


import sys
from collections.abc import Mapping

import numpy as np
import pyarrow as pa

from .. import config
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter







class 			snake_case__(							TensorFormatter[Mapping, np.ndarray, Mapping]							):







						'''simple docstring'''




						def __init__(			self				,  __lowercase=None				,  **__lowercase							)   ->      List[str]:
										super().__init__(features=__lowercase							)
										lowerCAmelCase_	: List[Any]      			=			np_array_kwargs




						def   lowercase_	(			self				,  __lowercase							)   ->      str:
										if isinstance(__lowercase				,  __lowercase							):
														if column and all(
														    isinstance(__lowercase				,  np.ndarray							) and x.shape == column[0].shape and x.dtype == column[0].dtype for x in column							):
																		return np.stack(__lowercase							)
														else:
																		# don't use np.array(column, dtype=object)
																		# since it fails in certain cases
																		# see https://stackoverflow.com/q/51005699
																		lowerCAmelCase_	: Any      			=			np.empty(len(__lowercase							)				,  dtype=__lowercase							)
																		lowerCAmelCase_	: Optional[Any]      			=			column
																		return out
										return column




						def   lowercase_	(			self				,  __lowercase							)   ->      int:
										if isinstance(__lowercase				,  (str, bytes, type(__lowercase							))							):
														return value
										elif isinstance(__lowercase				,  (np.character, np.ndarray)							) and np.issubdtype(value.dtype				,  np.character							):
														return value
										elif isinstance(__lowercase				,  np.number							):
														return value

										lowerCAmelCase_	: Optional[Any]      			=			{}

										if isinstance(__lowercase				,  np.ndarray							) and np.issubdtype(value.dtype				,  np.integer							):
														lowerCAmelCase_	: Tuple      			=			{'''dtype''': np.intaa}
										elif isinstance(__lowercase				,  np.ndarray							) and np.issubdtype(value.dtype				,  np.floating							):
														lowerCAmelCase_	: List[Any]      			=			{'''dtype''': np.floataa}
										elif config.PIL_AVAILABLE and "PIL" in sys.modules:
														import PIL.Image

														if isinstance(__lowercase				,  PIL.Image.Image							):
																		return np.asarray(__lowercase				,  **self.np_array_kwargs							)

										return np.asarray(__lowercase				,  **{**default_dtype, **self.np_array_kwargs}							)




						def   lowercase_	(			self				,  __lowercase							)   ->      Optional[int]:
										# support for torch, tf, jax etc.
										if config.TORCH_AVAILABLE and "torch" in sys.modules:
														import torch

														if isinstance(__lowercase				,  torch.Tensor							):
																		return self._tensorize(data_struct.detach().cpu().numpy()[()]							)
										if hasattr(__lowercase				,  '''__array__'''							) and not isinstance(__lowercase				,  (np.ndarray, np.character, np.number)							):
														lowerCAmelCase_	: str      			=			data_struct.__array__()
										# support for nested types like struct of list of struct
										if isinstance(__lowercase				,  np.ndarray							):
														if data_struct.dtype == object:
																		return self._consolidate([self.recursive_tensorize(__lowercase							) for substruct in data_struct]							)
										if isinstance(__lowercase				,  (list, tuple)							):
														return self._consolidate([self.recursive_tensorize(__lowercase							) for substruct in data_struct]							)
										return self._tensorize(__lowercase							)




						def   lowercase_	(			self				,  __lowercase							)   ->      Tuple:
										return map_nested(self._recursive_tensorize				,  __lowercase				,  map_list=__lowercase							)




						def   lowercase_	(			self				,  __lowercase							)   ->      Mapping:
										lowerCAmelCase_	: List[Any]      			=			self.numpy_arrow_extractor().extract_row(__lowercase							)
										lowerCAmelCase_	: int      			=			self.python_features_decoder.decode_row(__lowercase							)
										return self.recursive_tensorize(__lowercase							)




						def   lowercase_	(			self				,  __lowercase							)   ->      np.ndarray:
										lowerCAmelCase_	: Any      			=			self.numpy_arrow_extractor().extract_column(__lowercase							)
										lowerCAmelCase_	: int      			=			self.python_features_decoder.decode_column(__lowercase				,  pa_table.column_names[0]							)
										lowerCAmelCase_	: Optional[int]      			=			self.recursive_tensorize(__lowercase							)
										lowerCAmelCase_	: Union[str, Any]      			=			self._consolidate(__lowercase							)
										return column




						def   lowercase_	(			self				,  __lowercase							)   ->      Mapping:
										lowerCAmelCase_	: Union[str, Any]      			=			self.numpy_arrow_extractor().extract_batch(__lowercase							)
										lowerCAmelCase_	: Optional[int]      			=			self.python_features_decoder.decode_batch(__lowercase							)
										lowerCAmelCase_	: Union[str, Any]      			=			self.recursive_tensorize(__lowercase							)
										for column_name in batch:
														lowerCAmelCase_	: Tuple      			=			self._consolidate(batch[column_name]							)
										return batch



import argparse
import collections
import importlib.util
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_table.py
_UpperCAmelCase     :					List[Any]       ="""src/diffusers"""
_UpperCAmelCase     :					Dict       ="""docs/source/en"""
_UpperCAmelCase     :					List[str]       ="""."""



def      lowerCAmelCase	(						lowerCAmelCase_	,				lowerCAmelCase_	,				lowerCAmelCase_	)->	List[str]:
				with open(lowerCAmelCase_	,				'''r'''	,				encoding='''utf-8'''	,				newline='''\n'''	) as f:
								lowerCAmelCase_	: Any      			=			f.readlines()
				# Find the start prompt.
				lowerCAmelCase_	: Optional[Any]      			=			0
				while not lines[start_index].startswith(lowerCAmelCase_	):
								start_index += 1
				start_index += 1

				lowerCAmelCase_	: List[Any]      			=			start_index
				while not lines[end_index].startswith(lowerCAmelCase_	):
								end_index += 1
				end_index -= 1

				while len(lines[start_index]	) <= 1:
								start_index += 1
				while len(lines[end_index]	) <= 1:
								end_index -= 1
				end_index += 1
				return "".join(lines[start_index:end_index]	), start_index, end_index, lines


# Add here suffixes that are used to identify models, separated by |
_UpperCAmelCase     :					int       ="""Model|Encoder|Decoder|ForConditionalGeneration"""
# Regexes that match TF/Flax/PT model names.
_UpperCAmelCase     :					Any       =re.compile(R"""TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)""")
_UpperCAmelCase     :					Optional[int]       =re.compile(R"""Flax(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)""")
# Will match any TF or Flax model too so need to be in an else branch afterthe two previous regexes.
_UpperCAmelCase     :					Union[str, Any]       =re.compile(R"""(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)""")


# This is to make sure the diffusers module imported is the one in the repo.
_UpperCAmelCase     :					Dict       =importlib.util.spec_from_file_location(
    """diffusers""",
    os.path.join(TRANSFORMERS_PATH, """__init__.py"""),
    submodule_search_locations=[TRANSFORMERS_PATH],
)
_UpperCAmelCase     :					Tuple       =spec.loader.load_module()



def      lowerCAmelCase	(						lowerCAmelCase_	)->	Dict:
				lowerCAmelCase_	: Any      			=			re.finditer('''.+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)'''	,				lowerCAmelCase_	)
				return [m.group(0	) for m in matches]



def      lowerCAmelCase	(						lowerCAmelCase_	,				lowerCAmelCase_	)->	Any:
				lowerCAmelCase_	: Any      			=			2 if text == '''✅''' or text == '''❌''' else len(lowerCAmelCase_	)
				lowerCAmelCase_	: List[str]      			=			(width - text_length) // 2
				lowerCAmelCase_	: str      			=			width - text_length - left_indent
				return " " * left_indent + text + " " * right_indent



def      lowerCAmelCase	(						)->	Dict:
				lowerCAmelCase_	: Optional[int]      			=			diffusers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
				lowerCAmelCase_	: Tuple      			=			{
				    name: config_mapping_names[code]
				    for code, name in diffusers_module.MODEL_NAMES_MAPPING.items()
				    if code in config_mapping_names
				}
				lowerCAmelCase_	: str      			=			{name: config.replace('''ConfigMixin'''	,				''''''	) for name, config in model_name_to_config.items()}

				# Dictionaries flagging if each model prefix has a slow/fast tokenizer, backend in PT/TF/Flax.
				lowerCAmelCase_	: Union[str, Any]      			=			collections.defaultdict(lowerCAmelCase_	)
				lowerCAmelCase_	: Dict      			=			collections.defaultdict(lowerCAmelCase_	)
				lowerCAmelCase_	: Tuple      			=			collections.defaultdict(lowerCAmelCase_	)
				lowerCAmelCase_	: Optional[int]      			=			collections.defaultdict(lowerCAmelCase_	)
				lowerCAmelCase_	: Any      			=			collections.defaultdict(lowerCAmelCase_	)

				# Let's lookup through all diffusers object (once).
				for attr_name in dir(lowerCAmelCase_	):
								lowerCAmelCase_	: Tuple      			=			None
								if attr_name.endswith('''Tokenizer'''	):
												lowerCAmelCase_	: int      			=			slow_tokenizers
												lowerCAmelCase_	: Optional[Any]      			=			attr_name[:-9]
								elif attr_name.endswith('''TokenizerFast'''	):
												lowerCAmelCase_	: List[Any]      			=			fast_tokenizers
												lowerCAmelCase_	: Dict      			=			attr_name[:-13]
								elif _re_tf_models.match(lowerCAmelCase_	) is not None:
												lowerCAmelCase_	: Union[str, Any]      			=			tf_models
												lowerCAmelCase_	: Optional[int]      			=			_re_tf_models.match(lowerCAmelCase_	).groups()[0]
								elif _re_flax_models.match(lowerCAmelCase_	) is not None:
												lowerCAmelCase_	: Optional[Any]      			=			flax_models
												lowerCAmelCase_	: Optional[int]      			=			_re_flax_models.match(lowerCAmelCase_	).groups()[0]
								elif _re_pt_models.match(lowerCAmelCase_	) is not None:
												lowerCAmelCase_	: Tuple      			=			pt_models
												lowerCAmelCase_	: Tuple      			=			_re_pt_models.match(lowerCAmelCase_	).groups()[0]

								if lookup_dict is not None:
												while len(lowerCAmelCase_	) > 0:
																if attr_name in model_name_to_prefix.values():
																				lowerCAmelCase_	: Dict      			=			True
																				break
																# Try again after removing the last word in the name
																lowerCAmelCase_	: Tuple      			=			''''''.join(camel_case_split(lowerCAmelCase_	)[:-1]	)

    # Let's build that table!
				lowerCAmelCase_	: Union[str, Any]      			=			list(model_name_to_config.keys()	)
				model_names.sort(key=str.lower	)
				lowerCAmelCase_	: Tuple      			=			['''Model''', '''Tokenizer slow''', '''Tokenizer fast''', '''PyTorch support''', '''TensorFlow support''', '''Flax Support''']
				# We'll need widths to properly display everything in the center (+2 is to leave one extra space on each side).
				lowerCAmelCase_	: Optional[Any]      			=			[len(lowerCAmelCase_	) + 2 for c in columns]
				lowerCAmelCase_	: List[Any]      			=			max([len(lowerCAmelCase_	) for name in model_names]	) + 2

				# Build the table per se
				lowerCAmelCase_	: List[str]      			=			'''|''' + '''|'''.join([_center_text(lowerCAmelCase_	,				lowerCAmelCase_	) for c, w in zip(lowerCAmelCase_	,				lowerCAmelCase_	)]	) + '''|\n'''
				# Use ":-----:" format to center-aligned table cell texts
				table += "|" + "|".join([''':''' + '''-''' * (w - 2) + ''':''' for w in widths]	) + "|\n"

				lowerCAmelCase_	: Tuple      			=			{True: '''✅''', False: '''❌'''}
				for name in model_names:
								lowerCAmelCase_	: Optional[int]      			=			model_name_to_prefix[name]
								lowerCAmelCase_	: int      			=			[
								    name,
								    check[slow_tokenizers[prefix]],
								    check[fast_tokenizers[prefix]],
								    check[pt_models[prefix]],
								    check[tf_models[prefix]],
								    check[flax_models[prefix]],
								]
								table += "|" + "|".join([_center_text(lowerCAmelCase_	,				lowerCAmelCase_	) for l, w in zip(lowerCAmelCase_	,				lowerCAmelCase_	)]	) + "|\n"
				return table



def      lowerCAmelCase	(						lowerCAmelCase_=False	)->	str:
				lowerCAmelCase_			,					lowerCAmelCase_			,					lowerCAmelCase_			,					lowerCAmelCase_	: Optional[int]      			=			_find_text_in_file(
				    filename=os.path.join(lowerCAmelCase_	,				'''index.mdx'''	)	,				start_prompt='''<!--This table is updated automatically from the auto modules'''	,				end_prompt='''<!-- End table-->'''	,				)
				lowerCAmelCase_	: int      			=			get_model_table_from_auto_modules()

				if current_table != new_table:
								if overwrite:
												with open(os.path.join(lowerCAmelCase_	,				'''index.mdx'''	)	,				'''w'''	,				encoding='''utf-8'''	,				newline='''\n'''	) as f:
																f.writelines(lines[:start_index] + [new_table] + lines[end_index:]	)
								else:
												raise ValueError(
												    '''The model table in the `index.mdx` has not been updated. Run `make fix-copies` to fix this.'''	)


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

						check_model_table(args.fix_and_overwrite)