






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__      :

						def  A_				(			self				)					->      Optional[Any]:


								'''simple docstring'''

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

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

								torch.manual_seed(0				)
								_UpperCamelCase						    =	UNetaDConditionModel(
								    sample_size=32       ,							layers_per_block=1       ,							block_out_channels=[32, 64]       ,							down_block_types=[
								        """ResnetDownsampleBlock2D""",
								        """SimpleCrossAttnDownBlock2D""",
								    ]       ,							mid_block_type="""UNetMidBlock2DSimpleCrossAttn"""       ,							up_block_types=["""SimpleCrossAttnUpBlock2D""", """ResnetUpsampleBlock2D"""]       ,							in_channels=3       ,							out_channels=6       ,							cross_attention_dim=32       ,							encoder_hid_dim=32       ,							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				)
								_UpperCamelCase						    =	DDPMScheduler(
								    num_train_timesteps=10_00       ,							beta_schedule="""squaredcos_cap_v2"""       ,							beta_start=0.0001       ,							beta_end=0.02       ,							thresholding=a       ,							dynamic_thresholding_ratio=0.95       ,							sample_max_value=1.0       ,							prediction_type="""epsilon"""       ,							variance_type="""learned_range"""       ,							)

								torch.manual_seed(0				)
								_UpperCamelCase						    =	IFWatermarker()

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

						def  A_				(			self				)					->      int:


								'''simple docstring'''

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

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

								torch.manual_seed(0				)
								_UpperCamelCase						    =	UNetaDConditionModel(
								    sample_size=32       ,							layers_per_block=[1, 2]       ,							block_out_channels=[32, 64]       ,							down_block_types=[
								        """ResnetDownsampleBlock2D""",
								        """SimpleCrossAttnDownBlock2D""",
								    ]       ,							mid_block_type="""UNetMidBlock2DSimpleCrossAttn"""       ,							up_block_types=["""SimpleCrossAttnUpBlock2D""", """ResnetUpsampleBlock2D"""]       ,							in_channels=6       ,							out_channels=6       ,							cross_attention_dim=32       ,							encoder_hid_dim=32       ,							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=1.414       ,							time_embedding_act_fn="""gelu"""       ,							time_embedding_dim=32       ,							)
								unet.set_attn_processor(AttnAddedKVProcessor()				)  # For reproducibility tests

								torch.manual_seed(0				)
								_UpperCamelCase						    =	DDPMScheduler(
								    num_train_timesteps=10_00       ,							beta_schedule="""squaredcos_cap_v2"""       ,							beta_start=0.0001       ,							beta_end=0.02       ,							thresholding=a       ,							dynamic_thresholding_ratio=0.95       ,							sample_max_value=1.0       ,							prediction_type="""epsilon"""       ,							variance_type="""learned_range"""       ,							)

								torch.manual_seed(0				)
								_UpperCamelCase						    =	DDPMScheduler(
								    num_train_timesteps=10_00       ,							beta_schedule="""squaredcos_cap_v2"""       ,							beta_start=0.0001       ,							beta_end=0.02       ,							)

								torch.manual_seed(0				)
								_UpperCamelCase						    =	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  A_				(			self				)					->      Dict:


								'''simple docstring'''

								_UpperCamelCase						    =	self.get_dummy_components()
								_UpperCamelCase						    =	self.pipeline_class(**a				)
								pipe.to(a				)
								pipe.set_progress_bar_config(disable=a				)

								_UpperCamelCase						    =	self.get_dummy_inputs(a				)

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

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

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

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

								_UpperCamelCase    ,	_UpperCamelCase						    =	pipe.encode_prompt(a				)

								# inputs with prompt converted to embeddings
								_UpperCamelCase						    =	{
								    """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:
										_UpperCamelCase						    =	image

								if mask_image is not None:
										_UpperCamelCase						    =	mask_image

								if original_image is not None:
										_UpperCamelCase						    =	original_image

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

								_UpperCamelCase						    =	pipe(**a				)[0]

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

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

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

								_UpperCamelCase						    =	self.get_dummy_inputs(a				)

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

								# inputs with prompt converted to embeddings
								_UpperCamelCase						    =	{
								    """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:
										_UpperCamelCase						    =	image

								if mask_image is not None:
										_UpperCamelCase						    =	mask_image

								if original_image is not None:
										_UpperCamelCase						    =	original_image

								_UpperCamelCase						    =	pipe_loaded(**a				)[0]

								_UpperCamelCase						    =	np.abs(to_np(a				) - to_np(a				)				).max()
								self.assertLess(a       ,							1e-4				)







						def  A_				(			self				)					->      List[Any]:


								'''simple docstring'''

								_UpperCamelCase						    =	self.get_dummy_components()
								_UpperCamelCase						    =	self.pipeline_class(**a				)
								pipe.to(a				)
								pipe.set_progress_bar_config(disable=a				)

								_UpperCamelCase						    =	self.get_dummy_inputs(a				)
								_UpperCamelCase						    =	pipe(**a				)[0]

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

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

								_UpperCamelCase						    =	self.get_dummy_inputs(a				)
								_UpperCamelCase						    =	pipe_loaded(**a				)[0]

								_UpperCamelCase						    =	np.abs(to_np(a				) - to_np(a				)				).max()
								self.assertLess(a       ,							1e-4				)





import argparse
from argparse import Namespace

import torch
from torch import nn

from transformers import XGLMConfig, XGLMForCausalLM


def 							__A(lowerCAmelCase )		->				Any:

		"""simple docstring"""






		_UpperCamelCase						    =	[
		    """decoder.version""",
		    """decoder.output_projection.weight""",
		    """_float_tensor""",
		    """decoder.embed_positions._float_tensor""",
		]
		for k in ignore_keys:
				state_dict.pop(lowerCAmelCase						,  lowerCAmelCase )


def 							__A(lowerCAmelCase )		->				Optional[int]:

		"""simple docstring"""






		_UpperCamelCase    ,	_UpperCamelCase						    =	emb.weight.shape
		_UpperCamelCase						    =	nn.Linear(lowerCAmelCase						,  lowerCAmelCase						,  bias=lowerCAmelCase )
		_UpperCamelCase						    =	emb.weight.data
		return lin_layer


def 							__A(lowerCAmelCase )		->				Tuple:

		"""simple docstring"""






		_UpperCamelCase						    =	torch.load(lowerCAmelCase						,  map_location="""cpu""" )
		_UpperCamelCase						    =	Namespace(**checkpoint["""cfg"""]["""model"""] )
		_UpperCamelCase						    =	checkpoint["""model"""]
		remove_ignore_keys_(lowerCAmelCase )
		_UpperCamelCase						    =	state_dict["""decoder.embed_tokens.weight"""].shape[0]

		_UpperCamelCase						    =	{key.replace("""decoder"""						,  """model""" ): val for key, val in state_dict.items()}

		_UpperCamelCase						    =	XGLMConfig(
		    vocab_size=lowerCAmelCase						,  max_position_embeddings=args.max_target_positions						,  num_layers=args.decoder_layers						,  attention_heads=args.decoder_attention_heads						,  ffn_dim=args.decoder_ffn_embed_dim						,  d_model=args.decoder_embed_dim						,  layerdrop=args.decoder_layerdrop						,  dropout=args.dropout						,  attention_dropout=args.attention_dropout						,  activation_dropout=args.activation_dropout						,  activation_function="""gelu"""						,  scale_embedding=not args.no_scale_embedding						,  tie_word_embeddings=args.share_decoder_input_output_embed						,  )

		_UpperCamelCase						    =	XGLMForCausalLM(lowerCAmelCase )
		_UpperCamelCase						    =	model.load_state_dict(lowerCAmelCase						,  strict=lowerCAmelCase )
		print(lowerCAmelCase )
		_UpperCamelCase						    =	make_linear_from_emb(model.model.embed_tokens )

		return model


if __name__ == "__main__":
		lowerCamelCase__	=							argparse.ArgumentParser()
		# Required parameters
		parser.add_argument("fairseq_path", type=str, help="path to a model.pt on local filesystem.")
		parser.add_argument("pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
		lowerCamelCase__	=							parser.parse_args()
		lowerCamelCase__	=							convert_fairseq_xglm_checkpoint_from_disk(args.fairseq_path)
		model.save_pretrained(args.pytorch_dump_folder_path)



