

"""simple docstring"""




import glob
import os
from typing import Dict, List, Union

import torch

from diffusers.utils import is_safetensors_available


if is_safetensors_available():
	import safetensors.torch

from huggingface_hub import snapshot_download

from diffusers import DiffusionPipeline, __version__
from diffusers.schedulers.scheduling_utils import SCHEDULER_CONFIG_NAME
from diffusers.utils import CONFIG_NAME, DIFFUSERS_CACHE, ONNX_WEIGHTS_NAME, WEIGHTS_NAME





class 			__UpperCamelCase (   _A						):


		def __init__(self :		List[str]):
							self.register_to_config()
							super().__init__()


		def 					SCREAMING_SNAKE_CASE__   (self :		str     ,     __SCREAMING_SNAKE_CASE :		Union[str, Any]     ,     __SCREAMING_SNAKE_CASE :		str):
							if dicta == dicta:
												return True
							else:
												A			,    A			=					self._remove_meta_keys(__SCREAMING_SNAKE_CASE)
												A			,    A			=					self._remove_meta_keys(__SCREAMING_SNAKE_CASE)
												if configa == configa:
																	print(F"""Warning !: Mismatch in keys {meta_keysa} and {meta_keysa}.""")
																	return True
							return False


		def 					SCREAMING_SNAKE_CASE__   (self :		List[str]     ,     __SCREAMING_SNAKE_CASE :		Dict):
							A			=					[]
							A			=					config_dict.copy()
							for key in config_dict.keys():
												if key.startswith("_"):
																	temp_dict.pop(__SCREAMING_SNAKE_CASE)
																	meta_keys.append(__SCREAMING_SNAKE_CASE)
							return (temp_dict, meta_keys)


		@torch.no_grad()
		def 					SCREAMING_SNAKE_CASE__   (self :		List[str]     ,     __SCREAMING_SNAKE_CASE :		List[Union[str, os.PathLike]]     ,     **__SCREAMING_SNAKE_CASE :		Tuple):
							A			=					kwargs.pop("cache_dir"     ,     __SCREAMING_SNAKE_CASE)
							A			=					kwargs.pop("resume_download"     ,     __SCREAMING_SNAKE_CASE)
							A			=					kwargs.pop("force_download"     ,     __SCREAMING_SNAKE_CASE)
							A			=					kwargs.pop("proxies"     ,     __SCREAMING_SNAKE_CASE)
							A			=					kwargs.pop("local_files_only"     ,     __SCREAMING_SNAKE_CASE)
							A			=					kwargs.pop("use_auth_token"     ,     __SCREAMING_SNAKE_CASE)
							A			=					kwargs.pop("revision"     ,     __SCREAMING_SNAKE_CASE)
							A			=					kwargs.pop("torch_dtype"     ,     __SCREAMING_SNAKE_CASE)
							A			=					kwargs.pop("device_map"     ,     __SCREAMING_SNAKE_CASE)

							A			=					kwargs.pop("alpha"     ,     0.5)
							A			=					kwargs.pop("interp"     ,     __SCREAMING_SNAKE_CASE)

							print("Received list"     ,     __SCREAMING_SNAKE_CASE)
							print(F"""Combining with alpha={alpha}, interpolation mode={interp}""")

							A			=					len(__SCREAMING_SNAKE_CASE)
							# Ignore result from model_index_json comparision of the two checkpoints
							A			=					kwargs.pop("force"     ,     __SCREAMING_SNAKE_CASE)

							# If less than 2 checkpoints, nothing to merge. If more than 3, not supported for now.
							if checkpoint_count > 3 or checkpoint_count < 2:
												raise ValueError(
												    "Received incorrect number of checkpoints to merge. Ensure that either 2 or 3 checkpoints are being"
												    " passed.")

							print("Received the right number of checkpoints")
							# chkpt0, chkpt1 = pretrained_model_name_or_path_list[0:2]
							# chkpt2 = pretrained_model_name_or_path_list[2] if checkpoint_count == 3 else None

							# Validate that the checkpoints can be merged
							# Step 1: Load the model config and compare the checkpoints. We'll compare the model_index.json first while ignoring the keys starting with '_'
							A			=					[]
							for pretrained_model_name_or_path in pretrained_model_name_or_path_list:
												A			=					DiffusionPipeline.load_config(
												    __SCREAMING_SNAKE_CASE     ,     cache_dir=__SCREAMING_SNAKE_CASE     ,     resume_download=__SCREAMING_SNAKE_CASE     ,     force_download=__SCREAMING_SNAKE_CASE     ,     proxies=__SCREAMING_SNAKE_CASE     ,     local_files_only=__SCREAMING_SNAKE_CASE     ,     use_auth_token=__SCREAMING_SNAKE_CASE     ,     revision=__SCREAMING_SNAKE_CASE     ,     )
												config_dicts.append(__SCREAMING_SNAKE_CASE)

							A			=					True
							for idx in range(1     ,     len(__SCREAMING_SNAKE_CASE)):
												comparison_result &= self._compare_model_configs(config_dicts[idx - 1]     ,     config_dicts[idx])
												if not force and comparison_result is False:
																	raise ValueError("Incompatible checkpoints. Please check model_index.json for the models.")
																	print(config_dicts[0]     ,     config_dicts[1])
							print("Compatible model_index.json files found")
							# Step 2: Basic Validation has succeeded. Let's download the models and save them into our local files.
							A			=					[]
							for pretrained_model_name_or_path, config_dict in zip(__SCREAMING_SNAKE_CASE     ,     __SCREAMING_SNAKE_CASE):
												A			=					[k for k in config_dict.keys() if not k.startswith("_")]
												A			=					[os.path.join(__SCREAMING_SNAKE_CASE     ,     "*") for k in folder_names]
												allow_patterns += [
												    WEIGHTS_NAME,
												    SCHEDULER_CONFIG_NAME,
												    CONFIG_NAME,
												    ONNX_WEIGHTS_NAME,
												    DiffusionPipeline.config_name,
												]
												A			=					config_dict.get("_class_name")
												A			=					{"diffusers": __version__, "pipeline_class": requested_pipeline_class}

												A			=					(
												    pretrained_model_name_or_path
												    if os.path.isdir(__SCREAMING_SNAKE_CASE)
												    else snapshot_download(
												        __SCREAMING_SNAKE_CASE     ,     cache_dir=__SCREAMING_SNAKE_CASE     ,     resume_download=__SCREAMING_SNAKE_CASE     ,     proxies=__SCREAMING_SNAKE_CASE     ,     local_files_only=__SCREAMING_SNAKE_CASE     ,     use_auth_token=__SCREAMING_SNAKE_CASE     ,     revision=__SCREAMING_SNAKE_CASE     ,     allow_patterns=__SCREAMING_SNAKE_CASE     ,     user_agent=__SCREAMING_SNAKE_CASE     ,     )
												)
												print("Cached Folder"     ,     __SCREAMING_SNAKE_CASE)
												cached_folders.append(__SCREAMING_SNAKE_CASE)

							# Step 3:-
							# Load the first checkpoint as a diffusion pipeline and modify its module state_dict in place
							A			=					DiffusionPipeline.from_pretrained(
							    cached_folders[0]     ,     torch_dtype=__SCREAMING_SNAKE_CASE     ,     device_map=__SCREAMING_SNAKE_CASE)
							final_pipe.to(self.device)

							A			=					None
							if len(__SCREAMING_SNAKE_CASE) > 2:
												A			=					os.path.join(cached_folders[2])

							if interp == "sigmoid":
												A			=					CheckpointMergerPipeline.sigmoid
							elif interp == "inv_sigmoid":
												A			=					CheckpointMergerPipeline.inv_sigmoid
							elif interp == "add_diff":
												A			=					CheckpointMergerPipeline.add_difference
							else:
												A			=					CheckpointMergerPipeline.weighted_sum

							# Find each module's state dict.
							for attr in final_pipe.config.keys():
												if not attr.startswith("_"):
																	A			=					os.path.join(cached_folders[1]     ,     __SCREAMING_SNAKE_CASE)
																	if os.path.exists(__SCREAMING_SNAKE_CASE):
																						A			=					[
																						    *glob.glob(os.path.join(__SCREAMING_SNAKE_CASE     ,     "*.safetensors")),
																						    *glob.glob(os.path.join(__SCREAMING_SNAKE_CASE     ,     "*.bin")),
																						]
																						A			=					files[0] if len(__SCREAMING_SNAKE_CASE) > 0 else None
																	if len(__SCREAMING_SNAKE_CASE) < 3:
																						A			=					None
																	else:
																						A			=					os.path.join(cached_folders[2]     ,     __SCREAMING_SNAKE_CASE)
																						if os.path.exists(__SCREAMING_SNAKE_CASE):
																											A			=					[
																											    *glob.glob(os.path.join(__SCREAMING_SNAKE_CASE     ,     "*.safetensors")),
																											    *glob.glob(os.path.join(__SCREAMING_SNAKE_CASE     ,     "*.bin")),
																											]
																											A			=					files[0] if len(__SCREAMING_SNAKE_CASE) > 0 else None
                # For an attr if both checkpoint_path_1 and 2 are None, ignore.
                # If atleast one is present, deal with it according to interp method, of course only if the state_dict keys match.
																	if checkpoint_path_a is None and checkpoint_path_a is None:
																						print(F"""Skipping {attr}: not present in 2nd or 3d model""")
																						continue
																	try:
																						A			=					getattr(__SCREAMING_SNAKE_CASE     ,     __SCREAMING_SNAKE_CASE)
																						if isinstance(__SCREAMING_SNAKE_CASE     ,     __SCREAMING_SNAKE_CASE):  # ignore requires_safety_checker boolean
																											continue
																						A			=					getattr(__SCREAMING_SNAKE_CASE     ,     "state_dict")
																						A			=					theta_a()

																						A			=					getattr(__SCREAMING_SNAKE_CASE     ,     "load_state_dict")
																						A			=					(
																						    safetensors.torch.load_file(__SCREAMING_SNAKE_CASE)
																						    if (is_safetensors_available() and checkpoint_path_a.endswith(".safetensors"))
																						    else torch.load(__SCREAMING_SNAKE_CASE     ,     map_location="cpu")
																						)
																						A			=					None
																						if checkpoint_path_a:
																											A			=					(
																											    safetensors.torch.load_file(__SCREAMING_SNAKE_CASE)
																											    if (is_safetensors_available() and checkpoint_path_a.endswith(".safetensors"))
																											    else torch.load(__SCREAMING_SNAKE_CASE     ,     map_location="cpu")
																											)

																						if not theta_a.keys() == theta_a.keys():
																											print(F"""Skipping {attr}: key mismatch""")
																											continue
																						if theta_a and not theta_a.keys() == theta_a.keys():
																											print(F"""Skipping {attr}:y mismatch""")
																	except Exception as e:
																						print(F"""Skipping {attr} do to an unexpected error: {str(__SCREAMING_SNAKE_CASE)}""")
																						continue
																	print(F"""MERGING {attr}""")

																	for key in theta_a.keys():
																						if theta_a:
																											A			=					theta_func(theta_a[key]     ,     theta_a[key]     ,     theta_a[key]     ,     __SCREAMING_SNAKE_CASE)
																						else:
																											A			=					theta_func(theta_a[key]     ,     theta_a[key]     ,     __SCREAMING_SNAKE_CASE     ,     __SCREAMING_SNAKE_CASE)

																	del theta_a
																	del theta_a
																	update_theta_a(__SCREAMING_SNAKE_CASE)

																	del theta_a
							return final_pipe


		@staticmethod
		def 					SCREAMING_SNAKE_CASE__   (__SCREAMING_SNAKE_CASE :		Optional[int]     ,     __SCREAMING_SNAKE_CASE :		List[str]     ,     __SCREAMING_SNAKE_CASE :		Tuple     ,     __SCREAMING_SNAKE_CASE :		Tuple):
							return ((1 - alpha) * thetaa) + (alpha * thetaa)


		@staticmethod
		def 					SCREAMING_SNAKE_CASE__   (__SCREAMING_SNAKE_CASE :		Any     ,     __SCREAMING_SNAKE_CASE :		Tuple     ,     __SCREAMING_SNAKE_CASE :		Union[str, Any]     ,     __SCREAMING_SNAKE_CASE :		Dict):
							A			=					alpha * alpha * (3 - (2 * alpha))
							return thetaa + ((thetaa - thetaa) * alpha)


		@staticmethod
		def 					SCREAMING_SNAKE_CASE__   (__SCREAMING_SNAKE_CASE :		Optional[Any]     ,     __SCREAMING_SNAKE_CASE :		List[Any]     ,     __SCREAMING_SNAKE_CASE :		Dict     ,     __SCREAMING_SNAKE_CASE :		List[str]):
							import math

							A			=					0.5 - math.sin(math.asin(1.0 - 2.0 * alpha) / 3.0)
							return thetaa + ((thetaa - thetaa) * alpha)


		@staticmethod
		def 					SCREAMING_SNAKE_CASE__   (__SCREAMING_SNAKE_CASE :		Dict     ,     __SCREAMING_SNAKE_CASE :		List[Any]     ,     __SCREAMING_SNAKE_CASE :		Tuple     ,     __SCREAMING_SNAKE_CASE :		Dict):
							return thetaa + (thetaa - thetaa) * (1.0 - alpha)








"""simple docstring"""




import textwrap

import pyarrow as pa
import pytest

from datasets import Features, Image
from datasets.packaged_modules.text.text import Text

from ..utils import require_pil
@pytest.fixture
def    __SCREAMING_SNAKE_CASE						(						lowercase__      ):
					"""simple docstring"""
					A			=					tmp_path / "text.txt"
					A			=					textwrap.dedent(
					    "\\n        Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua.\n        Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat.\n        Duis aute irure dolor in reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla pariatur.\n        Excepteur sint occaecat cupidatat non proident, sunt in culpa qui officia deserunt mollit anim id est laborum.\n\n        Second paragraph:\n        Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua.\n        Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat.\n        Duis aute irure dolor in reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla pariatur.\n        Excepteur sint occaecat cupidatat non proident, sunt in culpa qui officia deserunt mollit anim id est laborum.\n        "      )
					with open(lowercase__			,							"w"			,							encoding="utf-8"      ) as f:
										f.write(lowercase__      )
					return str(lowercase__      )
@pytest.fixture
def    __SCREAMING_SNAKE_CASE						(						lowercase__			,							lowercase__      ):
					"""simple docstring"""
					A			=					tmp_path / "text_with_image.txt"
					with open(lowercase__			,							"w"			,							encoding="utf-8"      ) as f:
										f.write(lowercase__      )
					return str(lowercase__      )
@pytest.mark.parametrize("keep_linebreaks"			,							[True, False]      )
def    __SCREAMING_SNAKE_CASE						(						lowercase__			,							lowercase__      ):
					"""simple docstring"""
					with open(lowercase__			,							encoding="utf-8"      ) as f:
										A			=					f.read().splitlines(keepends=lowercase__      )
					A			=					Text(keep_linebreaks=lowercase__			,							encoding="utf-8"      )
					A			=					text._generate_tables([[text_file]]      )
					A			=					pa.concat_tables([table for _, table in generator]      ).to_pydict()["text"]
					assert generated_content == expected_content
@require_pil
def    __SCREAMING_SNAKE_CASE						(						lowercase__      ):
					"""simple docstring"""
					with open(lowercase__			,							encoding="utf-8"      ) as f:
										A			=					f.read().splitlines()[0]
					A			=					Text(encoding="utf-8"			,							features=Features({"image": Image()}      )      )
					A			=					text._generate_tables([[text_file_with_image]]      )
					A			=					pa.concat_tables([table for _, table in generator]      )
					assert pa_table.schema.field("image"      ).type == Image()()
					A			=					pa_table.to_pydict()["image"]
					assert generated_content == [{"path": image_file, "bytes": None}]
@pytest.mark.parametrize("sample_by"			,							["line", "paragraph", "document"]      )
def    __SCREAMING_SNAKE_CASE						(						lowercase__			,							lowercase__      ):
					"""simple docstring"""
					with open(lowercase__			,							encoding="utf-8"      ) as f:
										A			=					f.read()
					if sample_by == "line":
										A			=					expected_content.splitlines()
					elif sample_by == "paragraph":
										A			=					expected_content.split("\n\n"      )
					elif sample_by == "document":
										A			=					[expected_content]
					A			=					Text(sample_by=lowercase__			,							encoding="utf-8"			,							chunksize=100      )
					A			=					text._generate_tables([[text_file]]      )
					A			=					pa.concat_tables([table for _, table in generator]      ).to_pydict()["text"]
					assert generated_content == expected_content





