






import tempfile

import numpy as np
import torch
from transformers import AutoTokenizer, TaEncoderModel

from diffusers import DDPMScheduler, UNetaDConditionModel
from diffusers.models.attention_processor import AttnAddedKVProcessor
from diffusers.pipelines.deepfloyd_if import IFWatermarker
from diffusers.utils.testing_utils import torch_device

from ..test_pipelines_common import to_np


class    _lowerCAmelCase			:



				"""simple docstring"""



				def 					__magic_name__							(      self    :    Optional[Any]):






											'''simple docstring'''







											torch.manual_seed(0)
											snake_case__	  =       TaEncoderModel.from_pretrained("""hf-internal-testing/tiny-random-t5""")

											torch.manual_seed(0)
											snake_case__	  =       AutoTokenizer.from_pretrained("""hf-internal-testing/tiny-random-t5""")

											torch.manual_seed(0)
											snake_case__	  =       UNetaDConditionModel(
											    sample_size=3_2			,		layers_per_block=1			,		block_out_channels=[3_2, 6_4]			,		down_block_types=[
											        """ResnetDownsampleBlock2D""",
											        """SimpleCrossAttnDownBlock2D""",
											    ]			,		mid_block_type="""UNetMidBlock2DSimpleCrossAttn"""			,		up_block_types=["""SimpleCrossAttnUpBlock2D""", """ResnetUpsampleBlock2D"""]			,		in_channels=3			,		out_channels=6			,		cross_attention_dim=3_2			,		encoder_hid_dim=3_2			,		attention_head_dim=8			,		addition_embed_type="""text"""			,		addition_embed_type_num_heads=2			,		cross_attention_norm="""group_norm"""			,		resnet_time_scale_shift="""scale_shift"""			,		act_fn="""gelu"""			,		)
											unet.set_attn_processor(AttnAddedKVProcessor())  # For reproducibility tests

											torch.manual_seed(0)
											snake_case__	  =       DDPMScheduler(
											    num_train_timesteps=1_0_0_0			,		beta_schedule="""squaredcos_cap_v2"""			,		beta_start=0.00_01			,		beta_end=0.02			,		thresholding=UpperCamelCase__			,		dynamic_thresholding_ratio=0.95			,		sample_max_value=1.0			,		prediction_type="""epsilon"""			,		variance_type="""learned_range"""			,		)

											torch.manual_seed(0)
											snake_case__	  =       IFWatermarker()

											return {
											    "text_encoder": text_encoder,
											    "tokenizer": tokenizer,
											    "unet": unet,
											    "scheduler": scheduler,
											    "watermarker": watermarker,
											    "safety_checker": None,
											    "feature_extractor": None,
											}



				def 					__magic_name__							(      self    :    Optional[Any]):






											'''simple docstring'''







											torch.manual_seed(0)
											snake_case__	  =       TaEncoderModel.from_pretrained("""hf-internal-testing/tiny-random-t5""")

											torch.manual_seed(0)
											snake_case__	  =       AutoTokenizer.from_pretrained("""hf-internal-testing/tiny-random-t5""")

											torch.manual_seed(0)
											snake_case__	  =       UNetaDConditionModel(
											    sample_size=3_2			,		layers_per_block=[1, 2]			,		block_out_channels=[3_2, 6_4]			,		down_block_types=[
											        """ResnetDownsampleBlock2D""",
											        """SimpleCrossAttnDownBlock2D""",
											    ]			,		mid_block_type="""UNetMidBlock2DSimpleCrossAttn"""			,		up_block_types=["""SimpleCrossAttnUpBlock2D""", """ResnetUpsampleBlock2D"""]			,		in_channels=6			,		out_channels=6			,		cross_attention_dim=3_2			,		encoder_hid_dim=3_2			,		attention_head_dim=8			,		addition_embed_type="""text"""			,		addition_embed_type_num_heads=2			,		cross_attention_norm="""group_norm"""			,		resnet_time_scale_shift="""scale_shift"""			,		act_fn="""gelu"""			,		class_embed_type="""timestep"""			,		mid_block_scale_factor=1.4_14			,		time_embedding_act_fn="""gelu"""			,		time_embedding_dim=3_2			,		)
											unet.set_attn_processor(AttnAddedKVProcessor())  # For reproducibility tests

											torch.manual_seed(0)
											snake_case__	  =       DDPMScheduler(
											    num_train_timesteps=1_0_0_0			,		beta_schedule="""squaredcos_cap_v2"""			,		beta_start=0.00_01			,		beta_end=0.02			,		thresholding=UpperCamelCase__			,		dynamic_thresholding_ratio=0.95			,		sample_max_value=1.0			,		prediction_type="""epsilon"""			,		variance_type="""learned_range"""			,		)

											torch.manual_seed(0)
											snake_case__	  =       DDPMScheduler(
											    num_train_timesteps=1_0_0_0			,		beta_schedule="""squaredcos_cap_v2"""			,		beta_start=0.00_01			,		beta_end=0.02			,		)

											torch.manual_seed(0)
											snake_case__	  =       IFWatermarker()

											return {
											    "text_encoder": text_encoder,
											    "tokenizer": tokenizer,
											    "unet": unet,
											    "scheduler": scheduler,
											    "image_noising_scheduler": image_noising_scheduler,
											    "watermarker": watermarker,
											    "safety_checker": None,
											    "feature_extractor": None,
											}



				def 					__magic_name__							(      self    :    Dict):






											'''simple docstring'''







											snake_case__	  =       self.get_dummy_components()
											snake_case__	  =       self.pipeline_class(**UpperCamelCase__)
											pipe.to(UpperCamelCase__)
											pipe.set_progress_bar_config(disable=UpperCamelCase__)

											snake_case__	  =       self.get_dummy_inputs(UpperCamelCase__)

											snake_case__	  =       inputs["""prompt"""]
											snake_case__	  =       inputs["""generator"""]
											snake_case__	  =       inputs["""num_inference_steps"""]
											snake_case__	  =       inputs["""output_type"""]

											if "image" in inputs:
																		snake_case__	  =       inputs["""image"""]
											else:
																		snake_case__	  =       None

											if "mask_image" in inputs:
																		snake_case__	  =       inputs["""mask_image"""]
											else:
																		snake_case__	  =       None

											if "original_image" in inputs:
																		snake_case__	  =       inputs["""original_image"""]
											else:
																		snake_case__	  =       None

											snake_case__  , snake_case__	  =       pipe.encode_prompt(UpperCamelCase__)

											# inputs with prompt converted to embeddings
											snake_case__	  =       {
											    """prompt_embeds""": prompt_embeds,
											    """negative_prompt_embeds""": negative_prompt_embeds,
											    """generator""": generator,
											    """num_inference_steps""": num_inference_steps,
											    """output_type""": output_type,
											}

											if image is not None:
																		snake_case__	  =       image

											if mask_image is not None:
																		snake_case__	  =       mask_image

											if original_image is not None:
																		snake_case__	  =       original_image

											# set all optional components to None
											for optional_component in pipe._optional_components:
																		setattr(UpperCamelCase__			,		UpperCamelCase__			,		UpperCamelCase__)

											snake_case__	  =       pipe(**UpperCamelCase__)[0]

											with tempfile.TemporaryDirectory() as tmpdir:
																		pipe.save_pretrained(UpperCamelCase__)
																		snake_case__	  =       self.pipeline_class.from_pretrained(UpperCamelCase__)
																		pipe_loaded.to(UpperCamelCase__)
																		pipe_loaded.set_progress_bar_config(disable=UpperCamelCase__)

											pipe_loaded.unet.set_attn_processor(AttnAddedKVProcessor())  # For reproducibility tests

											for optional_component in pipe._optional_components:
																		self.assertTrue(
																		    getattr(UpperCamelCase__			,		UpperCamelCase__) is None			,		F'''`{optional_component}` did not stay set to None after loading.'''			,		)

											snake_case__	  =       self.get_dummy_inputs(UpperCamelCase__)

											snake_case__	  =       inputs["""generator"""]
											snake_case__	  =       inputs["""num_inference_steps"""]
											snake_case__	  =       inputs["""output_type"""]

											# inputs with prompt converted to embeddings
											snake_case__	  =       {
											    """prompt_embeds""": prompt_embeds,
											    """negative_prompt_embeds""": negative_prompt_embeds,
											    """generator""": generator,
											    """num_inference_steps""": num_inference_steps,
											    """output_type""": output_type,
											}

											if image is not None:
																		snake_case__	  =       image

											if mask_image is not None:
																		snake_case__	  =       mask_image

											if original_image is not None:
																		snake_case__	  =       original_image

											snake_case__	  =       pipe_loaded(**UpperCamelCase__)[0]

											snake_case__	  =       np.abs(to_np(UpperCamelCase__) - to_np(UpperCamelCase__)).max()
											self.assertLess(UpperCamelCase__			,		1E-4)



				def 					__magic_name__							(      self    :    Optional[Any]):






											'''simple docstring'''







											snake_case__	  =       self.get_dummy_components()
											snake_case__	  =       self.pipeline_class(**UpperCamelCase__)
											pipe.to(UpperCamelCase__)
											pipe.set_progress_bar_config(disable=UpperCamelCase__)

											snake_case__	  =       self.get_dummy_inputs(UpperCamelCase__)
											snake_case__	  =       pipe(**UpperCamelCase__)[0]

											with tempfile.TemporaryDirectory() as tmpdir:
																		pipe.save_pretrained(UpperCamelCase__)
																		snake_case__	  =       self.pipeline_class.from_pretrained(UpperCamelCase__)
																		pipe_loaded.to(UpperCamelCase__)
																		pipe_loaded.set_progress_bar_config(disable=UpperCamelCase__)

											pipe_loaded.unet.set_attn_processor(AttnAddedKVProcessor())  # For reproducibility tests

											snake_case__	  =       self.get_dummy_inputs(UpperCamelCase__)
											snake_case__	  =       pipe_loaded(**UpperCamelCase__)[0]

											snake_case__	  =       np.abs(to_np(UpperCamelCase__) - to_np(UpperCamelCase__)).max()
											self.assertLess(UpperCamelCase__			,		1E-4)








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 _UpperCAmelCase   (	a						:	Optional[Any]  ,					a						:	Dict  ,					a						:	Union[str, Any]=None ):
							# set parameter of one layer
							assert torch_layer.weight.shape == weight.shape, F'''{torch_layer} layer.weight does not match'''
							snake_case__	  =       nn.Parameter(a )
							if bias is not None:
														assert torch_layer.bias.shape == bias.shape, F'''{torch_layer} layer.bias does not match'''
														snake_case__	  =       nn.Parameter(a )

def _UpperCAmelCase   (	a						:	List[Any]  ,					a						:	List[str]  ,					a						:	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(a ).transpose(1  ,					2 ).contiguous().view(-1  ,					a )  ,					)
							set_param(
							    torch_layer.self_attention.value  ,					torch.tensor(a ).transpose(1  ,					2 ).contiguous().view(-1  ,					a )  ,					)
							set_param(
							    torch_layer.output.dense  ,					torch.tensor(a ).view(-1  ,					a ).contiguous().transpose(0  ,					1 )  ,					)

def _UpperCAmelCase   (	a						:	Optional[int]  ,					a						:	Dict  ,					a						:	Optional[int] ):
							# 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(a ).transpose(1  ,					2 ).contiguous().view(-1  ,					a )  ,					)
							set_param(
							    torch_layer.self_attention.key  ,					torch.tensor(a ).transpose(1  ,					2 ).contiguous().view(-1  ,					a )  ,					)
							set_param(
							    torch_layer.self_attention.value  ,					torch.tensor(a ).transpose(1  ,					2 ).contiguous().view(-1  ,					a )  ,					)
							set_param(
							    torch_layer.output.dense  ,					torch.tensor(a ).view(-1  ,					a ).contiguous().transpose(0  ,					1 )  ,					)

def _UpperCAmelCase   (	a						:	List[Any]  ,					a						:	Tuple  ,					a						:	List[Any] ):
							# 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(a )  ,					torch.tensor(a )  ,					)

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

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

							# Chunked Feed Forward
							if len(a ) == 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(a )  ,					torch.tensor(a )  ,					)

							# 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(a ).transpose(0  ,					1 ).contiguous()  ,					torch.tensor(a )  ,					)

							# 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(a ).transpose(0  ,					1 ).contiguous()  ,					torch.tensor(a )  ,					)

def _UpperCAmelCase   (	a						:	List[Any]  ,					a						:	Dict  ,					a						:	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(a )  ,					)

							if isinstance(weights[3]  ,					a ):
														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(a ) )

							snake_case__	  =       weights[5]
							assert len(torch_model_reformer.encoder.layers ) * 4 == len(
							    a ), "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(a  ,					a  ,					a )

							# 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(a )  ,					torch.tensor(a )  ,					)

							# 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(a ).transpose(0  ,					1 ).contiguous()  ,					torch.tensor(a )  ,					)

def _UpperCAmelCase   (	a						:	Any  ,					a						:	str  ,					a						:	str ):
							# Initialise PyTorch model
							snake_case__	  =       ReformerConfig.from_json_file(a )
							print(F'''Building PyTorch model from configuration: {config}''' )
							snake_case__	  =       ReformerModelWithLMHead(a )

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

							set_model_weights_in_torch(a  ,					a  ,					config.hidden_size )

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


if __name__ == "__main__":
	a__       =     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__       =     parser.parse_args()
	convert_trax_checkpoint_to_pytorch(args.trax_model_pkl_path, args.config_file, args.pytorch_dump_path)
