"""simple docstring"""





from __future__ import annotations





def        a_  (   _lowerCAmelCase						:				str     ,				_lowerCAmelCase						:				list[str] | None = None     ,				_lowerCAmelCase						:				dict[str, float] | None = None     ,				_lowerCAmelCase						:				bool = False     ,				):





       '''simple docstring'''

       lowercase__    :	Tuple     					=			cipher_alphabet or [chr(_A  ) 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)
              lowercase__    :	Dict     					=			{
                  "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
              lowercase__    :	Optional[int]     					=			frequencies_dict

       if not case_sensitive:
              lowercase__    :	str     					=			ciphertext.lower()

       # Chi squared statistic values
       lowercase__    :	dict[int, tuple[float, str]]     					=			{}

       # cycle through all of the shifts
       for shift in range(len(_A  )  ):
              lowercase__    :	Optional[Any]     					=			""

              # decrypt the message with the shift
              for letter in ciphertext:
                     try:
                            # Try to index the letter in the alphabet
                            lowercase__    :	Dict     					=			(alphabet_letters.index(letter.lower()  ) - shift) % len(
                                _A  )
                            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

              lowercase__    :	str     					=			0.0

              # Loop through each letter in the decoded message with the shift
              for letter in decrypted_with_shift:
                     if case_sensitive:
                            lowercase__    :	List[str]     					=			letter.lower()
                            if letter in frequencies:
                                   # Get the amount of times the letter occurs in the message
                                   lowercase__    :	List[str]     					=			decrypted_with_shift.lower().count(_A  )

                                   # Get the excepcted amount of times the letter should appear based
                                   # on letter frequencies
                                   lowercase__    :	List[Any]     					=			frequencies[letter] * occurrences

                                   # Complete the chi squared statistic formula
                                   lowercase__    :	str     					=			((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
                                   lowercase__    :	Any     					=			decrypted_with_shift.count(_A  )

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

                                   # Complete the chi squared statistic formula
                                   lowercase__    :	int     					=			((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
              lowercase__    :	Optional[int]     					=			(
                  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(_lowerCAmelCase						:				int  ) -> tuple[float, str]:
              return chi_squared_statistic_values[key]

       lowercase__    :	int     					=			min(
           _A     ,				key=_A     ,				)

       # Get all the data from the most likely cipher (key, decoded message)
       (
           lowercase__     
       )    :	int     					=			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"""





import gc
import unittest

from diffusers import FlaxControlNetModel, FlaxStableDiffusionControlNetPipeline
from diffusers.utils import is_flax_available, load_image, slow
from diffusers.utils.testing_utils import require_flax


if is_flax_available():
       import jax
       import jax.numpy as jnp
       from flax.jax_utils import replicate
       from flax.training.common_utils import shard


@slow
@require_flax
class   UpperCAmelCase_			(					unittest.TestCase):


      def 	_UpperCAmelCase (     self							) ->      List[Any]:
             # clean up the VRAM after each test
             super().tearDown()
             gc.collect()


      def 	_UpperCAmelCase (     self							) ->      Tuple:
             lowercase__					,   lowercase__    :	str     					=			FlaxControlNetModel.from_pretrained(
                 'lllyasviel/sd-controlnet-canny'	,    from_pt=a	,    dtype=jnp.bfloataa							)
             lowercase__					,   lowercase__    :	List[str]     					=			FlaxStableDiffusionControlNetPipeline.from_pretrained(
                 'runwayml/stable-diffusion-v1-5'	,    controlnet=a	,    from_pt=a	,    dtype=jnp.bfloataa							)
             lowercase__    :	List[Any]     					=			controlnet_params

             lowercase__    :	int     					=			'bird'
             lowercase__    :	List[Any]     					=			jax.device_count()
             lowercase__    :	Dict     					=			pipe.prepare_text_inputs([prompts] * num_samples							)

             lowercase__    :	Union[str, Any]     					=			load_image(
                 'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png'							)
             lowercase__    :	Optional[int]     					=			pipe.prepare_image_inputs([canny_image] * num_samples							)

             lowercase__    :	List[Any]     					=			jax.random.PRNGKey(0							)
             lowercase__    :	Tuple     					=			jax.random.split(a	,    jax.device_count()							)

             lowercase__    :	str     					=			replicate(a							)
             lowercase__    :	List[str]     					=			shard(a							)
             lowercase__    :	Dict     					=			shard(a							)

             lowercase__    :	List[Any]     					=			pipe(
                 prompt_ids=a	,    image=a	,    params=a	,    prng_seed=a	,    num_inference_steps=5_0	,    jit=a	,    ).images
             assert images.shape == (jax.device_count(), 1, 7_6_8, 5_1_2, 3)

             lowercase__    :	Any     					=			images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]							)
             lowercase__    :	Tuple     					=			images[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]

             lowercase__    :	int     					=			jnp.asarray(jax.device_get(image_slice.flatten()							)							)
             lowercase__    :	Optional[Any]     					=			jnp.array(
                 [0.167_969, 0.116_699, 0.081_543, 0.154_297, 0.132_812, 0.108_887, 0.169_922, 0.169_922, 0.205_078]							)
             print(f"""output_slice: {output_slice}"""							)
             assert jnp.abs(output_slice - expected_slice							).max() < 1e-2


      def 	_UpperCAmelCase (     self							) ->      List[str]:
             lowercase__					,   lowercase__    :	int     					=			FlaxControlNetModel.from_pretrained(
                 'lllyasviel/sd-controlnet-openpose'	,    from_pt=a	,    dtype=jnp.bfloataa							)
             lowercase__					,   lowercase__    :	Optional[Any]     					=			FlaxStableDiffusionControlNetPipeline.from_pretrained(
                 'runwayml/stable-diffusion-v1-5'	,    controlnet=a	,    from_pt=a	,    dtype=jnp.bfloataa							)
             lowercase__    :	Optional[Any]     					=			controlnet_params

             lowercase__    :	List[Any]     					=			'Chef in the kitchen'
             lowercase__    :	List[str]     					=			jax.device_count()
             lowercase__    :	Dict     					=			pipe.prepare_text_inputs([prompts] * num_samples							)

             lowercase__    :	Optional[Any]     					=			load_image(
                 'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/pose.png'							)
             lowercase__    :	Optional[int]     					=			pipe.prepare_image_inputs([pose_image] * num_samples							)

             lowercase__    :	List[str]     					=			jax.random.PRNGKey(0							)
             lowercase__    :	str     					=			jax.random.split(a	,    jax.device_count()							)

             lowercase__    :	Optional[Any]     					=			replicate(a							)
             lowercase__    :	Optional[Any]     					=			shard(a							)
             lowercase__    :	List[Any]     					=			shard(a							)

             lowercase__    :	List[Any]     					=			pipe(
                 prompt_ids=a	,    image=a	,    params=a	,    prng_seed=a	,    num_inference_steps=5_0	,    jit=a	,    ).images
             assert images.shape == (jax.device_count(), 1, 7_6_8, 5_1_2, 3)

             lowercase__    :	Union[str, Any]     					=			images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]							)
             lowercase__    :	List[str]     					=			images[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]

             lowercase__    :	Optional[int]     					=			jnp.asarray(jax.device_get(image_slice.flatten()							)							)
             lowercase__    :	str     					=			jnp.array(
                 [[0.271_484, 0.261_719, 0.275_391, 0.277_344, 0.279_297, 0.291_016, 0.294_922, 0.302_734, 0.302_734]]							)
             print(f"""output_slice: {output_slice}"""							)
             assert jnp.abs(output_slice - expected_slice							).max() < 1e-2

