





import gc
import inspect
import unittest

import torch
from parameterized import parameterized

from diffusers import PriorTransformer
from diffusers.utils import floats_tensor, slow, torch_all_close, torch_device
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin


enable_full_determinism()
class SCREAMING_SNAKE_CASE_						(					snake_case_       ,				unittest.TestCase					):
						__magic_name__:			Dict       =       PriorTransformer
						__magic_name__:			str       =       "hidden_states"






						@property
						def UpperCAmelCase_				(      self      :      Optional[Any]	)							->    Optional[int]:







							"""simple docstring"""


							snake_case_		:			Any											=	4
							snake_case_		:			int											=	8
							snake_case_		:			Dict											=	7

							snake_case_		:			Union[str, Any]											=	floats_tensor((batch_size, embedding_dim)	).to(_A	)

							snake_case_		:			int											=	floats_tensor((batch_size, embedding_dim)	).to(_A	)
							snake_case_		:			str											=	floats_tensor((batch_size, num_embeddings, embedding_dim)	).to(_A	)

							return {
							    "hidden_states": hidden_states,
							    "timestep": 2,
							    "proj_embedding": proj_embedding,
							    "encoder_hidden_states": encoder_hidden_states,
							}






						def UpperCAmelCase_				(      self      :      List[Any]		,   _A      :      List[Any]=0	)							->    str:







							"""simple docstring"""


							torch.manual_seed(_A	)
							snake_case_		:			List[Any]											=	4
							snake_case_		:			str											=	8
							snake_case_		:			Any											=	7

							snake_case_		:			List[Any]											=	torch.randn((batch_size, embedding_dim)	).to(_A	)

							snake_case_		:			Optional[int]											=	torch.randn((batch_size, embedding_dim)	).to(_A	)
							snake_case_		:			List[Any]											=	torch.randn((batch_size, num_embeddings, embedding_dim)	).to(_A	)

							return {
							    "hidden_states": hidden_states,
							    "timestep": 2,
							    "proj_embedding": proj_embedding,
							    "encoder_hidden_states": encoder_hidden_states,
							}






						@property
						def UpperCAmelCase_				(      self      :      str	)							->    Optional[Any]:







							"""simple docstring"""


							return (4, 8)






						@property
						def UpperCAmelCase_				(      self      :      Optional[int]	)							->    List[Any]:







							"""simple docstring"""


							return (4, 8)






						def UpperCAmelCase_				(      self      :      Optional[int]	)							->    Union[str, Any]:







							"""simple docstring"""


							snake_case_		:			Union[str, Any]											=	{
							    'num_attention_heads': 2,
							    'attention_head_dim': 4,
							    'num_layers': 2,
							    'embedding_dim': 8,
							    'num_embeddings': 7,
							    'additional_embeddings': 4,
							}
							snake_case_		:			Tuple											=	self.dummy_input
							return init_dict, inputs_dict






						def UpperCAmelCase_				(      self      :      List[Any]	)							->    Dict:







							"""simple docstring"""


							snake_case_		:			str											=	PriorTransformer.from_pretrained(
							    'hf-internal-testing/prior-dummy'		,   output_loading_info=_A	)
							self.assertIsNotNone(_A	)
							self.assertEqual(len(loading_info['missing_keys']	)		,   0	)

							model.to(_A	)
							snake_case_		:			Optional[Any]											=	model(**self.dummy_input	)[0]

							assert hidden_states is not None, "Make sure output is not None"






						def UpperCAmelCase_				(      self      :      Tuple	)							->    List[Any]:







							"""simple docstring"""


							snake_case_		:			Optional[int]											=	self.prepare_init_args_and_inputs_for_common()

							snake_case_		:			Tuple											=	self.model_class(**_A	)
							snake_case_		:			Any											=	inspect.signature(model.forward	)
							# signature.parameters is an OrderedDict => so arg_names order is deterministic
							snake_case_		:			List[str]											=	[*signature.parameters.keys()]

							snake_case_		:			int											=	['hidden_states', 'timestep']
							self.assertListEqual(arg_names[:2]		,   _A	)






						def UpperCAmelCase_				(      self      :      Union[str, Any]	)							->    List[Any]:







							"""simple docstring"""


							snake_case_		:			str											=	PriorTransformer.from_pretrained('hf-internal-testing/prior-dummy'	)
							snake_case_		:			str											=	model.to(_A	)

							if hasattr(_A		,   'set_default_attn_processor'	):
								model.set_default_attn_processor()

							snake_case_		:			Optional[int]											=	self.get_dummy_seed_input()

							with torch.no_grad():
								snake_case_		:			Any											=	model(**_A	)[0]

							snake_case_		:			Any											=	output[0, :5].flatten().cpu()
							print(_A	)

							# Since the VAE Gaussian prior's generator is seeded on the appropriate device,
							# the expected output slices are not the same for CPU and GPU.
							snake_case_		:			str											=	torch.tensor([-1.3_4_3_6, -0.2_8_7_0, 0.7_5_3_8, 0.4_3_6_8, -0.0_2_3_9]	)
							self.assertTrue(torch_all_close(_A		,   _A		,   rtol=1E-2	)	)
@slow
class SCREAMING_SNAKE_CASE_						(					unittest.TestCase					):






						def UpperCAmelCase_				(      self      :      Optional[Any]		,   _A      :      int=1		,   _A      :      int=768		,   _A      :      str=77		,   _A      :      List[str]=0	)							->    Tuple:







							"""simple docstring"""


							torch.manual_seed(_A	)
							snake_case_		:			Dict											=	batch_size
							snake_case_		:			Any											=	embedding_dim
							snake_case_		:			int											=	num_embeddings

							snake_case_		:			Dict											=	torch.randn((batch_size, embedding_dim)	).to(_A	)

							snake_case_		:			List[Any]											=	torch.randn((batch_size, embedding_dim)	).to(_A	)
							snake_case_		:			Optional[int]											=	torch.randn((batch_size, num_embeddings, embedding_dim)	).to(_A	)

							return {
							    "hidden_states": hidden_states,
							    "timestep": 2,
							    "proj_embedding": proj_embedding,
							    "encoder_hidden_states": encoder_hidden_states,
							}






						def UpperCAmelCase_				(      self      :      List[Any]	)							->    Optional[int]:







							"""simple docstring"""


							super().tearDown()
							gc.collect()
							torch.cuda.empty_cache()






						@parameterized.expand(
						    [
						        # fmt: off
						        [13, [-0.5_8_6_1,  0.1_2_8_3, -0.0_9_3_1,  0.0_8_8_2,  0.4_4_7_6,  0.1_3_2_9, -0.0_4_9_8,  0.0_6_4_0]],
						        [37, [-0.4_9_1_3,  0.0_1_1_0, -0.0_4_8_3,  0.0_5_4_1,  0.4_9_5_4, -0.0_1_7_0,  0.0_3_5_4,  0.1_6_5_1]],
						        # fmt: on
						    ]	)
						def UpperCAmelCase_				(      self      :      Tuple		,   _A      :      List[Any]		,   _A      :      List[str]	)							->    Optional[int]:







							"""simple docstring"""


							snake_case_		:			str											=	PriorTransformer.from_pretrained('kandinsky-community/kandinsky-2-1-prior'		,   subfolder='prior'	)
							model.to(_A	)
							snake_case_		:			Optional[Any]											=	self.get_dummy_seed_input(seed=_A	)

							with torch.no_grad():
								snake_case_		:			str											=	model(**_A	)[0]

							assert list(sample.shape	) == [1, 768]

							snake_case_		:			Optional[Any]											=	sample[0, :8].flatten().cpu()
							print(_A	)
							snake_case_		:			int											=	torch.tensor(_A	)

							assert torch_all_close(_A		,   _A		,   atol=1E-3	)








from __future__ import annotations


def 							SCREAMING_SNAKE_CASE__						(    __a   , __a = None   , __a = None   , __a = False   , ):
	snake_case_		:			int											=	cipher_alphabet or [chr(__a   ) for i in range(97   , 1_23   )]

	# 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)
		snake_case_		:			Dict											=	{
		    'a': 0.08497,
		    'b': 0.01492,
		    'c': 0.02202,
		    'd': 0.04253,
		    'e': 0.11162,
		    'f': 0.02228,
		    'g': 0.02015,
		    'h': 0.06094,
		    'i': 0.07546,
		    'j': 0.00153,
		    'k': 0.01292,
		    'l': 0.04025,
		    'm': 0.02406,
		    'n': 0.06749,
		    'o': 0.07507,
		    'p': 0.01929,
		    'q': 0.00095,
		    'r': 0.07587,
		    's': 0.06327,
		    't': 0.09356,
		    'u': 0.02758,
		    'v': 0.00978,
		    'w': 0.02560,
		    'x': 0.00150,
		    'y': 0.01994,
		    'z': 0.00077,
		}
	else:
		# Custom frequencies dictionary
		snake_case_		:			int											=	frequencies_dict

	if not case_sensitive:
		snake_case_		:			Optional[Any]											=	ciphertext.lower()

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

	# cycle through all of the shifts
	for shift in range(len(__a   )   ):
		snake_case_		:			int											=	''

		# decrypt the message with the shift
		for letter in ciphertext:
			try:
				# Try to index the letter in the alphabet
				snake_case_		:			List[Any]											=	(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

		snake_case_		:			Union[str, Any]											=	0.0

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

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

					# Complete the chi squared statistic formula
					snake_case_		:			Optional[int]											=	((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
					snake_case_		:			Any											=	decrypted_with_shift.count(__a   )

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

					# Complete the chi squared statistic formula
					snake_case_		:			Optional[Any]											=	((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
		snake_case_		:			Any											=	(
		    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(__a   ) -> tuple[float, str]:
		return chi_squared_statistic_values[key]

	snake_case_		:			int											=	min(
	    __a   , key=__a   , )

	# Get all the data from the most likely cipher (key, decoded message)
	(
	    (
	    snake_case_
	)						,(
	    snake_case_
	)						,
	)		:			str											=	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,
	)

