

import argparse
import re

from flax.traverse_util import flatten_dict, unflatten_dict
from tax import checkpoints

from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration
from transformers.modeling_flax_pytorch_utils import load_flax_weights_in_pytorch_model
from transformers.utils import logging


logging.set_verbosity_info()


# should not include what is already done by the `from_pt` argument
A									=  {
    '/attention/': '/0/SelfAttention/',
    '/self_attention/': '/0/SelfAttention/',
    '/encoder_decoder_attention/': '/1/EncDecAttention/',
    'value': 'v',
    'query': 'q',
    'key': 'k',
    'out': 'o',
    'pre_self_attention_layer_norm': '0/layer_norm',
    'pre_cross_attention_layer_norm': '1/layer_norm',
    'pre_attention_layer_norm': '0/layer_norm',  # previously 1, but seems wrong
    'token_embedder': 'shared',
    'encoder_norm': 'final_layer_norm',
    'decoder_norm': 'final_layer_norm',
    'relpos_bias/rel_embedding': 'block/0/layer/0/SelfAttention/relative_attention_bias/weight',
    'router/router_weights/w/': 'router/classifier/',
    'roer/roer_weights/w/': 'router/classifier/',
    'logits_dense': 'lm_head',
}





def a(lowercase__						):





	'''simple docstring'''
	# 1. in HF T5, we have block.{x}.layer.{y}. which corresponds to layer.{x} in
	# the original model
	snake_case_   		=			list(s_dict.keys()						)
	for key in keys:
		snake_case_   		=			R'.*/layers_(\d+)'
		snake_case_   		=			key
		if re.match(lowercase__      ,			lowercase__						):
			snake_case_   		=			re.sub(R'layers_(\d+)'      ,			R'block/\1/layer'      ,			lowercase__						)

		snake_case_   		=			R'(encoder|decoder)\/'

		if re.match(lowercase__      ,			lowercase__						):
			snake_case_   		=			re.match(lowercase__      ,			lowercase__						).groups()
			if groups[0] == "encoder":
				snake_case_   		=			re.sub(R'/mlp/'      ,			R'/1/mlp/'      ,			lowercase__						)
				snake_case_   		=			re.sub(R'/pre_mlp_layer_norm/'      ,			R'/1/layer_norm/'      ,			lowercase__						)

			elif groups[0] == "decoder":
				snake_case_   		=			re.sub(R'/mlp/'      ,			R'/2/mlp/'      ,			lowercase__						)
				snake_case_   		=			re.sub(R'/pre_mlp_layer_norm/'      ,			R'/2/layer_norm/'      ,			lowercase__						)

        # 2. Convert other classic mappings
		for old_key, temp_key in MOE_LAYER_NAME_MAPPING.items():
			if old_key in new_key:
				snake_case_   		=			new_key.replace(lowercase__      ,			lowercase__						)

		print(f"""{key} -> {new_key}"""						)
		snake_case_   		=			s_dict.pop(lowercase__						)

	if "encoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight" in s_dict:
		snake_case_   		=			s_dict[
		    'encoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight'
		].T
	if "decoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight" in s_dict:
		snake_case_   		=			s_dict[
		    'decoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight'
		].T

	# 3. Take extra care of the EXPERTS layer
	for key in list(s_dict.keys()						):
		if "expert" in key:
			snake_case_   		=			s_dict[key].shape[0]
			snake_case_   		=			s_dict[key]
			for idx in range(lowercase__						):
				snake_case_   		=			expert_weihts[idx]
				print(f"""{key} -> {key.replace('expert/'      ,			'nested fstring'						)}"""						)

			s_dict.pop(lowercase__						)

	return s_dict


A									=  {
    'NUM_ENCODER_LAYERS': 'num_layers',
    'NUM_DECODER_LAYERS': 'num_decoder_layers',
    'NUM_HEADS': 'num_heads',
    'HEAD_DIM': 'd_kv',
    'EMBED_DIM': 'd_model',
    'MLP_DIM': 'd_ff',
    'NUM_SELECTED_EXPERTS': 'num_selected_experts',
    'NUM_ENCODER_SPARSE_LAYERS': 'num_sparse_encoder_layers',
    'NUM_DECODER_SPARSE_LAYERS': 'num_sparse_decoder_layers',
    'dense.MlpBlock.activations': 'feed_forward_proj',
}





def a(lowercase__      ,			lowercase__						):





	'''simple docstring'''
	# Convert a google style config to the hugging face fromat
	import regex as re

	with open(lowercase__      ,			'r'						) as f:
		snake_case_   		=			f.read()

	snake_case_   		=			re.findall(R'(.*) = ([0-9.]*)'      ,			lowercase__						)
	snake_case_   		=			{}
	for param, value in regex_match:
		if param in GIN_TO_CONFIG_MAPPING and value != "":
			snake_case_   		=			float(lowercase__						) if '.' in value else int(lowercase__						)

	snake_case_   		=			re.findall(R'(.*activations) = \(\'(.*)\',\)'      ,			lowercase__						)[0]
	snake_case_   		=			str(activation[1]						)

	snake_case_   		=			num_experts
	snake_case_   		=			SwitchTransformersConfig(**lowercase__						)
	return config





def a(lowercase__      ,			lowercase__      ,			lowercase__=None      ,			lowercase__="./"      ,			lowercase__=8						):





	'''simple docstring'''
	# Initialise PyTorch model

	print(f"""Loading flax weights from : {flax_checkpoint_path}"""						)
	snake_case_   		=			checkpoints.load_tax_checkpoint(lowercase__						)

	if gin_file is not None:
		snake_case_   		=			convert_gin_to_config(lowercase__      ,			lowercase__						)
	else:
		snake_case_   		=			SwitchTransformersConfig.from_pretrained(lowercase__						)

	snake_case_   		=			SwitchTransformersForConditionalGeneration(lowercase__						)

	snake_case_   		=			flax_params['target']
	snake_case_   		=			flatten_dict(lowercase__      ,			sep='/'						)
	snake_case_   		=			rename_keys(lowercase__						)
	snake_case_   		=			unflatten_dict(lowercase__      ,			sep='/'						)

	# Load the flax params in the PT model
	load_flax_weights_in_pytorch_model(lowercase__      ,			lowercase__						)

	print(f"""Save PyTorch model to {pytorch_dump_path}"""						)
	pt_model.save_pretrained(lowercase__						)


if __name__ == "__main__":
		A									=  argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    '--switch_t5x_checkpoint_path',
		    default=None,
		    type=str,
		    required=True,
		    help=(
		        'The config json file corresponding to the pre-trained SwitchTransformers model. \nThis specifies the'
		        ' model architecture. If not provided, a `gin_file` has to be provided.'
		    ),
		)
		parser.add_argument(
		    '--gin_file',
		    default=None,
		    type=str,
		    required=False,
		    help='Path to the gin config file. If not provided, a `config_file` has to be passed   ',
		)
		parser.add_argument(
		    '--config_name', default=None, type=str, required=False, help='Config name of SwitchTransformers model.'
		)
		parser.add_argument(
		    '--pytorch_dump_folder_path', default=None, type=str, required=True, help='Path to the output pytorch model.'
		)
		parser.add_argument('--num_experts', default=8, type=int, required=False, help='Number of experts')
		A									=  parser.parse_args()
		convert_flax_checkpoint_to_pytorch(
		    args.switch_tax_checkpoint_path,
		    args.config_name,
		    args.gin_file,
		    args.pytorch_dump_folder_path,
		    args.num_experts,
		)



import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast

from diffusers import DDIMScheduler, KandinskyImgaImgPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()


class SCREAMING_SNAKE_CASE	(		__snake_case				,    unittest.TestCase				):






			"""simple docstring"""




			__A    	=	KandinskyImgaImgPipeline
			__A    	=	["""prompt""", """image_embeds""", """negative_image_embeds""", """image"""]
			__A    	=	[
			    """prompt""",
			    """negative_prompt""",
			    """image_embeds""",
			    """negative_image_embeds""",
			    """image""",
			]
			__A    	=	[
			    """generator""",
			    """height""",
			    """width""",
			    """strength""",
			    """guidance_scale""",
			    """negative_prompt""",
			    """num_inference_steps""",
			    """return_dict""",
			    """guidance_scale""",
			    """num_images_per_prompt""",
			    """output_type""",
			    """return_dict""",
			]
			__A    	=	False


			@property
			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				return 32


			@property
			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				return 32


			@property
			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				return self.time_input_dim


			@property
			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				return self.time_input_dim * 4


			@property
			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				return 1_00


			@property
			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				snake_case_   		=			XLMRobertaTokenizerFast.from_pretrained('YiYiXu/tiny-random-mclip-base'				)
				return tokenizer


			@property
			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				torch.manual_seed(0				)
				snake_case_   		=			MCLIPConfig(
				    numDims=self.cross_attention_dim			,					transformerDimensions=self.text_embedder_hidden_size			,					hidden_size=self.text_embedder_hidden_size			,					intermediate_size=37			,					num_attention_heads=4			,					num_hidden_layers=5			,					vocab_size=10_05			,					)

				snake_case_   		=			MultilingualCLIP(__UpperCamelCase				)
				snake_case_   		=			text_encoder.eval()

				return text_encoder


			@property
			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				torch.manual_seed(0				)

				snake_case_   		=			{
				    'in_channels': 4,
				    # Out channels is double in channels because predicts mean and variance
				    'out_channels': 8,
				    'addition_embed_type': 'text_image',
				    'down_block_types': ('ResnetDownsampleBlock2D', 'SimpleCrossAttnDownBlock2D'),
				    'up_block_types': ('SimpleCrossAttnUpBlock2D', 'ResnetUpsampleBlock2D'),
				    'mid_block_type': 'UNetMidBlock2DSimpleCrossAttn',
				    'block_out_channels': (self.block_out_channels_a, self.block_out_channels_a * 2),
				    'layers_per_block': 1,
				    'encoder_hid_dim': self.text_embedder_hidden_size,
				    'encoder_hid_dim_type': 'text_image_proj',
				    'cross_attention_dim': self.cross_attention_dim,
				    'attention_head_dim': 4,
				    'resnet_time_scale_shift': 'scale_shift',
				    'class_embed_type': None,
				}

				snake_case_   		=			UNetaDConditionModel(**__UpperCamelCase				)
				return model


			@property
			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				return {
				    "block_out_channels": [32, 64],
				    "down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
				    "in_channels": 3,
				    "latent_channels": 4,
				    "layers_per_block": 1,
				    "norm_num_groups": 8,
				    "norm_type": "spatial",
				    "num_vq_embeddings": 12,
				    "out_channels": 3,
				    "up_block_types": [
				        "AttnUpDecoderBlock2D",
				        "UpDecoderBlock2D",
				    ],
				    "vq_embed_dim": 4,
				}


			@property
			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				torch.manual_seed(0				)
				snake_case_   		=			VQModel(**self.dummy_movq_kwargs				)
				return model


			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				snake_case_   		=			self.dummy_text_encoder
				snake_case_   		=			self.dummy_tokenizer
				snake_case_   		=			self.dummy_unet
				snake_case_   		=			self.dummy_movq

				snake_case_   		=			{
				    'num_train_timesteps': 10_00,
				    'beta_schedule': 'linear',
				    'beta_start': 0.0_0085,
				    'beta_end': 0.012,
				    'clip_sample': False,
				    'set_alpha_to_one': False,
				    'steps_offset': 0,
				    'prediction_type': 'epsilon',
				    'thresholding': False,
				}

				snake_case_   		=			DDIMScheduler(**__UpperCamelCase				)

				snake_case_   		=			{
				    'text_encoder': text_encoder,
				    'tokenizer': tokenizer,
				    'unet': unet,
				    'scheduler': scheduler,
				    'movq': movq,
				}

				return components


			def       __lowerCAmelCase			(     self			,					__UpperCamelCase			,					__UpperCamelCase=0				):

				"""simple docstring"""


				snake_case_   		=			floats_tensor((1, self.cross_attention_dim)			,					rng=random.Random(__UpperCamelCase				)				).to(__UpperCamelCase				)
				snake_case_   		=			floats_tensor((1, self.cross_attention_dim)			,					rng=random.Random(seed + 1				)				).to(__UpperCamelCase				)
				# create init_image
				snake_case_   		=			floats_tensor((1, 3, 64, 64)			,					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'				).resize((2_56, 2_56)				)

				if str(__UpperCamelCase				).startswith('mps'				):
					snake_case_   		=			torch.manual_seed(__UpperCamelCase				)
				else:
					snake_case_   		=			torch.Generator(device=__UpperCamelCase				).manual_seed(__UpperCamelCase				)
				snake_case_   		=			{
				    'prompt': 'horse',
				    'image': init_image,
				    'image_embeds': image_embeds,
				    'negative_image_embeds': negative_image_embeds,
				    'generator': generator,
				    'height': 64,
				    'width': 64,
				    'num_inference_steps': 10,
				    'guidance_scale': 7.0,
				    'strength': 0.2,
				    'output_type': 'np',
				}
				return inputs







			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				snake_case_   		=			'cpu'

				snake_case_   		=			self.get_dummy_components()

				snake_case_   		=			self.pipeline_class(**__UpperCamelCase				)
				snake_case_   		=			pipe.to(__UpperCamelCase				)

				pipe.set_progress_bar_config(disable=__UpperCamelCase				)

				snake_case_   		=			pipe(**self.get_dummy_inputs(__UpperCamelCase				)				)
				snake_case_   		=			output.images

				snake_case_   		=			pipe(
				    **self.get_dummy_inputs(__UpperCamelCase				)			,					return_dict=__UpperCamelCase			,					)[0]

				snake_case_   		=			image[0, -3:, -3:, -1]
				snake_case_   		=			image_from_tuple[0, -3:, -3:, -1]

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

				snake_case_   		=			np.array(
				    [0.6147_4943, 0.607_3539, 0.4330_8544, 0.592_8269, 0.4749_3595, 0.4675_5973, 0.461_3838, 0.4536_8797, 0.5011_9233]				)
				assert (
				    np.abs(image_slice.flatten() - expected_slice				).max() < 1E-2
				), f""" expected_slice {expected_slice}, but got {image_slice.flatten()}"""
				assert (
				    np.abs(image_from_tuple_slice.flatten() - expected_slice				).max() < 1E-2
				), f""" expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}"""







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






			"""simple docstring"""




			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


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







			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				snake_case_   		=			load_numpy(
				    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
				    '/kandinsky/kandinsky_img2img_frog.npy'				)

				snake_case_   		=			load_image(
				    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main' '/kandinsky/cat.png'				)
				snake_case_   		=			'A red cartoon frog, 4k'

				snake_case_   		=			KandinskyPriorPipeline.from_pretrained(
				    'kandinsky-community/kandinsky-2-1-prior'			,					torch_dtype=torch.floataa				)
				pipe_prior.to(__UpperCamelCase				)

				snake_case_   		=			KandinskyImgaImgPipeline.from_pretrained(
				    'kandinsky-community/kandinsky-2-1'			,					torch_dtype=torch.floataa				)
				snake_case_   		=			pipeline.to(__UpperCamelCase				)

				pipeline.set_progress_bar_config(disable=__UpperCamelCase				)

				snake_case_   		=			torch.Generator(device='cpu'				).manual_seed(0				)
				snake_case_		,							snake_case_   		=			pipe_prior(
				    __UpperCamelCase			,					generator=__UpperCamelCase			,					num_inference_steps=5			,					negative_prompt=''			,					).to_tuple()

				snake_case_   		=			pipeline(
				    __UpperCamelCase			,					image=__UpperCamelCase			,					image_embeds=__UpperCamelCase			,					negative_image_embeds=__UpperCamelCase			,					generator=__UpperCamelCase			,					num_inference_steps=1_00			,					height=7_68			,					width=7_68			,					strength=0.2			,					output_type='np'			,					)

				snake_case_   		=			output.images[0]

				assert image.shape == (7_68, 7_68, 3)

				assert_mean_pixel_difference(__UpperCamelCase			,					__UpperCamelCase				)
