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


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

def 				SCREAMING_SNAKE_CASE     (      ) ->       Dict:

				"""simple docstring"""







				A__  :	List[str]         =							'''https://pypi.org/pypi/diffusers/json'''
				A__  :	Optional[int]         =							json.loads(request.urlopen(__UpperCamelCase       ).read()       )['''releases'''].keys()
				return sorted(__UpperCamelCase							,						key=lambda __UpperCamelCase       : version.Version(__UpperCamelCase       )       )

def 				SCREAMING_SNAKE_CASE     (      ) ->       Optional[int]:

				"""simple docstring"""







				if HF_MODULES_CACHE in sys.path:
								return

				sys.path.append(__UpperCamelCase       )
				os.makedirs(__UpperCamelCase							,						exist_ok=__UpperCamelCase       )
				A__  :	int         =							Path(__UpperCamelCase       ) / '''__init__.py'''
				if not init_path.exists():
								init_path.touch()

def 				SCREAMING_SNAKE_CASE     (      __UpperCamelCase :						Union[str, os.PathLike]       ) ->       Any:

				"""simple docstring"""







				init_hf_modules()
				A__  :	Tuple         =							Path(__UpperCamelCase       ) / 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(__UpperCamelCase							,						exist_ok=__UpperCamelCase       )
				A__  :	Optional[int]         =							dynamic_module_path / '''__init__.py'''
				if not init_path.exists():
								init_path.touch()

def 				SCREAMING_SNAKE_CASE     (      __UpperCamelCase :						List[str]       ) ->       str:

				"""simple docstring"""







				with open(__UpperCamelCase							,						'''r'''							,						encoding='''utf-8'''       ) as f:
								A__  :	Union[str, Any]         =							f.read()

				# Imports of the form `import .xxx`
				A__  :	int         =							re.findall('''^\s*import\s+\.(\S+)\s*$'''							,						__UpperCamelCase							,						flags=re.MULTILINE       )
				# Imports of the form `from .xxx import yyy`
				relative_imports += re.findall('''^\s*from\s+\.(\S+)\s+import'''							,						__UpperCamelCase							,						flags=re.MULTILINE       )
				# Unique-ify
				return list(set(__UpperCamelCase       )       )

def 				SCREAMING_SNAKE_CASE     (      __UpperCamelCase :						List[str]       ) ->       Dict:

				"""simple docstring"""







				A__  :	List[Any]         =							False
				A__  :	Any         =							[module_file]
				A__  :	List[Any]         =							[]

				# 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(__UpperCamelCase       )       )

								A__  :	Any         =							Path(__UpperCamelCase       ).parent
								A__  :	Optional[int]         =							[str(module_path / m       ) for m in new_imports]
								A__  :	str         =							[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__  :	Any         =							len(__UpperCamelCase       ) == 0
								all_relative_imports.extend(__UpperCamelCase       )

				return all_relative_imports

def 				SCREAMING_SNAKE_CASE     (      __UpperCamelCase :						List[str]       ) ->       str:

				"""simple docstring"""







				with open(__UpperCamelCase							,						'''r'''							,						encoding='''utf-8'''       ) as f:
								A__  :	str         =							f.read()

				# Imports of the form `import xxx`
				A__  :	List[Any]         =							re.findall('''^\s*import\s+(\S+)\s*$'''							,						__UpperCamelCase							,						flags=re.MULTILINE       )
				# Imports of the form `from xxx import yyy`
				imports += re.findall('''^\s*from\s+(\S+)\s+import'''							,						__UpperCamelCase							,						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__  :	Union[str, Any]         =							list(set(__UpperCamelCase       )       )
				A__  :	List[Any]         =							[]
				for imp in imports:
								try:
												importlib.import_module(__UpperCamelCase       )
								except ImportError:
												missing_packages.append(__UpperCamelCase       )

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

				return get_relative_imports(__UpperCamelCase       )

def 				SCREAMING_SNAKE_CASE     (      __UpperCamelCase :						int							,						__UpperCamelCase :						str       ) ->       Any:

				"""simple docstring"""







				A__  :	Any         =							module_path.replace(os.path.sep							,						'''.'''       )
				A__  :	List[str]         =							importlib.import_module(__UpperCamelCase       )

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

def 				SCREAMING_SNAKE_CASE     (      __UpperCamelCase :						Union[str, Any]       ) ->       str:

				"""simple docstring"""







				from ..pipelines import DiffusionPipeline

				A__  :	int         =							dict(inspect.getmembers(__UpperCamelCase							,						inspect.isclass       )       )

				A__  :	Any         =							None
				for cls_name, cls in cls_members.items():
								if (
								    cls_name != DiffusionPipeline.__name__
								    and issubclass(cls							,						__UpperCamelCase       )
								    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__  :	Dict         =							cls

				return pipeline_class

def 				SCREAMING_SNAKE_CASE     (      __UpperCamelCase :						Union[str, os.PathLike]							,						__UpperCamelCase :						str							,						__UpperCamelCase :						Optional[Union[str, os.PathLike]] = None							,						__UpperCamelCase :						bool = False							,						__UpperCamelCase :						bool = False							,						__UpperCamelCase :						Optional[Dict[str, str]] = None							,						__UpperCamelCase :						Optional[Union[bool, str]] = None							,						__UpperCamelCase :						Optional[str] = None							,						__UpperCamelCase :						bool = False							,						) ->       Union[str, Any]:

				"""simple docstring"""







				A__  :	str         =							str(__UpperCamelCase       )

				A__  :	Any         =							os.path.join(__UpperCamelCase							,						__UpperCamelCase       )

				if os.path.isfile(__UpperCamelCase       ):
								A__  :	Tuple         =							module_file_or_url
								A__  :	List[Any]         =							'''local'''
				elif pretrained_model_name_or_path.count('''/'''       ) == 0:
								A__  :	int         =							get_diffusers_versions()
								# cut ".dev0"
								A__  :	Union[str, Any]         =							'''v''' + '''.'''.join(__version__.split('''.'''       )[:3]       )

								# retrieve github version that matches
								if revision is None:
												A__  :	Dict         =							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__  :	Dict         =							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__  :	int         =							COMMUNITY_PIPELINES_URL.format(revision=__UpperCamelCase							,						pipeline=__UpperCamelCase       )
								try:
												A__  :	int         =							cached_download(
												    __UpperCamelCase							,						cache_dir=__UpperCamelCase							,						force_download=__UpperCamelCase							,						proxies=__UpperCamelCase							,						resume_download=__UpperCamelCase							,						local_files_only=__UpperCamelCase							,						use_auth_token=__UpperCamelCase							,						)
												A__  :	List[str]         =							'''git'''
												A__  :	List[str]         =							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__  :	Tuple         =							hf_hub_download(
												    __UpperCamelCase							,						__UpperCamelCase							,						cache_dir=__UpperCamelCase							,						force_download=__UpperCamelCase							,						proxies=__UpperCamelCase							,						resume_download=__UpperCamelCase							,						local_files_only=__UpperCamelCase							,						use_auth_token=__UpperCamelCase							,						)
												A__  :	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__  :	Optional[int]         =							check_imports(__UpperCamelCase       )

				# Now we move the module inside our cached dynamic modules.
				A__  :	List[Any]         =							DIFFUSERS_DYNAMIC_MODULE_NAME + os.path.sep + submodule
				create_dynamic_module(__UpperCamelCase       )
				A__  :	int         =							Path(__UpperCamelCase       ) / 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(__UpperCamelCase							,						submodule_path / module_file       )
								for module_needed in modules_needed:
												A__  :	Any         =							F"{module_needed}.py"
												shutil.copy(os.path.join(__UpperCamelCase							,						__UpperCamelCase       )							,						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(__UpperCamelCase							,						__UpperCamelCase       ):
												A__  :	List[Any]         =							use_auth_token
								elif use_auth_token is True:
												A__  :	Dict         =							HfFolder.get_token()
								else:
												A__  :	Optional[int]         =							None

								A__  :	List[str]         =							model_info(__UpperCamelCase							,						revision=__UpperCamelCase							,						token=__UpperCamelCase       ).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__  :	Tuple         =							submodule_path / commit_hash
								A__  :	Optional[Any]         =							full_submodule + os.path.sep + commit_hash
								create_dynamic_module(__UpperCamelCase       )

								if not (submodule_path / module_file).exists():
												shutil.copy(__UpperCamelCase							,						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(
																    __UpperCamelCase							,						F"{module_needed}.py"							,						cache_dir=__UpperCamelCase							,						force_download=__UpperCamelCase							,						resume_download=__UpperCamelCase							,						proxies=__UpperCamelCase							,						use_auth_token=__UpperCamelCase							,						revision=__UpperCamelCase							,						local_files_only=__UpperCamelCase							,						)
				return os.path.join(__UpperCamelCase							,						__UpperCamelCase       )



def 				SCREAMING_SNAKE_CASE     (      __UpperCamelCase :						Union[str, os.PathLike]							,						__UpperCamelCase :						str							,						__UpperCamelCase :						Optional[str] = None							,						__UpperCamelCase :						Optional[Union[str, os.PathLike]] = None							,						__UpperCamelCase :						bool = False							,						__UpperCamelCase :						bool = False							,						__UpperCamelCase :						Optional[Dict[str, str]] = None							,						__UpperCamelCase :						Optional[Union[bool, str]] = None							,						__UpperCamelCase :						Optional[str] = None							,						__UpperCamelCase :						bool = False							,						**__UpperCamelCase :						str							,						) ->       str:

				"""simple docstring"""







				A__  :	int         =							get_cached_module_file(
				    __UpperCamelCase							,						__UpperCamelCase							,						cache_dir=__UpperCamelCase							,						force_download=__UpperCamelCase							,						resume_download=__UpperCamelCase							,						proxies=__UpperCamelCase							,						use_auth_token=__UpperCamelCase							,						revision=__UpperCamelCase							,						local_files_only=__UpperCamelCase							,						)
				return get_class_in_module(__UpperCamelCase							,						final_module.replace('''.py'''							,						''''''       )       )

import logging

import torch
from accelerate import Accelerator
from arguments import EvaluationArguments
from datasets import load_dataset
from torch.utils.data import IterableDataset
from torch.utils.data.dataloader import DataLoader

from transformers import AutoModelForCausalLM, AutoTokenizer, HfArgumentParser, set_seed







class  UpperCamelCase__		(    SCREAMING_SNAKE_CASE_   ):







	'''simple docstring'''





	def __init__(			self				,       UpperCamelCase__				,       UpperCamelCase__				,       UpperCamelCase__=1024				,       UpperCamelCase__=1024				,       UpperCamelCase__=3.6		):
					A__  :	str         =							tokenizer
					A__  :	int         =							tokenizer.bos_token_id
					A__  :	List[Any]         =							dataset
					A__  :	Tuple         =							seq_length
					A__  :	Any         =							seq_length * chars_per_token * num_of_sequences





	def __iter__(			self		):
					A__  :	Dict         =							iter(self.dataset		)
					A__  :	Tuple         =							True
					while more_examples:
									A__       ,							A__  :	Optional[Any]         =							[], 0
									while True:
													if buffer_len >= self.input_characters:
																	break
													try:
																	buffer.append(next(UpperCamelCase__		)['''content''']		)
																	buffer_len += len(buffer[-1]		)
													except StopIteration:
																	A__  :	Dict         =							False
																	break
									A__  :	str         =							tokenizer(UpperCamelCase__				,       truncation=UpperCamelCase__		)['''input_ids''']
									A__  :	Optional[int]         =							[]
									for tokenized_input in tokenized_inputs:
													all_token_ids.extend(tokenized_input + [self.concat_token_id]		)
									for i in range(0				,       len(UpperCamelCase__		)				,       self.seq_length		):
													A__  :	Optional[int]         =							all_token_ids[i : i + self.seq_length]
													if len(UpperCamelCase__		) == self.seq_length:
																	yield torch.tensor(UpperCamelCase__		)




def 				SCREAMING_SNAKE_CASE     (      __UpperCamelCase :						List[Any]       ) ->       Any:

				"""simple docstring"""







				A__  :	Any         =							{'''streaming''': True}
				A__  :	List[str]         =							load_dataset(args.dataset_name							,						split='''train'''							,						**__UpperCamelCase       )
				A__  :	List[str]         =							ConstantLengthDataset(__UpperCamelCase							,						__UpperCamelCase							,						seq_length=args.seq_length       )
				A__  :	int         =							DataLoader(__UpperCamelCase							,						batch_size=args.batch_size       )
				return eval_dataloader

def 				SCREAMING_SNAKE_CASE     (      __UpperCamelCase :						List[str]       ) ->       Dict:

				"""simple docstring"""







				model.eval()
				A__  :	Dict         =							[]
				for step, batch in enumerate(__UpperCamelCase       ):
								with torch.no_grad():
												A__  :	Any         =							model(__UpperCamelCase							,						labels=__UpperCamelCase       )
								A__  :	Tuple         =							outputs.loss.repeat(args.batch_size       )
								losses.append(accelerator.gather(__UpperCamelCase       )       )

								if args.max_eval_steps > 0 and step >= args.max_eval_steps:
												break
				A__  :	Tuple         =							torch.mean(torch.cat(__UpperCamelCase       )       )
				try:
								A__  :	Optional[Any]         =							torch.exp(__UpperCamelCase       )
				except OverflowError:
								A__  :	Union[str, Any]         =							float('''inf'''       )
				return loss.item(), perplexity.item()


# Setup Accelerator
_SCREAMING_SNAKE_CASE    :      List[Any]          =			Accelerator()

# Parse configuration
_SCREAMING_SNAKE_CASE    :      Optional[int]          =			HfArgumentParser(EvaluationArguments)
_SCREAMING_SNAKE_CASE    :      Union[str, Any]          =			parser.parse_args()
set_seed(args.seed)

# Logging
_SCREAMING_SNAKE_CASE    :      Dict          =			logging.getLogger(__name__)
logging.basicConfig(
    format='%(asctime)s - %(levelname)s - %(name)s - %(message)s', datefmt='%m/%d/%Y %H:%M:%S', level=logging.INFO
)

# Load model and tokenizer
_SCREAMING_SNAKE_CASE    :      Optional[int]          =			AutoModelForCausalLM.from_pretrained(args.model_ckpt)
_SCREAMING_SNAKE_CASE    :      List[str]          =			AutoTokenizer.from_pretrained(args.model_ckpt)

# Load dataset and dataloader
_SCREAMING_SNAKE_CASE    :      Optional[Any]          =			create_dataloader(args)

# Prepare everything with our `accelerator`.
_SCREAMING_SNAKE_CASE   ,     _SCREAMING_SNAKE_CASE    :      Optional[Any]          =			accelerator.prepare(model, eval_dataloader)

# Evaluate and save the last checkpoint
logger.info('Evaluating and saving model after training')
_SCREAMING_SNAKE_CASE   ,     _SCREAMING_SNAKE_CASE    :      Optional[int]          =			evaluate(args)



logger.info(f"""loss/eval: {eval_loss}, perplexity: {perplexity}""")