



import argparse
import json
from collections import OrderedDict
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import (
    ConditionalDetrConfig,
    ConditionalDetrForObjectDetection,
    ConditionalDetrForSegmentation,
    ConditionalDetrImageProcessor,
)
from transformers.utils import logging


logging.set_verbosity_info()
__SCREAMING_SNAKE_CASE           =  logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
__SCREAMING_SNAKE_CASE           =  []
for i in range(6):
							# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
							rename_keys.append(
							    (F"""transformer.encoder.layers.{i}.self_attn.out_proj.weight""", F"""encoder.layers.{i}.self_attn.out_proj.weight""")
							)
							rename_keys.append(
							    (F"""transformer.encoder.layers.{i}.self_attn.out_proj.bias""", F"""encoder.layers.{i}.self_attn.out_proj.bias""")
							)
							rename_keys.append((F"""transformer.encoder.layers.{i}.linear1.weight""", F"""encoder.layers.{i}.fc1.weight"""))
							rename_keys.append((F"""transformer.encoder.layers.{i}.linear1.bias""", F"""encoder.layers.{i}.fc1.bias"""))
							rename_keys.append((F"""transformer.encoder.layers.{i}.linear2.weight""", F"""encoder.layers.{i}.fc2.weight"""))
							rename_keys.append((F"""transformer.encoder.layers.{i}.linear2.bias""", F"""encoder.layers.{i}.fc2.bias"""))
							rename_keys.append(
							    (F"""transformer.encoder.layers.{i}.norm1.weight""", F"""encoder.layers.{i}.self_attn_layer_norm.weight""")
							)
							rename_keys.append((F"""transformer.encoder.layers.{i}.norm1.bias""", F"""encoder.layers.{i}.self_attn_layer_norm.bias"""))
							rename_keys.append((F"""transformer.encoder.layers.{i}.norm2.weight""", F"""encoder.layers.{i}.final_layer_norm.weight"""))
							rename_keys.append((F"""transformer.encoder.layers.{i}.norm2.bias""", F"""encoder.layers.{i}.final_layer_norm.bias"""))
							# decoder layers: 2 times output projection, 2 feedforward neural networks and 3 layernorms
							rename_keys.append(
							    (F"""transformer.decoder.layers.{i}.self_attn.out_proj.weight""", F"""decoder.layers.{i}.self_attn.out_proj.weight""")
							)
							rename_keys.append(
							    (F"""transformer.decoder.layers.{i}.self_attn.out_proj.bias""", F"""decoder.layers.{i}.self_attn.out_proj.bias""")
							)
							rename_keys.append(
							    (
							        F"""transformer.decoder.layers.{i}.cross_attn.out_proj.weight""",
							        F"""decoder.layers.{i}.encoder_attn.out_proj.weight""",
							    )
							)
							rename_keys.append(
							    (
							        F"""transformer.decoder.layers.{i}.cross_attn.out_proj.bias""",
							        F"""decoder.layers.{i}.encoder_attn.out_proj.bias""",
							    )
							)
							rename_keys.append((F"""transformer.decoder.layers.{i}.linear1.weight""", F"""decoder.layers.{i}.fc1.weight"""))
							rename_keys.append((F"""transformer.decoder.layers.{i}.linear1.bias""", F"""decoder.layers.{i}.fc1.bias"""))
							rename_keys.append((F"""transformer.decoder.layers.{i}.linear2.weight""", F"""decoder.layers.{i}.fc2.weight"""))
							rename_keys.append((F"""transformer.decoder.layers.{i}.linear2.bias""", F"""decoder.layers.{i}.fc2.bias"""))
							rename_keys.append(
							    (F"""transformer.decoder.layers.{i}.norm1.weight""", F"""decoder.layers.{i}.self_attn_layer_norm.weight""")
							)
							rename_keys.append((F"""transformer.decoder.layers.{i}.norm1.bias""", F"""decoder.layers.{i}.self_attn_layer_norm.bias"""))
							rename_keys.append(
							    (F"""transformer.decoder.layers.{i}.norm2.weight""", F"""decoder.layers.{i}.encoder_attn_layer_norm.weight""")
							)
							rename_keys.append(
							    (F"""transformer.decoder.layers.{i}.norm2.bias""", F"""decoder.layers.{i}.encoder_attn_layer_norm.bias""")
							)
							rename_keys.append((F"""transformer.decoder.layers.{i}.norm3.weight""", F"""decoder.layers.{i}.final_layer_norm.weight"""))
							rename_keys.append((F"""transformer.decoder.layers.{i}.norm3.bias""", F"""decoder.layers.{i}.final_layer_norm.bias"""))

							# q, k, v projections in self/cross-attention in decoder for conditional DETR
							rename_keys.append(
							    (F"""transformer.decoder.layers.{i}.sa_qcontent_proj.weight""", F"""decoder.layers.{i}.sa_qcontent_proj.weight""")
							)
							rename_keys.append(
							    (F"""transformer.decoder.layers.{i}.sa_kcontent_proj.weight""", F"""decoder.layers.{i}.sa_kcontent_proj.weight""")
							)
							rename_keys.append(
							    (F"""transformer.decoder.layers.{i}.sa_qpos_proj.weight""", F"""decoder.layers.{i}.sa_qpos_proj.weight""")
							)
							rename_keys.append(
							    (F"""transformer.decoder.layers.{i}.sa_kpos_proj.weight""", F"""decoder.layers.{i}.sa_kpos_proj.weight""")
							)
							rename_keys.append((F"""transformer.decoder.layers.{i}.sa_v_proj.weight""", F"""decoder.layers.{i}.sa_v_proj.weight"""))
							rename_keys.append(
							    (F"""transformer.decoder.layers.{i}.ca_qcontent_proj.weight""", F"""decoder.layers.{i}.ca_qcontent_proj.weight""")
							)
							# rename_keys.append((f"transformer.decoder.layers.{i}.ca_qpos_proj.weight", f"decoder.layers.{i}.ca_qpos_proj.weight"))
							rename_keys.append(
							    (F"""transformer.decoder.layers.{i}.ca_kcontent_proj.weight""", F"""decoder.layers.{i}.ca_kcontent_proj.weight""")
							)
							rename_keys.append(
							    (F"""transformer.decoder.layers.{i}.ca_kpos_proj.weight""", F"""decoder.layers.{i}.ca_kpos_proj.weight""")
							)
							rename_keys.append((F"""transformer.decoder.layers.{i}.ca_v_proj.weight""", F"""decoder.layers.{i}.ca_v_proj.weight"""))
							rename_keys.append(
							    (F"""transformer.decoder.layers.{i}.ca_qpos_sine_proj.weight""", F"""decoder.layers.{i}.ca_qpos_sine_proj.weight""")
							)

							rename_keys.append(
							    (F"""transformer.decoder.layers.{i}.sa_qcontent_proj.bias""", F"""decoder.layers.{i}.sa_qcontent_proj.bias""")
							)
							rename_keys.append(
							    (F"""transformer.decoder.layers.{i}.sa_kcontent_proj.bias""", F"""decoder.layers.{i}.sa_kcontent_proj.bias""")
							)
							rename_keys.append((F"""transformer.decoder.layers.{i}.sa_qpos_proj.bias""", F"""decoder.layers.{i}.sa_qpos_proj.bias"""))
							rename_keys.append((F"""transformer.decoder.layers.{i}.sa_kpos_proj.bias""", F"""decoder.layers.{i}.sa_kpos_proj.bias"""))
							rename_keys.append((F"""transformer.decoder.layers.{i}.sa_v_proj.bias""", F"""decoder.layers.{i}.sa_v_proj.bias"""))
							rename_keys.append(
							    (F"""transformer.decoder.layers.{i}.ca_qcontent_proj.bias""", F"""decoder.layers.{i}.ca_qcontent_proj.bias""")
							)
							# rename_keys.append((f"transformer.decoder.layers.{i}.ca_qpos_proj.bias", f"decoder.layers.{i}.ca_qpos_proj.bias"))
							rename_keys.append(
							    (F"""transformer.decoder.layers.{i}.ca_kcontent_proj.bias""", F"""decoder.layers.{i}.ca_kcontent_proj.bias""")
							)
							rename_keys.append((F"""transformer.decoder.layers.{i}.ca_kpos_proj.bias""", F"""decoder.layers.{i}.ca_kpos_proj.bias"""))
							rename_keys.append((F"""transformer.decoder.layers.{i}.ca_v_proj.bias""", F"""decoder.layers.{i}.ca_v_proj.bias"""))
							rename_keys.append(
							    (F"""transformer.decoder.layers.{i}.ca_qpos_sine_proj.bias""", F"""decoder.layers.{i}.ca_qpos_sine_proj.bias""")
							)

# convolutional projection + query embeddings + layernorm of decoder + class and bounding box heads
# for conditional DETR, also convert reference point head and query scale MLP
rename_keys.extend(
    [
        ("""input_proj.weight""", """input_projection.weight"""),
        ("""input_proj.bias""", """input_projection.bias"""),
        ("""query_embed.weight""", """query_position_embeddings.weight"""),
        ("""transformer.decoder.norm.weight""", """decoder.layernorm.weight"""),
        ("""transformer.decoder.norm.bias""", """decoder.layernorm.bias"""),
        ("""class_embed.weight""", """class_labels_classifier.weight"""),
        ("""class_embed.bias""", """class_labels_classifier.bias"""),
        ("""bbox_embed.layers.0.weight""", """bbox_predictor.layers.0.weight"""),
        ("""bbox_embed.layers.0.bias""", """bbox_predictor.layers.0.bias"""),
        ("""bbox_embed.layers.1.weight""", """bbox_predictor.layers.1.weight"""),
        ("""bbox_embed.layers.1.bias""", """bbox_predictor.layers.1.bias"""),
        ("""bbox_embed.layers.2.weight""", """bbox_predictor.layers.2.weight"""),
        ("""bbox_embed.layers.2.bias""", """bbox_predictor.layers.2.bias"""),
        ("""transformer.decoder.ref_point_head.layers.0.weight""", """decoder.ref_point_head.layers.0.weight"""),
        ("""transformer.decoder.ref_point_head.layers.0.bias""", """decoder.ref_point_head.layers.0.bias"""),
        ("""transformer.decoder.ref_point_head.layers.1.weight""", """decoder.ref_point_head.layers.1.weight"""),
        ("""transformer.decoder.ref_point_head.layers.1.bias""", """decoder.ref_point_head.layers.1.bias"""),
        ("""transformer.decoder.query_scale.layers.0.weight""", """decoder.query_scale.layers.0.weight"""),
        ("""transformer.decoder.query_scale.layers.0.bias""", """decoder.query_scale.layers.0.bias"""),
        ("""transformer.decoder.query_scale.layers.1.weight""", """decoder.query_scale.layers.1.weight"""),
        ("""transformer.decoder.query_scale.layers.1.bias""", """decoder.query_scale.layers.1.bias"""),
        ("""transformer.decoder.layers.0.ca_qpos_proj.weight""", """decoder.layers.0.ca_qpos_proj.weight"""),
        ("""transformer.decoder.layers.0.ca_qpos_proj.bias""", """decoder.layers.0.ca_qpos_proj.bias"""),
    ]
)




def 	UpperCAmelCase   (						_lowerCamelCase ,    _lowerCamelCase ,    _lowerCamelCase    ):
			A							:					Optional[Any]              =						state_dict.pop(_lowerCamelCase    )
			A							:					Tuple              =						val




def 	UpperCAmelCase   (						_lowerCamelCase    ):
			A							:					Optional[int]              =						OrderedDict()
			for key, value in state_dict.items():
						if "backbone.0.body" in key:
									A							:					Optional[Any]              =						key.replace("backbone.0.body" ,    "backbone.conv_encoder.model"    )
									A							:					Dict              =						value
						else:
									A							:					Dict              =						value

			return new_state_dict




def 	UpperCAmelCase   (						_lowerCamelCase ,    _lowerCamelCase=False    ):
			A							:					int              =						""
			if is_panoptic:
						A							:					List[str]              =						"conditional_detr."

			# first: transformer encoder
			for i in range(6    ):
						# read in weights + bias of input projection layer (in PyTorch's MultiHeadAttention, this is a single matrix + bias)
						A							:					List[str]              =						state_dict.pop(f"""{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight"""    )
						A							:					Optional[Any]              =						state_dict.pop(f"""{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_bias"""    )
						# next, add query, keys and values (in that order) to the state dict
						A							:					Any              =						in_proj_weight[:256, :]
						A							:					Optional[Any]              =						in_proj_bias[:256]
						A							:					List[str]              =						in_proj_weight[256:512, :]
						A							:					List[str]              =						in_proj_bias[256:512]
						A							:					List[str]              =						in_proj_weight[-256:, :]
						A							:					List[str]              =						in_proj_bias[-256:]




def 	UpperCAmelCase   (						):
			A							:					Dict              =						"http://images.cocodataset.org/val2017/000000039769.jpg"
			A							:					List[str]              =						Image.open(requests.get(_lowerCamelCase ,    stream=_lowerCamelCase    ).raw    )

			return im




@torch.no_grad()
def 	UpperCAmelCase   (						_lowerCamelCase ,    _lowerCamelCase    ):
			A							:					Optional[int]              =						ConditionalDetrConfig()
			# set backbone and dilation attributes
			if "resnet101" in model_name:
						A							:					List[str]              =						"resnet101"
			if "dc5" in model_name:
						A							:					List[Any]              =						True
			A							:					List[str]              =						"panoptic" in model_name
			if is_panoptic:
						A							:					Any              =						250
			else:
						A							:					List[Any]              =						91
						A							:					Optional[Any]              =						"huggingface/label-files"
						A							:					Optional[Any]              =						"coco-detection-id2label.json"
						A							:					List[Any]              =						json.load(open(hf_hub_download(_lowerCamelCase ,    _lowerCamelCase ,    repo_type="dataset"    ) ,    "r"    )    )
						A							:					str              =						{int(_lowerCamelCase    ): v for k, v in idalabel.items()}
						A							:					int              =						idalabel
						A							:					Dict              =						{v: k for k, v in idalabel.items()}

			# load image processor
			A							:					Optional[Any]              =						"coco_panoptic" if is_panoptic else "coco_detection"
			A							:					int              =						ConditionalDetrImageProcessor(format=_lowerCamelCase    )

			# prepare image
			A							:					Any              =						prepare_img()
			A							:					Optional[Any]              =						image_processor(images=_lowerCamelCase ,    return_tensors="pt"    )
			A							:					Optional[Any]              =						encoding["pixel_values"]

			logger.info(f"""Converting model {model_name}..."""    )

			# load original model from torch hub
			A							:					Optional[Any]              =						torch.hub.load("DeppMeng/ConditionalDETR" ,    _lowerCamelCase ,    pretrained=_lowerCamelCase    ).eval()
			A							:					List[str]              =						conditional_detr.state_dict()
			# rename keys
			for src, dest in rename_keys:
						if is_panoptic:
									A							:					Dict              =						"conditional_detr." + src
						rename_key(_lowerCamelCase ,    _lowerCamelCase ,    _lowerCamelCase    )
			A							:					List[Any]              =						rename_backbone_keys(_lowerCamelCase    )
			# query, key and value matrices need special treatment
			read_in_q_k_v(_lowerCamelCase ,    is_panoptic=_lowerCamelCase    )
			# important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
			A							:					Optional[Any]              =						"conditional_detr.model." if is_panoptic else "model."
			for key in state_dict.copy().keys():
						if is_panoptic:
									if (
									    key.startswith("conditional_detr"    )
									    and not key.startswith("class_labels_classifier"    )
									    and not key.startswith("bbox_predictor"    )
									):
												A							:					List[str]              =						state_dict.pop(_lowerCamelCase    )
												A							:					int              =						val
									elif "class_labels_classifier" in key or "bbox_predictor" in key:
												A							:					Dict              =						state_dict.pop(_lowerCamelCase    )
												A							:					Any              =						val
									elif key.startswith("bbox_attention"    ) or key.startswith("mask_head"    ):
												continue
									else:
												A							:					Tuple              =						state_dict.pop(_lowerCamelCase    )
												A							:					Dict              =						val
						else:
									if not key.startswith("class_labels_classifier"    ) and not key.startswith("bbox_predictor"    ):
												A							:					Optional[int]              =						state_dict.pop(_lowerCamelCase    )
												A							:					Dict              =						val
    # finally, create HuggingFace model and load state dict
			A							:					str              =						ConditionalDetrForSegmentation(_lowerCamelCase    ) if is_panoptic else ConditionalDetrForObjectDetection(_lowerCamelCase    )
			model.load_state_dict(_lowerCamelCase    )
			model.eval()
			model.push_to_hub(repo_id=_lowerCamelCase ,    organization="DepuMeng" ,    commit_message="Add model"    )
			# verify our conversion
			A							:					Dict              =						conditional_detr(_lowerCamelCase    )
			A							:					Union[str, Any]              =						model(_lowerCamelCase    )
			assert torch.allclose(outputs.logits ,    original_outputs["pred_logits"] ,    atol=1e-4    )
			assert torch.allclose(outputs.pred_boxes ,    original_outputs["pred_boxes"] ,    atol=1e-4    )
			if is_panoptic:
						assert torch.allclose(outputs.pred_masks ,    original_outputs["pred_masks"] ,    atol=1e-4    )

			# Save model and image processor
			logger.info(f"""Saving PyTorch model and image processor to {pytorch_dump_folder_path}..."""    )
			Path(_lowerCamelCase    ).mkdir(exist_ok=_lowerCamelCase    )
			model.save_pretrained(_lowerCamelCase    )
			image_processor.save_pretrained(_lowerCamelCase    )


if __name__ == "__main__":
							__SCREAMING_SNAKE_CASE           =  argparse.ArgumentParser()

							parser.add_argument(
							    """--model_name""",
							    default="""conditional_detr_resnet50""",
							    type=str,
							    help="""Name of the CONDITIONAL_DETR model you'd like to convert.""",
							)
							parser.add_argument(
							    """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the folder to output PyTorch model."""
							)
							__SCREAMING_SNAKE_CASE           =  parser.parse_args()
							convert_conditional_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)




import importlib
import inspect
import json
import os
import re
import shutil
import sys
from pathlib import Path
from typing import Dict, Optional, Union
from urllib import request

from huggingface_hub import HfFolder, cached_download, hf_hub_download, model_info
from packaging import version

from .. import __version__
from . import DIFFUSERS_DYNAMIC_MODULE_NAME, HF_MODULES_CACHE, logging


__SCREAMING_SNAKE_CASE           =  (
    """https://raw.githubusercontent.com/huggingface/diffusers/{revision}/examples/community/{pipeline}.py"""
)


__SCREAMING_SNAKE_CASE           =  logging.get_logger(__name__)  # pylint: disable=invalid-name




def 	UpperCAmelCase   (						):
			A							:					Union[str, Any]              =						"https://pypi.org/pypi/diffusers/json"
			A							:					List[Any]              =						json.loads(request.urlopen(_lowerCamelCase    ).read()    )["releases"].keys()
			return sorted(_lowerCamelCase ,    key=lambda _lowerCamelCase    : version.Version(_lowerCamelCase    )    )




def 	UpperCAmelCase   (						):
			# This function has already been executed if HF_MODULES_CACHE already is in the Python path.
			if HF_MODULES_CACHE in sys.path:
						return

			sys.path.append(_lowerCamelCase    )
			os.makedirs(_lowerCamelCase ,    exist_ok=_lowerCamelCase    )
			A							:					List[Any]              =						Path(_lowerCamelCase    ) / "__init__.py"
			if not init_path.exists():
						init_path.touch()




def 	UpperCAmelCase   (						_lowerCamelCase    ):
			init_hf_modules()
			A							:					Tuple              =						Path(_lowerCamelCase    ) / name
			# If the parent module does not exist yet, recursively create it.
			if not dynamic_module_path.parent.exists():
						create_dynamic_module(dynamic_module_path.parent    )
			os.makedirs(_lowerCamelCase ,    exist_ok=_lowerCamelCase    )
			A							:					Optional[int]              =						dynamic_module_path / "__init__.py"
			if not init_path.exists():
						init_path.touch()




def 	UpperCAmelCase   (						_lowerCamelCase    ):
			with open(_lowerCamelCase ,    "r" ,    encoding="utf-8"    ) as f:
						A							:					Union[str, Any]              =						f.read()

			# Imports of the form `import .xxx`
			A							:					Union[str, Any]              =						re.findall("^\s*import\s+\.(\S+)\s*$" ,    _lowerCamelCase ,    flags=re.MULTILINE    )
			# Imports of the form `from .xxx import yyy`
			relative_imports += re.findall("^\s*from\s+\.(\S+)\s+import" ,    _lowerCamelCase ,    flags=re.MULTILINE    )
			# Unique-ify
			return list(set(_lowerCamelCase    )    )




def 	UpperCAmelCase   (						_lowerCamelCase    ):
			A							:					Optional[int]              =						False
			A							:					Tuple              =						[module_file]
			A							:					Optional[int]              =						[]

			# Let's recurse through all relative imports
			while not no_change:
						A							:					Optional[Any]              =						[]
						for f in files_to_check:
									new_imports.extend(get_relative_imports(_lowerCamelCase    )    )

						A							:					Optional[Any]              =						Path(_lowerCamelCase    ).parent
						A							:					List[str]              =						[str(module_path / m    ) for m in new_imports]
						A							:					Optional[Any]              =						[f for f in new_import_files if f not in all_relative_imports]
						A							:					Union[str, Any]              =						[f"""{f}.py""" for f in new_import_files]

						A							:					Tuple              =						len(_lowerCamelCase    ) == 0
						all_relative_imports.extend(_lowerCamelCase    )

			return all_relative_imports




def 	UpperCAmelCase   (						_lowerCamelCase    ):
			with open(_lowerCamelCase ,    "r" ,    encoding="utf-8"    ) as f:
						A							:					Dict              =						f.read()

			# Imports of the form `import xxx`
			A							:					List[str]              =						re.findall("^\s*import\s+(\S+)\s*$" ,    _lowerCamelCase ,    flags=re.MULTILINE    )
			# Imports of the form `from xxx import yyy`
			imports += re.findall("^\s*from\s+(\S+)\s+import" ,    _lowerCamelCase ,    flags=re.MULTILINE    )
			# Only keep the top-level module
			A							:					Optional[int]              =						[imp.split("."    )[0] for imp in imports if not imp.startswith("."    )]

			# Unique-ify and test we got them all
			A							:					Any              =						list(set(_lowerCamelCase    )    )
			A							:					Tuple              =						[]
			for imp in imports:
						try:
									importlib.import_module(_lowerCamelCase    )
						except ImportError:
									missing_packages.append(_lowerCamelCase    )

			if len(_lowerCamelCase    ) > 0:
						raise ImportError(
						    "This modeling file requires the following packages that were not found in your environment: "
						    f"""{", ".join(_lowerCamelCase    )}. Run `pip install {" ".join(_lowerCamelCase    )}`"""    )

			return get_relative_imports(_lowerCamelCase    )




def 	UpperCAmelCase   (						_lowerCamelCase ,    _lowerCamelCase    ):
			A							:					int              =						module_path.replace(os.path.sep ,    "."    )
			A							:					Optional[Any]              =						importlib.import_module(_lowerCamelCase    )

			if class_name is None:
						return find_pipeline_class(_lowerCamelCase    )
			return getattr(_lowerCamelCase ,    _lowerCamelCase    )




def 	UpperCAmelCase   (						_lowerCamelCase    ):
			from ..pipelines import DiffusionPipeline

			A							:					int              =						dict(inspect.getmembers(_lowerCamelCase ,    inspect.isclass    )    )

			A							:					Union[str, Any]              =						None
			for cls_name, cls in cls_members.items():
						if (
						    cls_name != DiffusionPipeline.__name__
						    and issubclass(cls ,    _lowerCamelCase    )
						    and cls.__module__.split("."    )[0] != "diffusers"
						):
									if pipeline_class is not None:
												raise ValueError(
												    f"""Multiple classes that inherit from {DiffusionPipeline.__name__} have been found:"""
												    f""" {pipeline_class.__name__}, and {cls_name}. Please make sure to define only one in"""
												    f""" {loaded_module}."""    )
									A							:					Any              =						cls

			return pipeline_class




def 	UpperCAmelCase   (						_lowerCamelCase ,    _lowerCamelCase ,    _lowerCamelCase = None ,    _lowerCamelCase = False ,    _lowerCamelCase = False ,    _lowerCamelCase = None ,    _lowerCamelCase = None ,    _lowerCamelCase = None ,    _lowerCamelCase = False ,    ):
			A							:					List[Any]              =						str(_lowerCamelCase    )

			A							:					Any              =						os.path.join(_lowerCamelCase ,    _lowerCamelCase    )

			if os.path.isfile(_lowerCamelCase    ):
						A							:					Union[str, Any]              =						module_file_or_url
						A							:					Any              =						"local"
			elif pretrained_model_name_or_path.count("/"    ) == 0:
						A							:					Optional[Any]              =						get_diffusers_versions()
						# cut ".dev0"
						A							:					Union[str, Any]              =						"v" + ".".join(__version__.split("."    )[:3]    )

						# retrieve github version that matches
						if revision is None:
									A							:					List[Any]              =						latest_version if latest_version[1:] in available_versions else "main"
									logger.info(f"""Defaulting to latest_version: {revision}."""    )
						elif revision in available_versions:
									A							:					Optional[Any]              =						f"""v{revision}"""
						elif revision == "main":
									A							:					Dict              =						revision
						else:
									raise ValueError(
									    f"""`custom_revision`: {revision} does not exist. Please make sure to choose one of"""
									    f""" {", ".join(available_versions + ["main"]    )}."""    )

						# community pipeline on GitHub
						A							:					Dict              =						COMMUNITY_PIPELINES_URL.format(revision=_lowerCamelCase ,    pipeline=_lowerCamelCase    )
						try:
									A							:					Optional[int]              =						cached_download(
									    _lowerCamelCase ,    cache_dir=_lowerCamelCase ,    force_download=_lowerCamelCase ,    proxies=_lowerCamelCase ,    resume_download=_lowerCamelCase ,    local_files_only=_lowerCamelCase ,    use_auth_token=_lowerCamelCase ,    )
									A							:					Optional[Any]              =						"git"
									A							:					Any              =						pretrained_model_name_or_path + ".py"
						except EnvironmentError:
									logger.error(f"""Could not locate the {module_file} inside {pretrained_model_name_or_path}."""    )
									raise
			else:
						try:
									# Load from URL or cache if already cached
									A							:					Any              =						hf_hub_download(
									    _lowerCamelCase ,    _lowerCamelCase ,    cache_dir=_lowerCamelCase ,    force_download=_lowerCamelCase ,    proxies=_lowerCamelCase ,    resume_download=_lowerCamelCase ,    local_files_only=_lowerCamelCase ,    use_auth_token=_lowerCamelCase ,    )
									A							:					Optional[Any]              =						os.path.join("local" ,    "--".join(pretrained_model_name_or_path.split("/"    )    )    )
						except EnvironmentError:
									logger.error(f"""Could not locate the {module_file} inside {pretrained_model_name_or_path}."""    )
									raise

    # Check we have all the requirements in our environment
			A							:					List[str]              =						check_imports(_lowerCamelCase    )

			# Now we move the module inside our cached dynamic modules.
			A							:					List[str]              =						DIFFUSERS_DYNAMIC_MODULE_NAME + os.path.sep + submodule
			create_dynamic_module(_lowerCamelCase    )
			A							:					Optional[int]              =						Path(_lowerCamelCase    ) / full_submodule
			if submodule == "local" or submodule == "git":
						# We always copy local files (we could hash the file to see if there was a change, and give them the name of
						# that hash, to only copy when there is a modification but it seems overkill for now).
						# The only reason we do the copy is to avoid putting too many folders in sys.path.
						shutil.copy(_lowerCamelCase ,    submodule_path / module_file    )
						for module_needed in modules_needed:
									A							:					int              =						f"""{module_needed}.py"""
									shutil.copy(os.path.join(_lowerCamelCase ,    _lowerCamelCase    ) ,    submodule_path / module_needed    )
			else:
						# Get the commit hash
						# TODO: we will get this info in the etag soon, so retrieve it from there and not here.
						if isinstance(_lowerCamelCase ,    _lowerCamelCase    ):
									A							:					Optional[Any]              =						use_auth_token
						elif use_auth_token is True:
									A							:					Dict              =						HfFolder.get_token()
						else:
									A							:					Tuple              =						None

						A							:					List[str]              =						model_info(_lowerCamelCase ,    revision=_lowerCamelCase ,    token=_lowerCamelCase    ).sha

						# The module file will end up being placed in a subfolder with the git hash of the repo. This way we get the
						# benefit of versioning.
						A							:					str              =						submodule_path / commit_hash
						A							:					List[str]              =						full_submodule + os.path.sep + commit_hash
						create_dynamic_module(_lowerCamelCase    )

						if not (submodule_path / module_file).exists():
									shutil.copy(_lowerCamelCase ,    submodule_path / module_file    )
						# Make sure we also have every file with relative
						for module_needed in modules_needed:
									if not (submodule_path / module_needed).exists():
												get_cached_module_file(
												    _lowerCamelCase ,    f"""{module_needed}.py""" ,    cache_dir=_lowerCamelCase ,    force_download=_lowerCamelCase ,    resume_download=_lowerCamelCase ,    proxies=_lowerCamelCase ,    use_auth_token=_lowerCamelCase ,    revision=_lowerCamelCase ,    local_files_only=_lowerCamelCase ,    )
			return os.path.join(_lowerCamelCase ,    _lowerCamelCase    )






def 	UpperCAmelCase   (						_lowerCamelCase ,    _lowerCamelCase ,    _lowerCamelCase = None ,    _lowerCamelCase = None ,    _lowerCamelCase = False ,    _lowerCamelCase = False ,    _lowerCamelCase = None ,    _lowerCamelCase = None ,    _lowerCamelCase = None ,    _lowerCamelCase = False ,    **_lowerCamelCase ,    ):
			A							:					int              =						get_cached_module_file(
			    _lowerCamelCase ,    _lowerCamelCase ,    cache_dir=_lowerCamelCase ,    force_download=_lowerCamelCase ,    resume_download=_lowerCamelCase ,    proxies=_lowerCamelCase ,    use_auth_token=_lowerCamelCase ,    revision=_lowerCamelCase ,    local_files_only=_lowerCamelCase ,    )
			return get_class_in_module(_lowerCamelCase ,    final_module.replace(".py" ,    ""    )    )