


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      :						Tuple    =		"src/diffusers"
UpperCAmelCase      :						str    =		"docs/source/en"
UpperCAmelCase      :						Any    =		"."






def   __lowerCamelCase	(       lowerCamelCase__		:	str     ,       lowerCamelCase__		:	List[str]     ,       lowerCamelCase__		:	str			):





			'''simple docstring'''




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

			lowerCamelCase								=     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      :						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      :						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      :						List[str]    =		re.compile(r"(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)")


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






def   __lowerCamelCase	(       lowerCamelCase__		:	List[str]			):





			'''simple docstring'''




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






def   __lowerCamelCase	(       lowerCamelCase__		:	Tuple     ,       lowerCamelCase__		:	Optional[Any]			):





			'''simple docstring'''




			lowerCamelCase								=     2 if text == """✅""" or text == """❌""" else len(lowerCamelCase__			)
			lowerCamelCase								=     (width - text_length) // 2
			lowerCamelCase								=     width - text_length - left_indent
			return " " * left_indent + text + " " * right_indent






def   __lowerCamelCase	(       ):





			'''simple docstring'''




			lowerCamelCase								=     diffusers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
			lowerCamelCase								=     {
			    name: config_mapping_names[code]
			    for code, name in diffusers_module.MODEL_NAMES_MAPPING.items()
			    if code in config_mapping_names
			}
			lowerCamelCase								=     {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								=     collections.defaultdict(lowerCamelCase__			)
			lowerCamelCase								=     collections.defaultdict(lowerCamelCase__			)
			lowerCamelCase								=     collections.defaultdict(lowerCamelCase__			)
			lowerCamelCase								=     collections.defaultdict(lowerCamelCase__			)
			lowerCamelCase								=     collections.defaultdict(lowerCamelCase__			)

			# Let's lookup through all diffusers object (once).
			for attr_name in dir(lowerCamelCase__			):
						lowerCamelCase								=     None
						if attr_name.endswith("""Tokenizer"""			):
									lowerCamelCase								=     slow_tokenizers
									lowerCamelCase								=     attr_name[:-9]
						elif attr_name.endswith("""TokenizerFast"""			):
									lowerCamelCase								=     fast_tokenizers
									lowerCamelCase								=     attr_name[:-13]
						elif _re_tf_models.match(lowerCamelCase__			) is not None:
									lowerCamelCase								=     tf_models
									lowerCamelCase								=     _re_tf_models.match(lowerCamelCase__			).groups()[0]
						elif _re_flax_models.match(lowerCamelCase__			) is not None:
									lowerCamelCase								=     flax_models
									lowerCamelCase								=     _re_flax_models.match(lowerCamelCase__			).groups()[0]
						elif _re_pt_models.match(lowerCamelCase__			) is not None:
									lowerCamelCase								=     pt_models
									lowerCamelCase								=     _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								=     True
															break
												# Try again after removing the last word in the name
												lowerCamelCase								=     """""".join(camel_case_split(lowerCamelCase__			)[:-1]			)

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

			# Build the table per se
			lowerCamelCase								=     """|""" + """|""".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								=     {True: """✅""", False: """❌"""}
			for name in model_names:
						lowerCamelCase								=     model_name_to_prefix[name]
						lowerCamelCase								=     [
						    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__		:	List[Any]=False			):





			'''simple docstring'''




			lowerCamelCase		,		lowerCamelCase		,		lowerCamelCase		,		lowerCamelCase								=     _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								=     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      :						Dict    =		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)





import unittest

from cer import CER


UpperCAmelCase      :						Union[str, Any]    =		CER()







class 					__lowercase					(       unittest.TestCase     ):



		"""simple docstring"""

		def 				__A      (   self  )     ->   Optional[int]:







					'''simple docstring'''







					lowerCamelCase								=     ["""White House"""]
					lowerCamelCase								=     ["""white house"""]
					# S = 2, D = 0, I = 0, N = 11, CER = 2 / 11
					lowerCamelCase								=     cer.compute(predictions=A		,			references=A  )
					self.assertTrue(abs(char_error_rate - 0.1818181818  ) < 1e-6  )







		def 				__A      (   self  )     ->   List[str]:







					'''simple docstring'''







					lowerCamelCase								=     ["""were wolf"""]
					lowerCamelCase								=     ["""werewolf"""]
					# S = 0, D = 0, I = 1, N = 9, CER = 1 / 9
					lowerCamelCase								=     cer.compute(predictions=A		,			references=A  )
					self.assertTrue(abs(char_error_rate - 0.1111111  ) < 1e-6  )

					lowerCamelCase								=     ["""werewolf"""]
					lowerCamelCase								=     ["""weae     wolf"""]
					# S = 1, D = 1, I = 0, N = 8, CER = 0.25
					lowerCamelCase								=     cer.compute(predictions=A		,			references=A  )
					self.assertTrue(abs(char_error_rate - 0.25  ) < 1e-6  )

					# consecutive whitespaces case 1
					lowerCamelCase								=     ["""were wolf"""]
					lowerCamelCase								=     ["""were               wolf"""]
					# S = 0, D = 0, I = 0, N = 9, CER = 0
					lowerCamelCase								=     cer.compute(predictions=A		,			references=A  )
					self.assertTrue(abs(char_error_rate - 0.0  ) < 1e-6  )

					# consecutive whitespaces case 2
					lowerCamelCase								=     ["""were   wolf"""]
					lowerCamelCase								=     ["""were               wolf"""]
					# S = 0, D = 0, I = 0, N = 9, CER = 0
					lowerCamelCase								=     cer.compute(predictions=A		,			references=A  )
					self.assertTrue(abs(char_error_rate - 0.0  ) < 1e-6  )







		def 				__A      (   self  )     ->   str:







					'''simple docstring'''







					lowerCamelCase								=     ["""werewolf"""]
					lowerCamelCase								=     ["""weaewolf"""]
					# S = 1, D = 0, I = 0, N = 8, CER = 0.125
					lowerCamelCase								=     cer.compute(predictions=A		,			references=A  )
					self.assertTrue(abs(char_error_rate - 0.125  ) < 1e-6  )







		def 				__A      (   self  )     ->   Tuple:







					'''simple docstring'''







					lowerCamelCase								=     ["""werewolf"""]
					lowerCamelCase								=     ["""wereawolf"""]
					# S = 0, D = 1, I = 0, N = 8, CER = 0.125
					lowerCamelCase								=     cer.compute(predictions=A		,			references=A  )
					self.assertTrue(abs(char_error_rate - 0.125  ) < 1e-6  )







		def 				__A      (   self  )     ->   List[Any]:







					'''simple docstring'''







					lowerCamelCase								=     ["""werewolf"""]
					lowerCamelCase								=     ["""wereolf"""]
					# S = 0, D = 0, I = 1, N = 8, CER = 0.125
					lowerCamelCase								=     cer.compute(predictions=A		,			references=A  )
					self.assertTrue(abs(char_error_rate - 0.125  ) < 1e-6  )







		def 				__A      (   self  )     ->   Tuple:







					'''simple docstring'''







					lowerCamelCase								=     ["""werewolf"""]
					lowerCamelCase								=     cer.compute(predictions=A		,			references=A  )
					self.assertEqual(A		,			0.0  )







		def 				__A      (   self  )     ->   List[str]:







					'''simple docstring'''







					lowerCamelCase								=     ["""werewolf""", """I am your father"""]
					lowerCamelCase								=     cer.compute(predictions=A		,			references=A  )
					self.assertEqual(A		,			0.0  )

					lowerCamelCase								=     ["""werewolf""", """I am your father""", """doge"""]
					lowerCamelCase								=     ["""werxwolf""", """I       am your father""", """doge"""]
					# S = 1, D = 0, I = 0, N = 28, CER = 1 / 28
					lowerCamelCase								=     cer.compute(predictions=A		,			references=A  )
					self.assertTrue(abs(char_error_rate - 0.03571428  ) < 1e-6  )







		def 				__A      (   self  )     ->   List[Any]:







					'''simple docstring'''







					lowerCamelCase								=     ["""My hovercraft""", """is full of eels"""]
					lowerCamelCase								=     ["""My hovercraft is full""", """ of eels"""]
					# S = 0, D = 0, I = 2, N = 28, CER = 2 / 28
					# whitespace at the front of " of eels" will be strip during preporcessing
					# so need to insert 2 whitespaces
					lowerCamelCase								=     cer.compute(predictions=A		,			references=A		,			concatenate_texts=A  )
					self.assertTrue(abs(char_error_rate - 0.071428  ) < 1e-6  )







		def 				__A      (   self  )     ->   Optional[int]:







					'''simple docstring'''







					lowerCamelCase								=     ["""我能吞下玻璃而不伤身体"""]
					lowerCamelCase								=     [""" 能吞虾玻璃而 不霜身体啦"""]
					# S = 3, D = 2, I = 0, N = 11, CER = 5 / 11
					lowerCamelCase								=     cer.compute(predictions=A		,			references=A  )
					self.assertTrue(abs(char_error_rate - 0.4545454545  ) < 1e-6  )

					lowerCamelCase								=     ["""我能吞下玻璃""", """而不伤身体"""]
					lowerCamelCase								=     ["""我    能 吞 下 玻 璃""", """而不伤身体"""]
					# S = 0, D = 5, I = 0, N = 11, CER = 5 / 11
					lowerCamelCase								=     cer.compute(predictions=A		,			references=A  )
					self.assertTrue(abs(char_error_rate - 0.454545454545  ) < 1e-6  )

					lowerCamelCase								=     ["""我能吞下玻璃而不伤身体"""]
					lowerCamelCase								=     cer.compute(predictions=A		,			references=A  )
					self.assertFalse(A		,			0.0  )







		def 				__A      (   self  )     ->   Optional[int]:







					'''simple docstring'''







					lowerCamelCase								=     [""""""]
					lowerCamelCase								=     ["""Hypothesis"""]
					with self.assertRaises(A  ):
								cer.compute(predictions=A		,			references=A  )


if __name__ == "__main__":
		unittest.main()

