code: 
  
  
  '''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
  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)
  
  
style_context: 
  
  
  import math
  
  import qiskit
  from qiskit import Aer, ClassicalRegister, QuantumCircuit, QuantumRegister, execute
  
  
  
  
  
  
  def   __lowerCamelCase	(       lowerCamelCase__		:	int = 1     ,       lowerCamelCase__		:	int = 1			):
  
  
  
  
  
  			'''simple docstring'''
  
  
  
  
  			if isinstance(lowerCamelCase__     ,       lowerCamelCase__			) or isinstance(lowerCamelCase__     ,       lowerCamelCase__			):
  						raise TypeError("""inputs must be integers."""			)
  			if (bit_a < 0) or (bit_a < 0):
  						raise ValueError("""inputs must be positive."""			)
  			if (math.floor(lowerCamelCase__			) != bit_a) or (math.floor(lowerCamelCase__			) != bit_a):
  						raise ValueError("""inputs must be exact integers."""			)
  			if (bit_a > 1) or (bit_a > 1):
  						raise ValueError("""inputs must be less or equal to 1."""			)
  
  			# build registers
  			lowerCamelCase								=     QuantumRegister(2     ,       """qr"""			)
  			lowerCamelCase								=     ClassicalRegister(2     ,       """cr"""			)
  
  			lowerCamelCase								=     QuantumCircuit(lowerCamelCase__     ,       lowerCamelCase__			)
  
  			# entanglement the qubits
  			quantum_circuit.h(1			)
  			quantum_circuit.cx(1     ,       0			)
  
  			# send the information
  			lowerCamelCase								=     str(lowerCamelCase__			) + str(lowerCamelCase__			)
  
  			if c_information == "11":
  						quantum_circuit.x(1			)
  						quantum_circuit.z(1			)
  			elif c_information == "10":
  						quantum_circuit.z(1			)
  			elif c_information == "01":
  						quantum_circuit.x(1			)
  			else:
  						quantum_circuit.i(1			)
  
  			# unentangled the circuit
  			quantum_circuit.cx(1     ,       0			)
  			quantum_circuit.h(1			)
  
  			# measure the circuit
  			quantum_circuit.measure(lowerCamelCase__     ,       lowerCamelCase__			)
  
  			lowerCamelCase								=     Aer.get_backend("""aer_simulator"""			)
  			lowerCamelCase								=     execute(lowerCamelCase__     ,       lowerCamelCase__     ,       shots=1000			)
  
  			return job.result().get_counts(lowerCamelCase__			)
  
  
  if __name__ == "__main__":
  		print(f"""Counts for classical state send: {superdense_coding(1,1)}""")
  
  