
"""simple docstring"""




from typing import Optional, Tuple, Union

import flax
import flax.linen as nn
import jax
import jax.numpy as jnp
from flax.core.frozen_dict import FrozenDict

from ..configuration_utils import ConfigMixin, flax_register_to_config
from ..utils import BaseOutput
from .embeddings_flax import FlaxTimestepEmbedding, FlaxTimesteps
from .modeling_flax_utils import FlaxModelMixin
from .unet_ad_blocks_flax import (
    FlaxCrossAttnDownBlockaD,
    FlaxDownBlockaD,
    FlaxUNetMidBlockaDCrossAttn,
)





@flax.struct.dataclass
class 							UpperCAmelCase				(  __SCREAMING_SNAKE_CASE  ):




	'''simple docstring'''





	snake_case_    =					42
	snake_case_    =					42





class 							UpperCAmelCase				(  nn.Module  ):




	'''simple docstring'''





	snake_case_    =					42
	snake_case_    =					(16, 32, 96, 256)
	snake_case_    =					jnp.floataa






	def     UpperCamelCase_  (	self :			Optional[Any]			):
					__A             =			nn.Conv(
					    self.block_out_channels[0]							,kernel_size=(3, 3)							,padding=((1, 1), (1, 1))							,dtype=self.dtype							,)

					__A             =			[]
					for i in range(len(self.block_out_channels			) - 1			):
									__A             =			self.block_out_channels[i]
									__A             =			self.block_out_channels[i + 1]
									__A             =			nn.Conv(
									    A							,kernel_size=(3, 3)							,padding=((1, 1), (1, 1))							,dtype=self.dtype							,)
									blocks.append(A			)
									__A             =			nn.Conv(
									    A							,kernel_size=(3, 3)							,strides=(2, 2)							,padding=((1, 1), (1, 1))							,dtype=self.dtype							,)
									blocks.append(A			)
					__A             =			blocks

					__A             =			nn.Conv(
					    self.conditioning_embedding_channels							,kernel_size=(3, 3)							,padding=((1, 1), (1, 1))							,kernel_init=nn.initializers.zeros_init()							,bias_init=nn.initializers.zeros_init()							,dtype=self.dtype							,)







	def __call__(	self :			Tuple							,A :			Dict			):
					__A             =			self.conv_in(A			)
					__A             =			nn.silu(A			)

					for block in self.blocks:
									__A             =			block(A			)
									__A             =			nn.silu(A			)

					__A             =			self.conv_out(A			)

					return embedding





@flax_register_to_config
class 							UpperCAmelCase				(  nn.Module      ,      __SCREAMING_SNAKE_CASE      ,      __SCREAMING_SNAKE_CASE  ):




	'''simple docstring'''





	snake_case_    =					32
	snake_case_    =					4
	snake_case_    =					(
	    "CrossAttnDownBlock2D",
	    "CrossAttnDownBlock2D",
	    "CrossAttnDownBlock2D",
	    "DownBlock2D",
	)
	snake_case_    =					False
	snake_case_    =					(320, 640, 1280, 1280)
	snake_case_    =					2
	snake_case_    =					8
	snake_case_    =					None
	snake_case_    =					1280
	snake_case_    =					0.0
	snake_case_    =					False
	snake_case_    =					jnp.floataa
	snake_case_    =					True
	snake_case_    =					0
	snake_case_    =					"rgb"
	snake_case_    =					(16, 32, 96, 256)






	def     UpperCamelCase_  (	self :			Any							,A :			jax.random.KeyArray			):
					# init input tensors
					__A             =			(1, self.in_channels, self.sample_size, self.sample_size)
					__A             =			jnp.zeros(A							,dtype=jnp.floataa			)
					__A             =			jnp.ones((1,)							,dtype=jnp.intaa			)
					__A             =			jnp.zeros((1, 1, self.cross_attention_dim)							,dtype=jnp.floataa			)
					__A             =			(1, 3, self.sample_size * 8, self.sample_size * 8)
					__A             =			jnp.zeros(A							,dtype=jnp.floataa			)

					__A      ,							__A             =			jax.random.split(A			)
					__A             =			{"params": params_rng, "dropout": dropout_rng}

					return self.init(A							,A							,A							,A							,A			)["params"]






	def     UpperCamelCase_  (	self :			Dict			):
					__A             =			self.block_out_channels
					__A             =			block_out_channels[0] * 4

					# If `num_attention_heads` is not defined (which is the case for most models)
					# it will default to `attention_head_dim`. This looks weird upon first reading it and it is.
					# The reason for this behavior is to correct for incorrectly named variables that were introduced
					# when this library was created. The incorrect naming was only discovered much later in https://github.com/huggingface/diffusers/issues/2011#issuecomment-1547958131
					# Changing `attention_head_dim` to `num_attention_heads` for 40,000+ configurations is too backwards breaking
					# which is why we correct for the naming here.
					__A             =			self.num_attention_heads or self.attention_head_dim

					# input
					__A             =			nn.Conv(
					    block_out_channels[0]							,kernel_size=(3, 3)							,strides=(1, 1)							,padding=((1, 1), (1, 1))							,dtype=self.dtype							,)

					# time
					__A             =			FlaxTimesteps(
					    block_out_channels[0]							,flip_sin_to_cos=self.flip_sin_to_cos							,freq_shift=self.config.freq_shift			)
					__A             =			FlaxTimestepEmbedding(A							,dtype=self.dtype			)

					__A             =			FlaxControlNetConditioningEmbedding(
					    conditioning_embedding_channels=block_out_channels[0]							,block_out_channels=self.conditioning_embedding_out_channels							,)

					__A             =			self.only_cross_attention
					if isinstance(A							,A			):
									__A             =			(only_cross_attention,) * len(self.down_block_types			)

					if isinstance(A							,A			):
									__A             =			(num_attention_heads,) * len(self.down_block_types			)

					# down
					__A             =			[]
					__A             =			[]

					__A             =			block_out_channels[0]

					__A             =			nn.Conv(
					    A							,kernel_size=(1, 1)							,padding="VALID"							,kernel_init=nn.initializers.zeros_init()							,bias_init=nn.initializers.zeros_init()							,dtype=self.dtype							,)
					controlnet_down_blocks.append(A			)

					for i, down_block_type in enumerate(self.down_block_types			):
									__A             =			output_channel
									__A             =			block_out_channels[i]
									__A             =			i == len(A			) - 1

									if down_block_type == "CrossAttnDownBlock2D":
													__A             =			FlaxCrossAttnDownBlockaD(
													    in_channels=A							,out_channels=A							,dropout=self.dropout							,num_layers=self.layers_per_block							,num_attention_heads=num_attention_heads[i]							,add_downsample=not is_final_block							,use_linear_projection=self.use_linear_projection							,only_cross_attention=only_cross_attention[i]							,dtype=self.dtype							,)
									else:
													__A             =			FlaxDownBlockaD(
													    in_channels=A							,out_channels=A							,dropout=self.dropout							,num_layers=self.layers_per_block							,add_downsample=not is_final_block							,dtype=self.dtype							,)

									down_blocks.append(A			)

									for _ in range(self.layers_per_block			):
													__A             =			nn.Conv(
													    A							,kernel_size=(1, 1)							,padding="VALID"							,kernel_init=nn.initializers.zeros_init()							,bias_init=nn.initializers.zeros_init()							,dtype=self.dtype							,)
													controlnet_down_blocks.append(A			)

									if not is_final_block:
													__A             =			nn.Conv(
													    A							,kernel_size=(1, 1)							,padding="VALID"							,kernel_init=nn.initializers.zeros_init()							,bias_init=nn.initializers.zeros_init()							,dtype=self.dtype							,)
													controlnet_down_blocks.append(A			)

					__A             =			down_blocks
					__A             =			controlnet_down_blocks

					# mid
					__A             =			block_out_channels[-1]
					__A             =			FlaxUNetMidBlockaDCrossAttn(
					    in_channels=A							,dropout=self.dropout							,num_attention_heads=num_attention_heads[-1]							,use_linear_projection=self.use_linear_projection							,dtype=self.dtype							,)

					__A             =			nn.Conv(
					    A							,kernel_size=(1, 1)							,padding="VALID"							,kernel_init=nn.initializers.zeros_init()							,bias_init=nn.initializers.zeros_init()							,dtype=self.dtype							,)







	def __call__(	self :			Optional[int]							,A :			Union[str, Any]							,A :			Dict							,A :			int							,A :			int							,A :			float = 1.0							,A :			bool = True							,A :			bool = False							,):
					__A             =			self.controlnet_conditioning_channel_order
					if channel_order == "bgr":
									__A             =			jnp.flip(A							,axis=1			)

					# 1. time
					if not isinstance(A							,jnp.ndarray			):
									__A             =			jnp.array([timesteps]							,dtype=jnp.intaa			)
					elif isinstance(A							,jnp.ndarray			) and len(timesteps.shape			) == 0:
									__A             =			timesteps.astype(dtype=jnp.floataa			)
									__A             =			jnp.expand_dims(A							,0			)

					__A             =			self.time_proj(A			)
					__A             =			self.time_embedding(A			)

					# 2. pre-process
					__A             =			jnp.transpose(A							,(0, 2, 3, 1)			)
					__A             =			self.conv_in(A			)

					__A             =			jnp.transpose(A							,(0, 2, 3, 1)			)
					__A             =			self.controlnet_cond_embedding(A			)
					sample += controlnet_cond

					# 3. down
					__A             =			(sample,)
					for down_block in self.down_blocks:
									if isinstance(A							,A			):
													__A      ,							__A             =			down_block(A							,A							,A							,deterministic=not train			)
									else:
													__A      ,							__A             =			down_block(A							,A							,deterministic=not train			)
									down_block_res_samples += res_samples

					# 4. mid
					__A             =			self.mid_block(A							,A							,A							,deterministic=not train			)

					# 5. contronet blocks
					__A             =			()
					for down_block_res_sample, controlnet_block in zip(A							,self.controlnet_down_blocks			):
									__A             =			controlnet_block(A			)
									controlnet_down_block_res_samples += (down_block_res_sample,)

					__A             =			controlnet_down_block_res_samples

					__A             =			self.controlnet_mid_block(A			)

					# 6. scaling
					__A             =			[sample * conditioning_scale for sample in down_block_res_samples]
					mid_block_res_sample *= conditioning_scale

					if not return_dict:
									return (down_block_res_samples, mid_block_res_sample)

					return FlaxControlNetOutput(
					    down_block_res_samples=A							,mid_block_res_sample=A			)


from __future__ import annotations

import math
from collections import Counter
from string import ascii_lowercase


def 						UpperCAmelCase      (     a_						)		->     None:







				"""simple docstring"""



				__A      ,							__A             =			analyze_text(a_						)
				__A             =			list(" " + ascii_lowercase						)
				# what is our total sum of probabilities.
				__A             =			sum(single_char_strings.values()						)

				# one length string
				__A             =			0
				# for each alpha we go in our dict and if it is in it we calculate entropy
				for ch in my_alphas:
								if ch in single_char_strings:
												__A             =			single_char_strings[ch]
												__A             =			my_str / all_sum
												my_fir_sum += prob * math.loga(a_						)  # entropy formula.

    # print entropy
				print(F'''{round(-1 * my_fir_sum						):.1f}'''						)

				# two len string
				__A             =			sum(two_char_strings.values()						)
				__A             =			0
				# for each alpha (two in size) calculate entropy.
				for cha in my_alphas:
								for cha in my_alphas:
												__A             =			cha + cha
												if sequence in two_char_strings:
																__A             =			two_char_strings[sequence]
																__A             =			int(a_						) / all_sum
																my_sec_sum += prob * math.loga(a_						)

    # print second entropy
				print(F'''{round(-1 * my_sec_sum						):.1f}'''						)

				# print the difference between them
				print(F'''{round((-1 * my_sec_sum) - (-1 * my_fir_sum)						):.1f}'''						)


def 						UpperCAmelCase      (     a_						)		->     tuple[dict, dict]:







				"""simple docstring"""



				__A             =			Counter()  # type: ignore
				__A             =			Counter()  # type: ignore
				single_char_strings[text[-1]] += 1

				# first case when we have space at start.
				two_char_strings[" " + text[0]] += 1
				for i in range(0		,       len(a_						) - 1						):
								single_char_strings[text[i]] += 1
								two_char_strings[text[i : i + 2]] += 1
				return single_char_strings, two_char_strings


def 						UpperCAmelCase      (     )		->     Optional[Any]:







				"""simple docstring"""



				import doctest

				doctest.testmod()
				# text = (
				#     "Had repulsive dashwoods suspicion sincerity but advantage now him. Remark "
				#     "easily garret nor nay. Civil those mrs enjoy shy fat merry. You greatest "
				#     "jointure saw horrible. He private he on be imagine suppose. Fertile "
				#     "beloved evident through no service elderly is. Blind there if every no so "
				#     "at. Own neglected you preferred way sincerity delivered his attempted. To "
				#     "of message cottage windows do besides against uncivil.  Delightful "
				#     "unreserved impossible few estimating men favourable see entreaties. She "
				#     "propriety immediate was improving. He or entrance humoured likewise "
				#     "moderate. Much nor game son say feel. Fat make met can must form into "
				#     "gate. Me we offending prevailed discovery. "
				# )

				# calculate_prob(text)


if __name__ == "__main__":
					main()
