


"""simple docstring"""




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 			__UpperCAmelCase       (  self      ):
				torch.manual_seed(0      )
				__a           =			TaEncoderModel.from_pretrained('''hf-internal-testing/tiny-random-t5'''      )

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

				torch.manual_seed(0      )
				__a           =			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      )
				__a           =			DDPMScheduler(
				    num_train_timesteps=1_000							, 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      )
				__a           =			IFWatermarker()

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

		def 			__UpperCAmelCase       (  self      ):
				torch.manual_seed(0      )
				__a           =			TaEncoderModel.from_pretrained('''hf-internal-testing/tiny-random-t5'''      )

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

				torch.manual_seed(0      )
				__a           =			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      )
				__a           =			DDPMScheduler(
				    num_train_timesteps=1_000							, 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      )
				__a           =			DDPMScheduler(
				    num_train_timesteps=1_000							, beta_schedule='''squaredcos_cap_v2'''							, beta_start=0.0001							, beta_end=0.02							, )

				torch.manual_seed(0      )
				__a           =			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 			__UpperCAmelCase       (  self      ):
				__a           =			self.get_dummy_components()
				__a           =			self.pipeline_class(**_a      )
				pipe.to(_a      )
				pipe.set_progress_bar_config(disable=_a      )

				__a           =			self.get_dummy_inputs(_a      )

				__a           =			inputs['''prompt''']
				__a           =			inputs['''generator''']
				__a           =			inputs['''num_inference_steps''']
				__a           =			inputs['''output_type''']

				if "image" in inputs:
						__a           =			inputs['''image''']
				else:
						__a           =			None

				if "mask_image" in inputs:
						__a           =			inputs['''mask_image''']
				else:
						__a           =			None

				if "original_image" in inputs:
						__a           =			inputs['''original_image''']
				else:
						__a           =			None

				__a      ,     __a           =			pipe.encode_prompt(_a      )

				# inputs with prompt converted to embeddings
				__a           =			{
				    '''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:
						__a           =			image

				if mask_image is not None:
						__a           =			mask_image

				if original_image is not None:
						__a           =			original_image

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

				__a           =			pipe(**_a      )[0]

				with tempfile.TemporaryDirectory() as tmpdir:
						pipe.save_pretrained(_a      )
						__a           =			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.'''							, )

				__a           =			self.get_dummy_inputs(_a      )

				__a           =			inputs['''generator''']
				__a           =			inputs['''num_inference_steps''']
				__a           =			inputs['''output_type''']

				# inputs with prompt converted to embeddings
				__a           =			{
				    '''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:
						__a           =			image

				if mask_image is not None:
						__a           =			mask_image

				if original_image is not None:
						__a           =			original_image

				__a           =			pipe_loaded(**_a      )[0]

				__a           =			np.abs(to_np(_a      ) - to_np(_a      )      ).max()
				self.assertLess(_a							, 1E-4      )





		def 			__UpperCAmelCase       (  self      ):
				__a           =			self.get_dummy_components()
				__a           =			self.pipeline_class(**_a      )
				pipe.to(_a      )
				pipe.set_progress_bar_config(disable=_a      )

				__a           =			self.get_dummy_inputs(_a      )
				__a           =			pipe(**_a      )[0]

				with tempfile.TemporaryDirectory() as tmpdir:
						pipe.save_pretrained(_a      )
						__a           =			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

				__a           =			self.get_dummy_inputs(_a      )
				__a           =			pipe_loaded(**_a      )[0]

				__a           =			np.abs(to_np(_a      ) - to_np(_a      )      ).max()
				self.assertLess(_a							, 1E-4      )












"""simple docstring"""




import argparse
from pathlib import Path

import torch

from transformers import OPTConfig, OPTModel
from transformers.utils import logging


logging.set_verbosity_info()
lowerCAmelCase							:			Dict     							=							logging.get_logger(__name__)



def     a__ (				snake_case__				)						->					Dict:
			lowerCamelCase			      =					torch.load(snake_case__       , map_location="""cpu"""				)
			if "model" in sd.keys():
						lowerCamelCase			      =					torch.load(snake_case__       , map_location="""cpu"""				)["""model"""]

			# pop unnecessary weights
			lowerCamelCase			      =					[
			    """decoder.version""",
			    """decoder.output_projection.weight""",
			]
			for key in keys_to_delete:
						if key in sd:
									sd.pop(snake_case__				)

			lowerCamelCase			      =					{
			    """decoder.project_in_dim.weight""": """decoder.project_in.weight""",
			    """decoder.project_out_dim.weight""": """decoder.project_out.weight""",
			    """decoder.layer_norm.weight""": """decoder.final_layer_norm.weight""",
			    """decoder.layer_norm.bias""": """decoder.final_layer_norm.bias""",
			}
			for old_key, new_key in keys_to_rename.items():
						if old_key in sd:
									lowerCamelCase			      =					sd.pop(snake_case__				)

			lowerCamelCase			      =					list(sd.keys()				)
			for key in keys:
						if ".qkv_proj." in key:
									lowerCamelCase			      =					sd[key]
									# We split QKV in separate Q,K,V

									lowerCamelCase			      =					key.replace(""".qkv_proj."""       , """.q_proj."""				)
									lowerCamelCase			      =					key.replace(""".qkv_proj."""       , """.k_proj."""				)
									lowerCamelCase			      =					key.replace(""".qkv_proj."""       , """.v_proj."""				)

									lowerCamelCase			      =					value.shape[0]
									assert depth % 3 == 0
									# `SequeuceParallelTransformerBlock` has QKV weight is separated in K,V,Q despite the naming:
									# https://cs.github.com/facebookresearch/metaseq/blob/51871bd73cd04c038f239ea2a26db1d7f6b37927/metaseq/modules/sequence_parallel_transformer_layer.py#L97
									lowerCamelCase      ,   lowerCamelCase      ,   lowerCamelCase			      =					torch.split(snake_case__       , depth // 3       , dim=0				)

									lowerCamelCase			      =					q
									lowerCamelCase			      =					k
									lowerCamelCase			      =					v
									del sd[key]

			return sd



@torch.no_grad()
def     a__ (				snake_case__       , snake_case__       , snake_case__=None				)						->					Tuple:
			lowerCamelCase			      =					load_checkpoint(snake_case__				)

			if config is not None:
						lowerCamelCase			      =					OPTConfig.from_pretrained(snake_case__				)
			else:
						lowerCamelCase			      =					OPTConfig()

			lowerCamelCase			      =					OPTModel(snake_case__				).half().eval()
			model.load_state_dict(snake_case__				)

			# Check results
			Path(snake_case__				).mkdir(exist_ok=snake_case__				)
			model.save_pretrained(snake_case__				)


if __name__ == "__main__":
					lowerCAmelCase							:			List[str]     							=							argparse.ArgumentParser()
					# Required parameters
					parser.add_argument(
					    """--fairseq_path""",
					    type=str,
					    help=(
					        """path to fairseq checkpoint in correct format. You can find all checkpoints in the correct format here:"""
					        """ https://huggingface.co/models?other=opt_metasq"""
					    ),
					)
					parser.add_argument("""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
					parser.add_argument("""--hf_config""", default=None, type=str, help="""Define HF config.""")
					lowerCAmelCase							:			Optional[Any]     							=							parser.parse_args()
					convert_opt_checkpoint(args.fairseq_path, args.pytorch_dump_folder_path, config=args.hf_config)



