



import argparse
import collections
import os
import re

from transformers.utils import direct_transformers_import


# 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__   : Tuple					  = """src/transformers"""
UpperCamelCase__   : List[Any]					  = """docs/source/en"""
UpperCamelCase__   : Tuple					  = """."""




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

	UpperCAmelCase_								=     start_index
	while not lines[end_index].startswith(A   ):
		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__   : Optional[Any]					  = """Model|Encoder|Decoder|ForConditionalGeneration"""
# Regexes that match TF/Flax/PT model names.
UpperCamelCase__   : List[Any]					  = re.compile(r"""TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)""")
UpperCamelCase__   : str					  = 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__   : int					  = re.compile(r"""(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)""")


# This is to make sure the transformers module imported is the one in the repo.
UpperCamelCase__   : Tuple					  = direct_transformers_import(TRANSFORMERS_PATH)




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




def    A_(			A				,      A   ):
	UpperCAmelCase_								=     2 if text == """✅""" or text == """❌""" else len(A   )
	UpperCAmelCase_								=     (width - text_length) // 2
	UpperCAmelCase_								=     width - text_length - left_indent
	return " " * left_indent + text + " " * right_indent




def    A_(			):
	UpperCAmelCase_								=     transformers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
	UpperCAmelCase_								=     {
	    name: config_maping_names[code]
	    for code, name in transformers_module.MODEL_NAMES_MAPPING.items()
	    if code in config_maping_names
	}
	UpperCAmelCase_								=     {name: config.replace("""Config"""				,      """"""   ) 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.
	UpperCAmelCase_								=     collections.defaultdict(A   )
	UpperCAmelCase_								=     collections.defaultdict(A   )
	UpperCAmelCase_								=     collections.defaultdict(A   )
	UpperCAmelCase_								=     collections.defaultdict(A   )
	UpperCAmelCase_								=     collections.defaultdict(A   )

	# Let's lookup through all transformers object (once).
	for attr_name in dir(A   ):
		UpperCAmelCase_								=     None
		if attr_name.endswith("""Tokenizer"""   ):
			UpperCAmelCase_								=     slow_tokenizers
			UpperCAmelCase_								=     attr_name[:-9]
		elif attr_name.endswith("""TokenizerFast"""   ):
			UpperCAmelCase_								=     fast_tokenizers
			UpperCAmelCase_								=     attr_name[:-13]
		elif _re_tf_models.match(A   ) is not None:
			UpperCAmelCase_								=     tf_models
			UpperCAmelCase_								=     _re_tf_models.match(A   ).groups()[0]
		elif _re_flax_models.match(A   ) is not None:
			UpperCAmelCase_								=     flax_models
			UpperCAmelCase_								=     _re_flax_models.match(A   ).groups()[0]
		elif _re_pt_models.match(A   ) is not None:
			UpperCAmelCase_								=     pt_models
			UpperCAmelCase_								=     _re_pt_models.match(A   ).groups()[0]

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

    # Let's build that table!
	UpperCAmelCase_								=     list(model_name_to_config.keys()   )
	model_names.sort(key=str.lower   )
	UpperCAmelCase_								=     ["""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).
	UpperCAmelCase_								=     [len(A   ) + 2 for c in columns]
	UpperCAmelCase_								=     max([len(A   ) for name in model_names]   ) + 2

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

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




def    A_(			A=False   ):
	UpperCAmelCase_						,	UpperCAmelCase_						,	UpperCAmelCase_						,	UpperCAmelCase_								=     _find_text_in_file(
	    filename=os.path.join(A				,      """index.md"""   )				,      start_prompt="""<!--This table is updated automatically from the auto modules"""				,      end_prompt="""<!-- End table-->"""				,      )
	UpperCAmelCase_								=     get_model_table_from_auto_modules()

	if current_table != new_table:
		if overwrite:
			with open(os.path.join(A				,      """index.md"""   )				,      """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.md` 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__   : List[Any]					  = parser.parse_args()

				check_model_table(args.fix_and_overwrite)









from __future__ import annotations

UpperCamelCase__   : str					  = [
    [-1, 0],  # left
    [0, -1],  # down
    [1, 0],  # right
    [0, 1],  # up
]




def    A_(			A				,      A				,      A				,      A				,      A				,      ):
	UpperCAmelCase_								=     [
	    [0 for col in range(len(grid[0]   )   )] for row in range(len(A   )   )
	]  # the reference grid
	UpperCAmelCase_								=     1
	UpperCAmelCase_								=     [
	    [0 for col in range(len(grid[0]   )   )] for row in range(len(A   )   )
	]  # the action grid

	UpperCAmelCase_								=     init[0]
	UpperCAmelCase_								=     init[1]
	UpperCAmelCase_								=     0
	UpperCAmelCase_								=     g + heuristic[x][y]  # cost from starting cell to destination cell
	UpperCAmelCase_								=     [[f, g, x, y]]

	UpperCAmelCase_								=     False  # flag that is set when search is complete
	UpperCAmelCase_								=     False  # flag set if we can't find expand

	while not found and not resign:
		if len(A   ) == 0:
			raise ValueError("""Algorithm is unable to find solution"""   )
		else:  # to choose the least costliest action so as to move closer to the goal
			cell.sort()
			cell.reverse()
			UpperCAmelCase_								=     cell.pop()
			UpperCAmelCase_								=     next_cell[2]
			UpperCAmelCase_								=     next_cell[3]
			UpperCAmelCase_								=     next_cell[1]

			if x == goal[0] and y == goal[1]:
				UpperCAmelCase_								=     True
			else:
				for i in range(len(A   )   ):  # to try out different valid actions
					UpperCAmelCase_								=     x + DIRECTIONS[i][0]
					UpperCAmelCase_								=     y + DIRECTIONS[i][1]
					if xa >= 0 and xa < len(A   ) and ya >= 0 and ya < len(grid[0]   ):
						if closed[xa][ya] == 0 and grid[xa][ya] == 0:
							UpperCAmelCase_								=     g + cost
							UpperCAmelCase_								=     ga + heuristic[xa][ya]
							cell.append([fa, ga, xa, ya]   )
							UpperCAmelCase_								=     1
							UpperCAmelCase_								=     i
	UpperCAmelCase_								=     []
	UpperCAmelCase_								=     goal[0]
	UpperCAmelCase_								=     goal[1]
	invpath.append([x, y]   )  # we get the reverse path from here
	while x != init[0] or y != init[1]:
		UpperCAmelCase_								=     x - DIRECTIONS[action[x][y]][0]
		UpperCAmelCase_								=     y - DIRECTIONS[action[x][y]][1]
		UpperCAmelCase_								=     xa
		UpperCAmelCase_								=     ya
		invpath.append([x, y]   )

	UpperCAmelCase_								=     []
	for i in range(len(A   )   ):
		path.append(invpath[len(A   ) - 1 - i]   )
	return path, action


if __name__ == "__main__":
				UpperCamelCase__   : Tuple					  = [
				    [0, 1, 0, 0, 0, 0],
				    [0, 1, 0, 0, 0, 0],  # 0 are free path whereas 1's are obstacles
				    [0, 1, 0, 0, 0, 0],
				    [0, 1, 0, 0, 1, 0],
				    [0, 0, 0, 0, 1, 0],
				]

				UpperCamelCase__   : Dict					  = [0, 0]
				# all coordinates are given in format [y,x]
				UpperCamelCase__   : Optional[int]					  = [len(grid) - 1, len(grid[0]) - 1]
				UpperCamelCase__   : Optional[Any]					  = 1

				# the cost map which pushes the path closer to the goal
				UpperCamelCase__   : Any					  = [[0 for row in range(len(grid[0]))] for col in range(len(grid))]
				for i in range(len(grid)):
								for j in range(len(grid[0])):
												UpperCamelCase__   : Any					  = abs(i - goal[0]) + abs(j - goal[1])
												if grid[i][j] == 1:
																# added extra penalty in the heuristic map
																UpperCamelCase__   : Dict					  = 99

				UpperCamelCase__  ,			UpperCamelCase__   : Dict					  = search(grid, init, goal, cost, heuristic)

				print("""ACTION MAP""")
				for i in range(len(action)):
								print(action[i])

				for i in range(len(path)):
								print(path[i])


