				height=512				,				sld_guidance_scale=0				,				)

        __UpperCamelCase  :List[Any]    						=	output.images
        __UpperCamelCase  :Tuple    						=	image[0, -3:, -3:, -1]
        __UpperCamelCase  :Dict    						=	[0.22_78, 0.22_31, 0.22_49, 0.23_33, 0.23_03, 0.18_85, 0.22_73, 0.21_44, 0.21_76]

        assert image.shape == (1, 512, 512, 3)

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

        # without safety guidance (strong configuration)
        __UpperCamelCase  :Union[str, Any]    						=	torch.manual_seed(__lowercase)
        __UpperCamelCase  :Optional[int]    						=	sd_pipe(
            [prompt]				,				generator=__lowercase				,				guidance_scale=__lowercase				,				num_inference_steps=50				,				output_type='''np'''				,				width=512				,				height=512				,				sld_guidance_scale=2_000				,				sld_warmup_steps=7				,				sld_threshold=0.0_25				,				sld_momentum_scale=0.5				,				sld_mom_beta=0.7				,				)

        __UpperCamelCase  :Tuple    						=	output.images
        __UpperCamelCase  :Union[str, Any]    						=	image[0, -3:, -3:, -1]
        __UpperCamelCase  :Dict    						=	[0.23_83, 0.22_76, 0.2_36, 0.21_92, 0.21_86, 0.20_53, 0.19_71, 0.19_01, 0.17_19]

        assert image.shape == (1, 512, 512, 3)

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







    def     UpperCamelCase__     (			self) ->			Tuple:
        __UpperCamelCase  :Tuple    						=	StableDiffusionPipeline.from_pretrained('''runwayml/stable-diffusion-v1-5'''				,				safety_checker=__lowercase)
        __UpperCamelCase  :Optional[Any]    						=	LMSDiscreteScheduler.from_config(sd_pipe.scheduler.config)
        __UpperCamelCase  :List[Any]    						=	sd_pipe.to(__lowercase)
        sd_pipe.set_progress_bar_config(disable=__lowercase)

        __UpperCamelCase  :List[Any]    						=	'''padme amidala taking a bath artwork, safe for work, no nudity'''
        __UpperCamelCase  :Any    						=	2_734_971_755
        __UpperCamelCase  :Optional[int]    						=	7

        __UpperCamelCase  :Dict    						=	torch.manual_seed(__lowercase)
        __UpperCamelCase  :Optional[int]    						=	sd_pipe(
            [prompt]				,				generator=__lowercase				,				guidance_scale=__lowercase				,				num_inference_steps=50				,				output_type='''np'''				,				width=512				,				height=512				,				sld_guidance_scale=0				,				)

        __UpperCamelCase  :Dict    						=	output.images
        __UpperCamelCase  :Union[str, Any]    						=	image[0, -3:, -3:, -1]
        __UpperCamelCase  :str    						=	[0.35_02, 0.36_22, 0.33_96, 0.36_42, 0.34_78, 0.33_18, 0.35, 0.33_48, 0.32_97]

        assert image.shape == (1, 512, 512, 3)

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

        __UpperCamelCase  :Union[str, Any]    						=	torch.manual_seed(__lowercase)
        __UpperCamelCase  :str    						=	sd_pipe(
            [prompt]				,				generator=__lowercase				,				guidance_scale=__lowercase				,				num_inference_steps=50				,				output_type='''np'''				,				width=512				,				height=512				,				sld_guidance_scale=2_000				,				sld_warmup_steps=7				,				sld_threshold=0.0_25				,				sld_momentum_scale=0.5				,				sld_mom_beta=0.7				,				)

        __UpperCamelCase  :Optional[int]    						=	output.images
        __UpperCamelCase  :Tuple    						=	image[0, -3:, -3:, -1]
        __UpperCamelCase  :Dict    						=	[0.55_31, 0.52_06, 0.48_95, 0.51_56, 0.51_82, 0.47_51, 0.48_02, 0.48_03, 0.44_43]

        assert image.shape == (1, 512, 512, 3)

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







    def     UpperCamelCase__     (			self) ->			Union[str, Any]:
        __UpperCamelCase  :Optional[Any]    						=	StableDiffusionPipeline.from_pretrained('''runwayml/stable-diffusion-v1-5''')
        __UpperCamelCase  :List[str]    						=	sd_pipe.to(__lowercase)
        sd_pipe.set_progress_bar_config(disable=__lowercase)

        __UpperCamelCase  :int    						=	(
            '''the four horsewomen of the apocalypse, painting by tom of finland, gaston bussiere, craig mullins, j. c.'''
            ''' leyendecker'''
        )
        __UpperCamelCase  :List[str]    						=	1_044_355_234
        __UpperCamelCase  :List[Any]    						=	12

        __UpperCamelCase  :str    						=	torch.manual_seed(__lowercase)
        __UpperCamelCase  :Optional[Any]    						=	sd_pipe(
            [prompt]				,				generator=__lowercase				,				guidance_scale=__lowercase				,				num_inference_steps=50				,				output_type='''np'''				,				width=512				,				height=512				,				sld_guidance_scale=0				,				)

        __UpperCamelCase  :Optional[int]    						=	output.images
        __UpperCamelCase  :Dict    						=	image[0, -3:, -3:, -1]
        __UpperCamelCase  :List[Any]    						=	np.array([0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0])

        assert image.shape == (1, 512, 512, 3)

        assert np.abs(image_slice.flatten() - expected_slice).max() < 1E-7

        __UpperCamelCase  :Any    						=	torch.manual_seed(__lowercase)
        __UpperCamelCase  :int    						=	sd_pipe(
            [prompt]				,				generator=__lowercase				,				guidance_scale=__lowercase				,				num_inference_steps=50				,				output_type='''np'''				,				width=512				,				height=512				,				sld_guidance_scale=2_000				,				sld_warmup_steps=7				,				sld_threshold=0.0_25				,				sld_momentum_scale=0.5				,				sld_mom_beta=0.7				,				)

        __UpperCamelCase  :List[Any]    						=	output.images
        __UpperCamelCase  :List[str]    						=	image[0, -3:, -3:, -1]
        __UpperCamelCase  :Optional[Any]    						=	np.array([0.58_18, 0.62_85, 0.68_35, 0.60_19, 0.6_25, 0.67_54, 0.60_96, 0.63_34, 0.65_61])
        assert image.shape == (1, 512, 512, 3)

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






import argparse
import collections

import torch
from flax import traverse_util
from tax import checkpoints

from transformers import TaConfig, TaEncoderModel, TaForConditionalGeneration
from transformers.utils import logging


logging.set_verbosity_info()



def lowerCamelCase						(     SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE="attention"			):
    '''simple docstring'''







    __UpperCamelCase  :int    						=	params[f"""{prefix}/layers_{i}/{layer_name}/key/kernel"""]
    __UpperCamelCase  :Optional[int]    						=	params[f"""{prefix}/layers_{i}/{layer_name}/out/kernel"""]
    __UpperCamelCase  :List[str]    						=	params[f"""{prefix}/layers_{i}/{layer_name}/query/kernel"""]
    __UpperCamelCase  :Union[str, Any]    						=	params[f"""{prefix}/layers_{i}/{layer_name}/value/kernel"""]
    return k, o, q, v



def lowerCamelCase						(     SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE=False			):
    '''simple docstring'''







    if split_mlp_wi:
        __UpperCamelCase  :List[str]    						=	params[f"""{prefix}/layers_{i}/mlp/wi_0/kernel"""]
        __UpperCamelCase  :Dict    						=	params[f"""{prefix}/layers_{i}/mlp/wi_1/kernel"""]
        __UpperCamelCase  :Optional[Any]    						=	(wi_a, wi_a)
    else:
        __UpperCamelCase  :List[str]    						=	params[f"""{prefix}/layers_{i}/mlp/wi/kernel"""]

    __UpperCamelCase  :int    						=	params[f"""{prefix}/layers_{i}/mlp/wo/kernel"""]
    return wi, wo



def lowerCamelCase						(     SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE			):
    '''simple docstring'''







    return params[f"""{prefix}/layers_{i}/{layer_name}/scale"""]



def lowerCamelCase						(     SCREAMING_SNAKE_CASE						,    *, SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE			):
    '''simple docstring'''







    __UpperCamelCase  :Tuple    						=	traverse_util.flatten_dict(variables['''target''']			)
    __UpperCamelCase  :Optional[int]    						=	{'''/'''.join(SCREAMING_SNAKE_CASE			): v for k, v in old.items()}

    # v1.1 models have a gated GeLU with wi_0 and wi_1 instead of wi
    __UpperCamelCase  :Union[str, Any]    						=	'''encoder/layers_0/mlp/wi_0/kernel''' in old
    print('''Split MLP:'''						,    SCREAMING_SNAKE_CASE			)

    __UpperCamelCase  :Optional[Any]    						=	collections.OrderedDict()

    # Shared embeddings.
    __UpperCamelCase  :Any    						=	old['''token_embedder/embedding''']

    # Encoder.
    for i in range(SCREAMING_SNAKE_CASE			):
        # Block i, layer 0 (Self Attention).
        __UpperCamelCase  :Tuple    						=	tax_layer_norm_lookup(SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    '''encoder'''						,    '''pre_attention_layer_norm'''			)
        __UpperCamelCase						, __UpperCamelCase						, __UpperCamelCase						, __UpperCamelCase  :Dict    						=	tax_attention_lookup(SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    '''encoder'''						,    '''attention'''			)
        __UpperCamelCase  :str    						=	layer_norm
        __UpperCamelCase  :str    						=	k.T
        __UpperCamelCase  :Optional[Any]    						=	o.T
        __UpperCamelCase  :Optional[int]    						=	q.T
        __UpperCamelCase  :Any    						=	v.T

        # Block i, layer 1 (MLP).
        __UpperCamelCase  :Union[str, Any]    						=	tax_layer_norm_lookup(SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    '''encoder'''						,    '''pre_mlp_layer_norm'''			)
        __UpperCamelCase						, __UpperCamelCase  :Optional[int]    						=	tax_mlp_lookup(SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    '''encoder'''						,    SCREAMING_SNAKE_CASE			)
        __UpperCamelCase  :Any    						=	layer_norm
        if split_mlp_wi:
            __UpperCamelCase  :Optional[Any]    						=	wi[0].T
            __UpperCamelCase  :int    						=	wi[1].T
        else:
            __UpperCamelCase  :Dict    						=	wi.T
        __UpperCamelCase  :str    						=	wo.T

    __UpperCamelCase  :Optional[Any]    						=	old[
        '''encoder/relpos_bias/rel_embedding'''
    ].T
    __UpperCamelCase  :Optional[int]    						=	old['''encoder/encoder_norm/scale''']

    if not is_encoder_only:
        # Decoder.
        for i in range(SCREAMING_SNAKE_CASE			):
            # Block i, layer 0 (Self Attention).
            __UpperCamelCase  :int    						=	tax_layer_norm_lookup(SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    '''decoder'''						,    '''pre_self_attention_layer_norm'''			)
            __UpperCamelCase						, __UpperCamelCase						, __UpperCamelCase						, __UpperCamelCase  :Dict    						=	tax_attention_lookup(SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    '''decoder'''						,    '''self_attention'''			)
            __UpperCamelCase  :str    						=	layer_norm
            __UpperCamelCase  :Optional[Any]    						=	k.T
            __UpperCamelCase  :List[str]    						=	o.T
            __UpperCamelCase  :Union[str, Any]    						=	q.T
            __UpperCamelCase  :int    						=	v.T

            # Block i, layer 1 (Cross Attention).
            __UpperCamelCase  :Optional[Any]    						=	tax_layer_norm_lookup(SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    '''decoder'''						,    '''pre_cross_attention_layer_norm'''			)
            __UpperCamelCase						, __UpperCamelCase						, __UpperCamelCase						, __UpperCamelCase  :int    						=	tax_attention_lookup(SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    '''decoder'''						,    '''encoder_decoder_attention'''			)
            __UpperCamelCase  :List[str]    						=	layer_norm
            __UpperCamelCase  :Any    						=	k.T
            __UpperCamelCase  :Any    						=	o.T
            __UpperCamelCase  :Optional[int]    						=	q.T
            __UpperCamelCase  :Union[str, Any]    						=	v.T

            # Block i, layer 2 (MLP).
            __UpperCamelCase  :Tuple    						=	tax_layer_norm_lookup(SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    '''decoder'''						,    '''pre_mlp_layer_norm'''			)
            __UpperCamelCase						, __UpperCamelCase  :Optional[Any]    						=	tax_mlp_lookup(SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    '''decoder'''						,    SCREAMING_SNAKE_CASE			)
            __UpperCamelCase  :Optional[Any]    						=	layer_norm
            if split_mlp_wi:
                __UpperCamelCase  :int    						=	wi[0].T
                __UpperCamelCase  :Any    						=	wi[1].T
            else:
                __UpperCamelCase  :Optional[Any]    						=	wi.T
            __UpperCamelCase  :Dict    						=	wo.T

        __UpperCamelCase  :Optional[int]    						=	old['''decoder/decoder_norm/scale''']
        __UpperCamelCase  :Optional[Any]    						=	old[
            '''decoder/relpos_bias/rel_embedding'''
        ].T

        # LM Head (only in v1.1 checkpoints, in v1.0 embeddings are used instead)
        if "decoder/logits_dense/kernel" in old:
            __UpperCamelCase  :List[str]    						=	old['''decoder/logits_dense/kernel'''].T

    return new



def lowerCamelCase						(     SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE			):
    '''simple docstring'''







    __UpperCamelCase  :Optional[Any]    						=	collections.OrderedDict([(k, torch.from_numpy(v.copy()			)) for (k, v) in converted_params.items()]			)

    # Add what is missing.
    if "encoder.embed_tokens.weight" not in state_dict:
        __UpperCamelCase  :Union[str, Any]    						=	state_dict['''shared.weight''']

    if not is_encoder_only:
        if "decoder.embed_tokens.weight" not in state_dict:
            __UpperCamelCase  :Any    						=	state_dict['''shared.weight''']

        if "lm_head.weight" not in state_dict:  # For old 1.0 models.
            print('''Using shared word embeddings as lm_head.'''			)
            __UpperCamelCase  :Optional[int]    						=	state_dict['''shared.weight''']

    return state_dict



def lowerCamelCase						(     SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE			):
    '''simple docstring'''







    __UpperCamelCase  :int    						=	checkpoints.load_tax_checkpoint(SCREAMING_SNAKE_CASE			)
    __UpperCamelCase  :str    						=	convert_tax_to_pytorch(SCREAMING_SNAKE_CASE						,    num_layers=config.num_layers						,    is_encoder_only=SCREAMING_SNAKE_CASE			)
    __UpperCamelCase  :Optional[int]    						=	make_state_dict(SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE			)
    model.load_state_dict(SCREAMING_SNAKE_CASE						,    strict=SCREAMING_SNAKE_CASE			)



def lowerCamelCase						(     SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE = False			):
    '''simple docstring'''







    __UpperCamelCase  :Union[str, Any]    						=	TaConfig.from_json_file(SCREAMING_SNAKE_CASE			)
    print(f"""Building PyTorch model from configuration: {config}"""			)
    # Non-v1.1 checkpoints could also use T5Model, but this works for all.
    # The v1.0 checkpoints will simply have an LM head that is the word embeddings.
    if is_encoder_only:
        __UpperCamelCase  :Dict    						=	TaEncoderModel(SCREAMING_SNAKE_CASE			)
    else:
        __UpperCamelCase  :str    						=	TaForConditionalGeneration(SCREAMING_SNAKE_CASE			)

    # Load weights from tf checkpoint
    load_tax_weights_in_ta(SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE			)

    # Save pytorch-model
    print(f"""Save PyTorch model to {pytorch_dump_path}"""			)
    model.save_pretrained(SCREAMING_SNAKE_CASE			)

    # Verify that we can load the checkpoint.
    model.from_pretrained(SCREAMING_SNAKE_CASE			)
    print('''Done'''			)


if __name__ == "__main__":
    __lowercase											=      argparse.ArgumentParser(description='''Converts a native T5X checkpoint into a PyTorch checkpoint.''')
    # Required parameters
    parser.add_argument(
        '''--t5x_checkpoint_path''', default=None, type=str, required=True, help='''Path to the T5X checkpoint.'''
    )
    parser.add_argument(
        '''--config_file''',
        default=None,
        type=str,
        required=True,
        help='''The config json file corresponding to the pre-trained T5 model.\nThis specifies the model architecture.''',
    )
    parser.add_argument(
        '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
    )
    parser.add_argument(
        '''--is_encoder_only''', action='''store_true''', help='''Check if the model is encoder-decoder model''', default=False
    )
    __lowercase											=      parser.parse_args()
    convert_tax_checkpoint_to_pytorch(
        args.tax_checkpoint_path, args.config_file, args.pytorch_dump_path, args.is_encoder_only
    )





