



import argparse
import os
import re


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_dummies.py
snake_case_			:     List[Any]  	=  'src/diffusers'

# Matches is_xxx_available()
snake_case_			:     List[str]  	=  re.compile(r'is\_([a-z_]*)_available\(\)')
# Matches from xxx import bla
snake_case_			:     Any  	=  re.compile(r'\s+from\s+\S*\s+import\s+([^\(\s].*)\n')


snake_case_			:     Optional[Any]  	=  '\n{0} = None\n'

snake_case_			:     Tuple  	=  '\nclass {0}(metaclass=DummyObject):\n    _backends = {1}\n\n    def __init__(self, *args, **kwargs):\n        requires_backends(self, {1})\n\n    @classmethod\n    def from_config(cls, *args, **kwargs):\n        requires_backends(cls, {1})\n\n    @classmethod\n    def from_pretrained(cls, *args, **kwargs):\n        requires_backends(cls, {1})\n'


snake_case_			:     int  	=  '\ndef {0}(*args, **kwargs):\n    requires_backends({0}, {1})\n'






def A__   (					UpperCAmelCase_							):
						_UpperCamelCase :		List[Any]			    =       _re_backend.findall(UpperCAmelCase_							)
						if len(UpperCAmelCase_							) == 0:
												return None

						return "_and_".join(UpperCAmelCase_							)






def A__   (					):
						with open(os.path.join(UpperCAmelCase_  ,  '__init__.py'							)  ,  'r'  ,  encoding='utf-8'  ,  newline='\n'							) as f:
												_UpperCamelCase :		Union[str, Any]			    =       f.readlines()

						# Get to the point we do the actual imports for type checking
						_UpperCamelCase :		List[str]			    =       0
						_UpperCamelCase :		Any			    =       {}
						# Go through the end of the file
						while line_index < len(UpperCAmelCase_							):
												# If the line contains is_backend_available, we grab all objects associated with the `else` block
												_UpperCamelCase :		Tuple			    =       find_backend(lines[line_index]							)
												if backend is not None:
																		while not lines[line_index].startswith('else:'							):
																								line_index += 1
																		line_index += 1
																		_UpperCamelCase :		Tuple			    =       []
																		# Until we unindent, add backend objects to the list
																		while line_index < len(UpperCAmelCase_							) and len(lines[line_index]							) > 1:
																								_UpperCamelCase :		int			    =       lines[line_index]
																								_UpperCamelCase :		str			    =       _re_single_line_import.search(UpperCAmelCase_							)
																								if single_line_import_search is not None:
																														objects.extend(single_line_import_search.groups()[0].split(', '							)							)
																								elif line.startswith(' ' * 8							):
																														objects.append(line[8:-2]							)
																								line_index += 1

																		if len(UpperCAmelCase_							) > 0:
																								_UpperCamelCase :		Dict			    =       objects
												else:
																		line_index += 1

						return backend_specific_objects






def A__   (					UpperCAmelCase_  ,  UpperCAmelCase_							):
						if name.isupper():
												return DUMMY_CONSTANT.format(UpperCAmelCase_							)
						elif name.islower():
												return DUMMY_FUNCTION.format(UpperCAmelCase_  ,  UpperCAmelCase_							)
						else:
												return DUMMY_CLASS.format(UpperCAmelCase_  ,  UpperCAmelCase_							)






def A__   (					UpperCAmelCase_=None							):
						if backend_specific_objects is None:
												_UpperCamelCase :		Dict			    =       read_init()
						# For special correspondence backend to module name as used in the function requires_modulename
						_UpperCamelCase :		Any			    =       {}

						for backend, objects in backend_specific_objects.items():
												_UpperCamelCase :		int			    =       '[' + ', '.join(f'"{b}"' for b in backend.split('_and_'							)							) + ']'
												_UpperCamelCase :		Dict			    =       '# This file is autogenerated by the command `make fix-copies`, do not edit.\n'
												dummy_file += "from ..utils import DummyObject, requires_backends\n\n"
												dummy_file += "\n".join([create_dummy_object(UpperCAmelCase_  ,  UpperCAmelCase_							) for o in objects]							)
												_UpperCamelCase :		Dict			    =       dummy_file

						return dummy_files






def A__   (					UpperCAmelCase_=False							):
						_UpperCamelCase :		List[str]			    =       create_dummy_files()
						# For special correspondence backend to shortcut as used in utils/dummy_xxx_objects.py
						_UpperCamelCase :		int			    =       {'torch': 'pt'}

						# Locate actual dummy modules and read their content.
						_UpperCamelCase :		List[str]			    =       os.path.join(UpperCAmelCase_  ,  'utils'							)
						_UpperCamelCase :		str			    =       {
						    backend: os.path.join(UpperCAmelCase_  ,  f'dummy_{short_names.get(UpperCAmelCase_  ,  UpperCAmelCase_							)}_objects.py'							)
						    for backend in dummy_files.keys()
						}

						_UpperCamelCase :		List[Any]			    =       {}
						for backend, file_path in dummy_file_paths.items():
												if os.path.isfile(UpperCAmelCase_							):
																		with open(UpperCAmelCase_  ,  'r'  ,  encoding='utf-8'  ,  newline='\n'							) as f:
																								_UpperCamelCase :		Optional[int]			    =       f.read()
												else:
																		_UpperCamelCase :		Optional[Any]			    =       ''

						for backend in dummy_files.keys():
												if dummy_files[backend] != actual_dummies[backend]:
																		if overwrite:
																								print(
																								    f'Updating diffusers.utils.dummy_{short_names.get(UpperCAmelCase_  ,  UpperCAmelCase_							)}_objects.py as the main '
																								    '__init__ has new objects.'							)
																								with open(dummy_file_paths[backend]  ,  'w'  ,  encoding='utf-8'  ,  newline='\n'							) as f:
																														f.write(dummy_files[backend]							)
																		else:
																								raise ValueError(
																								    'The main __init__ has objects that are not present in '
																								    f'diffusers.utils.dummy_{short_names.get(UpperCAmelCase_  ,  UpperCAmelCase_							)}_objects.py. Run `make fix-copies` '
																								    'to fix this.'							)


if __name__ == "__main__":
					snake_case_			:     Dict  	=  argparse.ArgumentParser()
					parser.add_argument('--fix_and_overwrite', action='store_true', help='Whether to fix inconsistencies.')
					snake_case_			:     Optional[int]  	=  parser.parse_args()

					check_dummies(args.fix_and_overwrite)







'''simple docstring'''
from collections import OrderedDict
from typing import Any, List, Mapping, Optional

from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging


snake_case_			:     int  	=  logging.get_logger(__name__)

snake_case_			:     List[str]  	=  {
    'EleutherAI/gpt-j-6B': 'https://huggingface.co/EleutherAI/gpt-j-6B/resolve/main/config.json',
    # See all GPT-J models at https://huggingface.co/models?filter=gpt_j
}






class 							lowercase__     (							lowercase       ):
	lowercase__      =      """gptj"""
	lowercase__      =      {
	    """max_position_embeddings""": """n_positions""",
	    """hidden_size""": """n_embd""",
	    """num_attention_heads""": """n_head""",
	    """num_hidden_layers""": """n_layer""",
	}





	def __init__(			self :    Any			,lowerCamelCase__ :    Optional[Any]=50400			,lowerCamelCase__ :    Tuple=2048			,lowerCamelCase__ :    Tuple=4096			,lowerCamelCase__ :    int=28			,lowerCamelCase__ :    Optional[Any]=16			,lowerCamelCase__ :    Optional[Any]=64			,lowerCamelCase__ :    List[Any]=None			,lowerCamelCase__ :    List[Any]="gelu_new"			,lowerCamelCase__ :    Optional[Any]=0.0			,lowerCamelCase__ :    List[str]=0.0			,lowerCamelCase__ :    List[Any]=0.0			,lowerCamelCase__ :    Tuple=1E-5			,lowerCamelCase__ :    int=0.0_2			,lowerCamelCase__ :    Any=True			,lowerCamelCase__ :    str=50256			,lowerCamelCase__ :    Any=50256			,lowerCamelCase__ :    Tuple=False			,**lowerCamelCase__ :    Optional[Any]			,):







							'''simple docstring'''
							_UpperCamelCase :		Optional[Any]			    =       vocab_size
							_UpperCamelCase :		Optional[Any]			    =       n_positions
							_UpperCamelCase :		Union[str, Any]			    =       n_embd
							_UpperCamelCase :		Any			    =       n_layer
							_UpperCamelCase :		Optional[int]			    =       n_head
							_UpperCamelCase :		List[str]			    =       n_inner
							_UpperCamelCase :		List[Any]			    =       rotary_dim
							_UpperCamelCase :		int			    =       activation_function
							_UpperCamelCase :		Dict			    =       resid_pdrop
							_UpperCamelCase :		Any			    =       embd_pdrop
							_UpperCamelCase :		Union[str, Any]			    =       attn_pdrop
							_UpperCamelCase :		Union[str, Any]			    =       layer_norm_epsilon
							_UpperCamelCase :		Optional[Any]			    =       initializer_range
							_UpperCamelCase :		str			    =       use_cache

							_UpperCamelCase :		Union[str, Any]			    =       bos_token_id
							_UpperCamelCase :		Any			    =       eos_token_id

							super().__init__(
							    bos_token_id=lowerCamelCase__			,eos_token_id=lowerCamelCase__			,tie_word_embeddings=lowerCamelCase__			,**lowerCamelCase__			)






class 							lowercase__     (							lowercase       ):





	def __init__(			self :    Tuple			,lowerCamelCase__ :    PretrainedConfig			,lowerCamelCase__ :    str = "default"			,lowerCamelCase__ :    List[PatchingSpec] = None			,lowerCamelCase__ :    bool = False			,):







							'''simple docstring'''
							super().__init__(lowerCamelCase__			,task=lowerCamelCase__			,patching_specs=lowerCamelCase__			,use_past=lowerCamelCase__			)
							if not getattr(self._config			,'pad_token_id'			,lowerCamelCase__			):
													# TODO: how to do that better?
													_UpperCamelCase :		int			    =       0





	@property
	def      UpperCamelCase_  (			self :    Optional[Any]			):







							'''simple docstring'''
							_UpperCamelCase :		List[str]			    =       OrderedDict({'input_ids': {0: 'batch', 1: 'sequence'}}			)
							if self.use_past:
													self.fill_with_past_key_values_(lowerCamelCase__			,direction='inputs'			)
													_UpperCamelCase :		Tuple			    =       {0: 'batch', 1: 'past_sequence + sequence'}
							else:
													_UpperCamelCase :		Any			    =       {0: 'batch', 1: 'sequence'}

							return common_inputs





	@property
	def      UpperCamelCase_  (			self :    Tuple			):







							'''simple docstring'''
							return self._config.n_layer





	@property
	def      UpperCamelCase_  (			self :    List[str]			):







							'''simple docstring'''
							return self._config.n_head





	def      UpperCamelCase_  (			self :    List[str]			,lowerCamelCase__ :    PreTrainedTokenizer			,lowerCamelCase__ :    int = -1			,lowerCamelCase__ :    int = -1			,lowerCamelCase__ :    bool = False			,lowerCamelCase__ :    Optional[TensorType] = None			,):







							'''simple docstring'''
							_UpperCamelCase :		Union[str, Any]			    =       super(lowerCamelCase__			,self			).generate_dummy_inputs(
							    lowerCamelCase__			,batch_size=lowerCamelCase__			,seq_length=lowerCamelCase__			,is_pair=lowerCamelCase__			,framework=lowerCamelCase__			)

							# We need to order the input in the way they appears in the forward()
							_UpperCamelCase :		Tuple			    =       OrderedDict({'input_ids': common_inputs['input_ids']}			)

							# Need to add the past_keys
							if self.use_past:
													if not is_torch_available():
																			raise ValueError('Cannot generate dummy past_keys inputs without PyTorch installed.'			)
													else:
																			import torch

																			_UpperCamelCase    ,      _UpperCamelCase :		str			    =       common_inputs['input_ids'].shape
																			# Not using the same length for past_key_values
																			_UpperCamelCase :		Optional[int]			    =       seqlen + 2
																			_UpperCamelCase :		List[Any]			    =       (
																			    batch,
																			    self.num_attention_heads,
																			    past_key_values_length,
																			    self._config.hidden_size // self.num_attention_heads,
																			)
																			_UpperCamelCase :		Optional[Any]			    =       [
																			    (torch.zeros(lowerCamelCase__			), torch.zeros(lowerCamelCase__			)) for _ in range(self.num_layers			)
																			]

							_UpperCamelCase :		Union[str, Any]			    =       common_inputs['attention_mask']
							if self.use_past:
													_UpperCamelCase :		Any			    =       ordered_inputs['attention_mask'].dtype
													_UpperCamelCase :		List[str]			    =       torch.cat(
													    [ordered_inputs['attention_mask'], torch.ones(lowerCamelCase__			,lowerCamelCase__			,dtype=lowerCamelCase__			)]			,dim=1			)

							return ordered_inputs





	@property
	def      UpperCamelCase_  (			self :    str			):







							'''simple docstring'''
							return 13


