


"""simple docstring"""






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
lowercase  		=			"""src/diffusers"""
lowercase  		=			"""docs/source/en"""
lowercase  		=			"""."""



def 			A__		(					_UpperCAmelCase    :      Optional[Any]				,    _UpperCAmelCase    :      Optional[int]				,    _UpperCAmelCase    :      str	)							->	Optional[Any]:

	'''simple docstring'''


	with open(_UpperCAmelCase				,    "r"				,    encoding="utf-8"				,    newline="\n"	) as f:
		snake_case__  :						Any		=      f.readlines()
	# Find the start prompt.
	snake_case__  :						Optional[Any]		=      0
	while not lines[start_index].startswith(_UpperCAmelCase	):
		start_index += 1
	start_index += 1

	snake_case__  :						Union[str, Any]		=      start_index
	while not lines[end_index].startswith(_UpperCAmelCase	):
		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 |
lowercase  		=			"""Model|Encoder|Decoder|ForConditionalGeneration"""
# Regexes that match TF/Flax/PT model names.
lowercase  		=			re.compile(r"""TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)""")
lowercase  		=			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.
lowercase  		=			re.compile(r"""(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)""")


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



def 			A__		(					_UpperCAmelCase    :      Union[str, Any]	)							->	Tuple:

	'''simple docstring'''


	snake_case__  :						List[Any]		=      re.finditer(".+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)"				,    _UpperCAmelCase	)
	return [m.group(0	) for m in matches]



def 			A__		(					_UpperCAmelCase    :      Any				,    _UpperCAmelCase    :      List[str]	)							->	Optional[int]:

	'''simple docstring'''


	snake_case__  :						List[Any]		=      2 if text == "✅" or text == "❌" else len(_UpperCAmelCase	)
	snake_case__  :						Optional[Any]		=      (width - text_length) // 2
	snake_case__  :						Union[str, Any]		=      width - text_length - left_indent
	return " " * left_indent + text + " " * right_indent



def 			A__		(					)							->	Any:

	'''simple docstring'''


	snake_case__  :						Optional[Any]		=      diffusers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
	snake_case__  :						Optional[int]		=      {
	    name: config_mapping_names[code]
	    for code, name in diffusers_module.MODEL_NAMES_MAPPING.items()
	    if code in config_mapping_names
	}
	snake_case__  :						int		=      {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.
	snake_case__  :						Optional[Any]		=      collections.defaultdict(_UpperCAmelCase	)
	snake_case__  :						Optional[Any]		=      collections.defaultdict(_UpperCAmelCase	)
	snake_case__  :						Tuple		=      collections.defaultdict(_UpperCAmelCase	)
	snake_case__  :						List[Any]		=      collections.defaultdict(_UpperCAmelCase	)
	snake_case__  :						List[Any]		=      collections.defaultdict(_UpperCAmelCase	)

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

		if lookup_dict is not None:
			while len(_UpperCAmelCase	) > 0:
				if attr_name in model_name_to_prefix.values():
					snake_case__  :						Optional[Any]		=      True
					break
				# Try again after removing the last word in the name
				snake_case__  :						List[str]		=      "".join(camel_case_split(_UpperCAmelCase	)[:-1]	)

    # Let's build that table!
	snake_case__  :						Union[str, Any]		=      list(model_name_to_config.keys()	)
	model_names.sort(key=str.lower	)
	snake_case__  :						Optional[Any]		=      ["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).
	snake_case__  :						Union[str, Any]		=      [len(_UpperCAmelCase	) + 2 for c in columns]
	snake_case__  :						Union[str, Any]		=      max([len(_UpperCAmelCase	) for name in model_names]	) + 2

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

	snake_case__  :						Union[str, Any]		=      {True: "✅", False: "❌"}
	for name in model_names:
		snake_case__  :						str		=      model_name_to_prefix[name]
		snake_case__  :						Union[str, Any]		=      [
		    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(_UpperCAmelCase				,    _UpperCAmelCase	) for l, w in zip(_UpperCAmelCase				,    _UpperCAmelCase	)]	) + "|\n"
	return table



def 			A__		(					_UpperCAmelCase    :      str=False	)							->	List[Any]:

	'''simple docstring'''


	snake_case__,					snake_case__,					snake_case__,					snake_case__  :						str		=      _find_text_in_file(
	    filename=os.path.join(_UpperCAmelCase				,    "index.mdx"	)				,    start_prompt="<!--This table is updated automatically from the auto modules"				,    end_prompt="<!-- End table-->"				,    )
	snake_case__  :						Any		=      get_model_table_from_auto_modules()

	if current_table != new_table:
		if overwrite:
			with open(os.path.join(_UpperCAmelCase				,    "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__":
					lowercase  		=			argparse.ArgumentParser()
					parser.add_argument("""--fix_and_overwrite""", action="""store_true""", help="""Whether to fix inconsistencies.""")
					lowercase  		=			parser.parse_args()

					check_model_table(args.fix_and_overwrite)











"""simple docstring"""

# This Python program implements an optimal binary search tree (abbreviated BST)
# building dynamic programming algorithm that delivers O(n^2) performance.
#
# The goal of the optimal BST problem is to build a low-cost BST for a
# given set of nodes, each with its own key and frequency. The frequency
# of the node is defined as how many time the node is being searched.
# The search cost of binary search tree is given by this formula:
#
# cost(1, n) = sum{i = 1 to n}((depth(node_i) + 1) * node_i_freq)
#
# where n is number of nodes in the BST. The characteristic of low-cost
# BSTs is having a faster overall search time than other implementations.
# The reason for their fast search time is that the nodes with high
# frequencies will be placed near the root of the tree while the nodes
# with low frequencies will be placed near the leaves of the tree thus
# reducing search time in the most frequent instances.
import sys
from random import randint

class 							lowercase   :







				def __init__(				self :       Optional[int]   ,  _lowerCamelCase :       str   ,  _lowerCamelCase :       Optional[Any]							):



					"""simple docstring"""

					A_	:					Union[str, Any]						 =		key
					A_	:					str						 =		freq







				def __str__(				self :       Optional[Any]							):



					"""simple docstring"""

					return F"""Node(key={self.key}, freq={self.freq})"""

def 		lowercase_   (		_UpperCAmelCase	,   _UpperCAmelCase	,   _UpperCAmelCase	,   _UpperCAmelCase	,   _UpperCAmelCase	,   _UpperCAmelCase						):




	"""simple docstring"""






	if i > j or i < 0 or j > len(_UpperCAmelCase						) - 1:
		return

	A_	:					Union[str, Any]						 =		root[i][j]
	if parent == -1:  # root does not have a parent
		print(f"""{key[node]} is the root of the binary search tree."""						)
	elif is_left:
		print(f"""{key[node]} is the left child of key {parent}."""						)
	else:
		print(f"""{key[node]} is the right child of key {parent}."""						)

	print_binary_search_tree(_UpperCAmelCase	,   _UpperCAmelCase	,   _UpperCAmelCase	,   node - 1	,   key[node]	,   _UpperCAmelCase						)
	print_binary_search_tree(_UpperCAmelCase	,   _UpperCAmelCase	,   node + 1	,   _UpperCAmelCase	,   key[node]	,   _UpperCAmelCase						)

def 		lowercase_   (		_UpperCAmelCase						):




	"""simple docstring"""






	nodes.sort(key=lambda _UpperCAmelCase						: node.key						)

	A_	:					Any						 =		len(_UpperCAmelCase						)

	A_	:					int						 =		[nodes[i].key for i in range(_UpperCAmelCase						)]
	A_	:					List[str]						 =		[nodes[i].freq for i in range(_UpperCAmelCase						)]

	# This 2D array stores the overall tree cost (which's as minimized as possible);
	# for a single key, cost is equal to frequency of the key.
	A_	:					List[str]						 =		[[freqs[i] if i == j else 0 for j in range(_UpperCAmelCase						)] for i in range(_UpperCAmelCase						)]
	# sum[i][j] stores the sum of key frequencies between i and j inclusive in nodes
	# array
	A_	:					Optional[int]						 =		[[freqs[i] if i == j else 0 for j in range(_UpperCAmelCase						)] for i in range(_UpperCAmelCase						)]
	# stores tree roots that will be used later for constructing binary search tree
	A_	:					List[Any]						 =		[[i if i == j else 0 for j in range(_UpperCAmelCase						)] for i in range(_UpperCAmelCase						)]

	for interval_length in range(2	,   n + 1						):
		for i in range(n - interval_length + 1						):
			A_	:					List[Any]						 =		i + interval_length - 1

			A_	:					List[Any]						 =		sys.maxsize  # set the value to "infinity"
			A_	:					Union[str, Any]						 =		total[i][j - 1] + freqs[j]

			# Apply Knuth's optimization
			# Loop without optimization: for r in range(i, j + 1):
			for r in range(root[i][j - 1]	,   root[i + 1][j] + 1						):  # r is a temporal root
				A_	:					Tuple						 =		dp[i][r - 1] if r != i else 0  # optimal cost for left subtree
				A_	:					List[str]						 =		dp[r + 1][j] if r != j else 0  # optimal cost for right subtree
				A_	:					Optional[int]						 =		left + total[i][j] + right

				if dp[i][j] > cost:
					A_	:					Dict						 =		cost
					A_	:					List[str]						 =		r

	print('''Binary search tree nodes:'''						)
	for node in nodes:
		print(_UpperCAmelCase						)

	print(f"""\nThe cost of optimal BST for given tree nodes is {dp[0][n - 1]}."""						)
	print_binary_search_tree(_UpperCAmelCase	,   _UpperCAmelCase	,   0	,   n - 1	,   -1	,   _UpperCAmelCase						)

def 		lowercase_   (		):




	"""simple docstring"""






	A_	:					List[Any]						 =		[Node(_UpperCAmelCase	,   randint(1	,   50						)						) for i in range(10	,   0	,   -1						)]
	find_optimal_binary_search_tree(_UpperCAmelCase						)


if __name__ == "__main__":
		main()





