





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
lowerCAmelCase_        =							'''src/diffusers'''

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


lowerCAmelCase_        =							'''
{0} = None
'''

lowerCAmelCase_        =							'''
class {0}(metaclass=DummyObject):
    _backends = {1}

    def __init__(self, *args, **kwargs):
        requires_backends(self, {1})

    @classmethod
    def from_config(cls, *args, **kwargs):
        requires_backends(cls, {1})

    @classmethod
    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, {1})
'''


lowerCAmelCase_        =							'''
def {0}(*args, **kwargs):
    requires_backends({0}, {1})
'''






def 	lowerCamelCase_		(							_UpperCamelCase					)						->		List[str]:






				"""simple docstring"""


				snake_case_		:       Optional[Any]           =    _re_backend.findall(_UpperCamelCase					)
				if len(_UpperCamelCase					) == 0:
								return None

				return "_and_".join(_UpperCamelCase					)






def 	lowerCamelCase_		(							)						->		List[str]:






				"""simple docstring"""


				with open(os.path.join(_UpperCamelCase					,							'''__init__.py'''					)					,							'''r'''					,							encoding='''utf-8'''					,							newline='''\n'''					) as f:
								snake_case_		:       Any           =    f.readlines()

				# Get to the point we do the actual imports for type checking
				snake_case_		:       List[Any]           =    0
				snake_case_		:       Tuple           =    {}
				# 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
								snake_case_		:       int           =    find_backend(lines[line_index]					)
								if backend is not None:
												while not lines[line_index].startswith('''else:'''					):
																line_index += 1
												line_index += 1
												snake_case_		:       List[str]           =    []
												# Until we unindent, add backend objects to the list
												while line_index < len(_UpperCamelCase					) and len(lines[line_index]					) > 1:
																snake_case_		:       Optional[Any]           =    lines[line_index]
																snake_case_		:       List[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:
																snake_case_		:       Union[str, Any]           =    objects
								else:
												line_index += 1

				return backend_specific_objects






def 	lowerCamelCase_		(							_UpperCamelCase					,							_UpperCamelCase					)						->		Tuple:






				"""simple docstring"""


				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 	lowerCamelCase_		(							_UpperCamelCase=None					)						->		List[str]:






				"""simple docstring"""


				if backend_specific_objects is None:
								snake_case_		:       Optional[int]           =    read_init()
				# For special correspondence backend to module name as used in the function requires_modulename
				snake_case_		:       List[str]           =    {}

				for backend, objects in backend_specific_objects.items():
								snake_case_		:       List[str]           =    '''[''' + ''', '''.join(f'''"{b}"''' for b in backend.split('''_and_'''					)					) + ''']'''
								snake_case_		:       str           =    '''# 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]					)
								snake_case_		:       Any           =    dummy_file

				return dummy_files






def 	lowerCamelCase_		(							_UpperCamelCase=False					)						->		Union[str, Any]:






				"""simple docstring"""


				snake_case_		:       Any           =    create_dummy_files()
				# For special correspondence backend to shortcut as used in utils/dummy_xxx_objects.py
				snake_case_		:       Optional[Any]           =    {'''torch''': '''pt'''}

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

				snake_case_		:       Union[str, 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:
																snake_case_		:       Union[str, Any]           =    f.read()
								else:
												snake_case_		:       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__":
	lowerCAmelCase_        =							argparse.ArgumentParser()
	parser.add_argument('''--fix_and_overwrite''', action='''store_true''', help='''Whether to fix inconsistencies.''')
	lowerCAmelCase_        =							parser.parse_args()

	check_dummies(args.fix_and_overwrite)





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


lowerCamelCase		       =      logging.get_logger(__name__)


lowerCamelCase		       =      {
    """Salesforce/codegen-350M-nl""": """https://huggingface.co/Salesforce/codegen-350M-nl/resolve/main/config.json""",
    """Salesforce/codegen-350M-multi""": """https://huggingface.co/Salesforce/codegen-350M-multi/resolve/main/config.json""",
    """Salesforce/codegen-350M-mono""": """https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/config.json""",
    """Salesforce/codegen-2B-nl""": """https://huggingface.co/Salesforce/codegen-2B-nl/resolve/main/config.json""",
    """Salesforce/codegen-2B-multi""": """https://huggingface.co/Salesforce/codegen-2B-multi/resolve/main/config.json""",
    """Salesforce/codegen-2B-mono""": """https://huggingface.co/Salesforce/codegen-2B-mono/resolve/main/config.json""",
    """Salesforce/codegen-6B-nl""": """https://huggingface.co/Salesforce/codegen-6B-nl/resolve/main/config.json""",
    """Salesforce/codegen-6B-multi""": """https://huggingface.co/Salesforce/codegen-6B-multi/resolve/main/config.json""",
    """Salesforce/codegen-6B-mono""": """https://huggingface.co/Salesforce/codegen-6B-mono/resolve/main/config.json""",
    """Salesforce/codegen-16B-nl""": """https://huggingface.co/Salesforce/codegen-16B-nl/resolve/main/config.json""",
    """Salesforce/codegen-16B-multi""": """https://huggingface.co/Salesforce/codegen-16B-multi/resolve/main/config.json""",
    """Salesforce/codegen-16B-mono""": """https://huggingface.co/Salesforce/codegen-16B-mono/resolve/main/config.json""",
}


class 				_a						(       SCREAMING_SNAKE_CASE			):





							'''simple docstring'''

							A				:str =       "codegen"
							A				:Tuple =       {
							    "max_position_embeddings": "n_positions",
							    "hidden_size": "n_embd",
							    "num_attention_heads": "n_head",
							    "num_hidden_layers": "n_layer",
							}



							def __init__(							self ,					__UpperCAmelCase=5_0400 ,					__UpperCAmelCase=2048 ,					__UpperCAmelCase=2048 ,					__UpperCAmelCase=4096 ,					__UpperCAmelCase=28 ,					__UpperCAmelCase=16 ,					__UpperCAmelCase=64 ,					__UpperCAmelCase=None ,					__UpperCAmelCase="gelu_new" ,					__UpperCAmelCase=0.0 ,					__UpperCAmelCase=0.0 ,					__UpperCAmelCase=0.0 ,					__UpperCAmelCase=1E-5 ,					__UpperCAmelCase=0.0_2 ,					__UpperCAmelCase=True ,					__UpperCAmelCase=5_0256 ,					__UpperCAmelCase=5_0256 ,					__UpperCAmelCase=False ,					**__UpperCAmelCase ,					):







											"""simple docstring"""



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

											a__	:							Tuple      						= bos_token_id
											a__	:							List[Any]      						= eos_token_id

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





class 				_a						(       SCREAMING_SNAKE_CASE			):





							'''simple docstring'''

							def __init__(							self ,					__UpperCAmelCase ,					__UpperCAmelCase = "default" ,					__UpperCAmelCase = None ,					__UpperCAmelCase = False ,					):







											"""simple docstring"""



											super().__init__(__UpperCAmelCase ,					task=__UpperCAmelCase ,					patching_specs=__UpperCAmelCase ,					use_past=__UpperCAmelCase				)
											if not getattr(self._config ,					"pad_token_id" ,					__UpperCAmelCase				):
															# TODO: how to do that better?
															a__	:							Union[str, Any]      						= 0



							@property
							def 		_A	(							self				):







											"""simple docstring"""



											a__	:							Optional[Any]      						= OrderedDict({"input_ids": {0: "batch", 1: "sequence"}}				)
											if self.use_past:
															self.fill_with_past_key_values_(__UpperCAmelCase ,					direction="inputs"				)
															a__	:							Any      						= {0: "batch", 1: "past_sequence + sequence"}
											else:
															a__	:							List[Any]      						= {0: "batch", 1: "sequence"}

											return common_inputs



							@property
							def 		_A	(							self				):







											"""simple docstring"""



											return self._config.n_layer



							@property
							def 		_A	(							self				):







											"""simple docstring"""



											return self._config.n_head



							def 		_A	(							self ,					__UpperCAmelCase ,					__UpperCAmelCase = -1 ,					__UpperCAmelCase = -1 ,					__UpperCAmelCase = False ,					__UpperCAmelCase = None ,					):







											"""simple docstring"""



											a__	:							Dict      						= super(__UpperCAmelCase ,					self				).generate_dummy_inputs(
											    __UpperCAmelCase ,					batch_size=__UpperCAmelCase ,					seq_length=__UpperCAmelCase ,					is_pair=__UpperCAmelCase ,					framework=__UpperCAmelCase				)

											# We need to order the input in the way they appears in the forward()
											a__	:							Union[str, Any]      						= 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

																			a__						,   a__	:							Union[str, Any]      						= common_inputs["input_ids"].shape
																			# Not using the same length for past_key_values
																			a__	:							Any      						= seqlen + 2
																			a__	:							str      						= (
																			    batch,
																			    self.num_attention_heads,
																			    past_key_values_length,
																			    self._config.hidden_size // self.num_attention_heads,
																			)
																			a__	:							str      						= [
																			    (torch.zeros(__UpperCAmelCase				), torch.zeros(__UpperCAmelCase				)) for _ in range(self.num_layers				)
																			]

											a__	:							Union[str, Any]      						= common_inputs["attention_mask"]
											if self.use_past:
															a__	:							str      						= ordered_inputs["attention_mask"].dtype
															a__	:							Union[str, Any]      						= torch.cat(
															    [ordered_inputs["attention_mask"], torch.ones(__UpperCAmelCase ,					__UpperCAmelCase ,					dtype=__UpperCAmelCase				)] ,					dim=1				)

											return ordered_inputs




							@property
							def 		_A	(							self				):







											"""simple docstring"""



											return 13


