from __future__ import annotations





def 			lowercase(	UpperCamelCase_							,					UpperCamelCase_ = None							,					UpperCamelCase_ = None							,					UpperCamelCase_ = False							,					)		->	tuple[int, float, str]:







 '''simple docstring'''
 UpperCamelCase			  =      cipher_alphabet or [chr(UpperCamelCase_       ) for i in range(97							,					123       )]

 # If the argument is None or the user provided an empty dictionary
 if not frequencies_dict:
  # Frequencies of letters in the english language (how much they show up)
  UpperCamelCase			  =      {
      "a": 0.0_8_4_9_7,
      "b": 0.0_1_4_9_2,
      "c": 0.0_2_2_0_2,
      "d": 0.0_4_2_5_3,
      "e": 0.1_1_1_6_2,
      "f": 0.0_2_2_2_8,
      "g": 0.0_2_0_1_5,
      "h": 0.0_6_0_9_4,
      "i": 0.0_7_5_4_6,
      "j": 0.0_0_1_5_3,
      "k": 0.0_1_2_9_2,
      "l": 0.0_4_0_2_5,
      "m": 0.0_2_4_0_6,
      "n": 0.0_6_7_4_9,
      "o": 0.0_7_5_0_7,
      "p": 0.0_1_9_2_9,
      "q": 0.0_0_0_9_5,
      "r": 0.0_7_5_8_7,
      "s": 0.0_6_3_2_7,
      "t": 0.0_9_3_5_6,
      "u": 0.0_2_7_5_8,
      "v": 0.0_0_9_7_8,
      "w": 0.0_2_5_6_0,
      "x": 0.0_0_1_5_0,
      "y": 0.0_1_9_9_4,
      "z": 0.0_0_0_7_7,
  }
 else:
  # Custom frequencies dictionary
  UpperCamelCase			  =      frequencies_dict

 if not case_sensitive:
  UpperCamelCase			  =      ciphertext.lower()

 # Chi squared statistic values
 UpperCamelCase			  =      {}

 # cycle through all of the shifts
 for shift in range(len(UpperCamelCase_       )       ):
  UpperCamelCase			  =      ""

  # decrypt the message with the shift
  for letter in ciphertext:
   try:
    # Try to index the letter in the alphabet
    UpperCamelCase			  =      (alphabet_letters.index(letter.lower()       ) - shift) % len(
        UpperCamelCase_       )
    decrypted_with_shift += (
        alphabet_letters[new_key].upper()
        if case_sensitive and letter.isupper()
        else alphabet_letters[new_key]
    )
   except ValueError:
    # Append the character if it isn't in the alphabet
    decrypted_with_shift += letter

  UpperCamelCase			  =      0.0

  # Loop through each letter in the decoded message with the shift
  for letter in decrypted_with_shift:
   if case_sensitive:
    UpperCamelCase			  =      letter.lower()
    if letter in frequencies:
     # Get the amount of times the letter occurs in the message
     UpperCamelCase			  =      decrypted_with_shift.lower().count(UpperCamelCase_       )

     # Get the excepcted amount of times the letter should appear based
     # on letter frequencies
     UpperCamelCase			  =      frequencies[letter] * occurrences

     # Complete the chi squared statistic formula
     UpperCamelCase			  =      ((occurrences - expected) ** 2) / expected

     # Add the margin of error to the total chi squared statistic
     chi_squared_statistic += chi_letter_value
   else:
    if letter.lower() in frequencies:
     # Get the amount of times the letter occurs in the message
     UpperCamelCase			  =      decrypted_with_shift.count(UpperCamelCase_       )

     # Get the excepcted amount of times the letter should appear based
     # on letter frequencies
     UpperCamelCase			  =      frequencies[letter] * occurrences

     # Complete the chi squared statistic formula
     UpperCamelCase			  =      ((occurrences - expected) ** 2) / expected

     # Add the margin of error to the total chi squared statistic
     chi_squared_statistic += chi_letter_value

        # Add the data to the chi_squared_statistic_values dictionary
  UpperCamelCase			  =      (
      chi_squared_statistic,
      decrypted_with_shift,
  )

 # Get the most likely cipher by finding the cipher with the smallest chi squared
 # statistic
 def chi_squared_statistic_values_sorting_key(UpperCamelCase_       ) -> tuple[float, str]:
  return chi_squared_statistic_values[key]

 UpperCamelCase			  =      min(
     UpperCamelCase_							,					key=UpperCamelCase_							,					)

 # Get all the data from the most likely cipher (key, decoded message)
 (
     UpperCamelCase		
 )			  =      chi_squared_statistic_values[most_likely_cipher]

 # Return the data on the most likely shift
 return (
     most_likely_cipher,
     most_likely_cipher_chi_squared_value,
     decoded_most_likely_cipher,
 )




'''simple docstring'''
from pathlib import Path
from typing import List

from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import get_tests_dir, is_tool_test
from transformers.tools.agent_types import AGENT_TYPE_MAPPING, AgentAudio, AgentImage, AgentText


if is_torch_available():
					import torch

if is_vision_available():
					from PIL import Image


lowerCAmelCase   : List[Any]             =				["""text""", """image""", """audio"""]




def 	_A  (       A							)  ->				Dict:
	lowercase						:		str   					=		[]

	for input_type in input_types:
		if input_type == "text":
			inputs.append("Text input"							)
		elif input_type == "image":
			inputs.append(
			    Image.open(Path(get_tests_dir("fixtures/tests_samples/COCO"							)							) / "000000039769.png"							).resize((5_1_2, 5_1_2)							)							)
		elif input_type == "audio":
			inputs.append(torch.ones(3_0_0_0							)							)
		elif isinstance(A							,A							):
			inputs.append(create_inputs(A							)							)
		else:
			raise ValueError(F'''Invalid type requested: {input_type}'''							)

	return inputs




def 	_A  (       A							)  ->				str:
	lowercase						:		Tuple   					=		[]

	for output in outputs:
		if isinstance(A							,(str, AgentText)							):
			output_types.append("text"							)
		elif isinstance(A							,(Image.Image, AgentImage)							):
			output_types.append("image"							)
		elif isinstance(A							,(torch.Tensor, AgentAudio)							):
			output_types.append("audio"							)
		else:
			raise ValueError(F'''Invalid output: {output}'''							)

	return output_types






@is_tool_test
class        _UpperCamelCase	:


				'''simple docstring'''


				def 	a__		(     self							)    ->  Optional[Any]:
					self.assertTrue(hasattr(self.tool							,      "inputs"							)							)
					self.assertTrue(hasattr(self.tool							,      "outputs"							)							)

					lowercase						:		Optional[Any]   					=		self.tool.inputs
					for _input in inputs:
						if isinstance(_input							,      a_							):
							for __input in _input:
								self.assertTrue(__input in authorized_types							)
						else:
							self.assertTrue(_input in authorized_types							)

					lowercase						:		Any   					=		self.tool.outputs
					for _output in outputs:
						self.assertTrue(_output in authorized_types							)

				def 	a__		(     self							)    ->  Any:
					lowercase						:		Any   					=		create_inputs(self.tool.inputs							)
					lowercase						:		Tuple   					=		self.tool(*a_							)

					# There is a single output
					if len(self.tool.outputs							) == 1:
						lowercase						:		Any   					=		[outputs]

					self.assertListEqual(output_types(a_							)							,      self.tool.outputs							)

				def 	a__		(     self							)    ->  List[str]:
					self.assertTrue(hasattr(self.tool							,      "description"							)							)
					self.assertTrue(hasattr(self.tool							,      "default_checkpoint"							)							)
					self.assertTrue(self.tool.description.startswith("This is a tool that"							)							)

				def 	a__		(     self							)    ->  int:
					lowercase						:		str   					=		create_inputs(self.tool.inputs							)
					lowercase						:		str   					=		self.tool(*a_							)

					if not isinstance(a_							,      a_							):
						lowercase						:		Union[str, Any]   					=		[outputs]

					self.assertEqual(len(a_							)							,      len(self.tool.outputs							)							)

					for output, output_type in zip(a_							,      self.tool.outputs							):
						lowercase						:		List[str]   					=		AGENT_TYPE_MAPPING[output_type]
						self.assertTrue(isinstance(a_							,      a_							)							)


				def 	a__		(     self							)    ->  Optional[int]:
					lowercase						:		int   					=		create_inputs(self.tool.inputs							)

					lowercase						:		str   					=		[]

					for _input, input_type in zip(a_							,      self.tool.inputs							):
						if isinstance(a_							,      a_							):
							_inputs.append([AGENT_TYPE_MAPPING[_input_type](_input							) for _input_type in input_type]							)
						else:
							_inputs.append(AGENT_TYPE_MAPPING[input_type](_input							)							)

        # Should not raise an error
					lowercase						:		Optional[int]   					=		self.tool(*a_							)

					if not isinstance(a_							,      a_							):
						lowercase						:		str   					=		[outputs]

					self.assertEqual(len(a_							)							,      len(self.tool.outputs							)							)


