
from dataclasses import dataclass
from typing import Any, Dict, Optional

import torch
import torch.nn.functional as F
from torch import nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..models.embeddings import ImagePositionalEmbeddings
from ..utils import BaseOutput, deprecate
from .attention import BasicTransformerBlock
from .embeddings import PatchEmbed
from .modeling_utils import ModelMixin

@dataclass
class        UpperCamelCase__(    lowerCAmelCase__	):

	"""simple docstring"""

	_A  =		42


class        UpperCamelCase__(    lowerCAmelCase__						,						lowerCAmelCase__	):

	"""simple docstring"""

	@register_to_config
	def __init__(      self				:			Tuple							,			snake_case__				:			int = 16							,			snake_case__				:			int = 88							,			snake_case__				:			Optional[int] = None							,			snake_case__				:			Optional[int] = None							,			snake_case__				:			int = 1							,			snake_case__				:			float = 0.0							,			snake_case__				:			int = 32							,			snake_case__				:			Optional[int] = None							,			snake_case__				:			bool = False							,			snake_case__				:			Optional[int] = None							,			snake_case__				:			Optional[int] = None							,			snake_case__				:			Optional[int] = None							,			snake_case__				:			str = "geglu"							,			snake_case__				:			Optional[int] = None							,			snake_case__				:			bool = False							,			snake_case__				:			bool = False							,			snake_case__				:			bool = False							,			snake_case__				:			str = "layer_norm"							,			snake_case__				:			bool = True							,			):
			"""simple docstring"""





			super().__init__()
			A				=use_linear_projection
			A				=num_attention_heads
			A				=attention_head_dim
			A				=num_attention_heads * attention_head_dim

			# 1. Transformer2DModel can process both standard continuous images of shape `(batch_size, num_channels, width, height)` as well as quantized image embeddings of shape `(batch_size, num_image_vectors)`
			# Define whether input is continuous or discrete depending on configuration
			A				=(in_channels is not None) and (patch_size is None)
			A				=num_vector_embeds is not None
			A				=in_channels is not None and patch_size is not None

			if norm_type == "layer_norm" and num_embeds_ada_norm is not None:
					A				=(
					    f'''The configuration file of this model: {self.__class__} is outdated. `norm_type` is either not set or'''
					    " incorrectly set to `'layer_norm'`.Make sure to set `norm_type` to `'ada_norm'` in the config."
					    " Please make sure to update the config accordingly as leaving `norm_type` might led to incorrect"
					    " results in future versions. If you have downloaded this checkpoint from the Hugging Face Hub, it"
					    " would be very nice if you could open a Pull request for the `transformer/config.json` file"
					)
					deprecate("norm_type!=num_embeds_ada_norm"							,			"1.0.0"							,			snake_case__							,			standard_warn=snake_case__ )
					A				="ada_norm"

			if self.is_input_continuous and self.is_input_vectorized:
					raise ValueError(
					    f'''Cannot define both `in_channels`: {in_channels} and `num_vector_embeds`: {num_vector_embeds}. Make'''
					    " sure that either `in_channels` or `num_vector_embeds` is None." )
			elif self.is_input_vectorized and self.is_input_patches:
					raise ValueError(
					    f'''Cannot define both `num_vector_embeds`: {num_vector_embeds} and `patch_size`: {patch_size}. Make'''
					    " sure that either `num_vector_embeds` or `num_patches` is None." )
			elif not self.is_input_continuous and not self.is_input_vectorized and not self.is_input_patches:
					raise ValueError(
					    f'''Has to define `in_channels`: {in_channels}, `num_vector_embeds`: {num_vector_embeds}, or patch_size:'''
					    f''' {patch_size}. Make sure that `in_channels`, `num_vector_embeds` or `num_patches` is not None.''' )

			# 2. Define input layers
			if self.is_input_continuous:
					A				=in_channels

					A				=torch.nn.GroupNorm(num_groups=snake_case__							,			num_channels=snake_case__							,			eps=1E-6							,			affine=snake_case__ )
					if use_linear_projection:
							A				=nn.Linear(snake_case__							,			snake_case__ )
					else:
							A				=nn.Convad(snake_case__							,			snake_case__							,			kernel_size=1							,			stride=1							,			padding=0 )
			elif self.is_input_vectorized:
					assert sample_size is not None, "Transformer2DModel over discrete input must provide sample_size"
					assert num_vector_embeds is not None, "Transformer2DModel over discrete input must provide num_embed"

					A				=sample_size
					A				=sample_size
					A				=num_vector_embeds
					A				=self.height * self.width

					A				=ImagePositionalEmbeddings(
					    num_embed=snake_case__							,			embed_dim=snake_case__							,			height=self.height							,			width=self.width )
			elif self.is_input_patches:
					assert sample_size is not None, "Transformer2DModel over patched input must provide sample_size"

					A				=sample_size
					A				=sample_size

					A				=patch_size
					A				=PatchEmbed(
					    height=snake_case__							,			width=snake_case__							,			patch_size=snake_case__							,			in_channels=snake_case__							,			embed_dim=snake_case__							,			)

			# 3. Define transformers blocks
			A				=nn.ModuleList(
			    [
			        BasicTransformerBlock(
			            snake_case__							,			snake_case__							,			snake_case__							,			dropout=snake_case__							,			cross_attention_dim=snake_case__							,			activation_fn=snake_case__							,			num_embeds_ada_norm=snake_case__							,			attention_bias=snake_case__							,			only_cross_attention=snake_case__							,			upcast_attention=snake_case__							,			norm_type=snake_case__							,			norm_elementwise_affine=snake_case__							,			)
			        for d in range(snake_case__ )
			    ] )

			# 4. Define output layers
			A				=in_channels if out_channels is None else out_channels
			if self.is_input_continuous:
					# TODO: should use out_channels for continuous projections
					if use_linear_projection:
							A				=nn.Linear(snake_case__							,			snake_case__ )
					else:
							A				=nn.Convad(snake_case__							,			snake_case__							,			kernel_size=1							,			stride=1							,			padding=0 )
			elif self.is_input_vectorized:
					A				=nn.LayerNorm(snake_case__ )
					A				=nn.Linear(snake_case__							,			self.num_vector_embeds - 1 )
			elif self.is_input_patches:
					A				=nn.LayerNorm(snake_case__							,			elementwise_affine=snake_case__							,			eps=1E-6 )
					A				=nn.Linear(snake_case__							,			2 * inner_dim )
					A				=nn.Linear(snake_case__							,			patch_size * patch_size * self.out_channels )







	def        _a					(      self				:			Dict							,			snake_case__				:			torch.Tensor							,			snake_case__				:			Optional[torch.Tensor] = None							,			snake_case__				:			Optional[torch.LongTensor] = None							,			snake_case__				:			Optional[torch.LongTensor] = None							,			snake_case__				:			Dict[str, Any] = None							,			snake_case__				:			Optional[torch.Tensor] = None							,			snake_case__				:			Optional[torch.Tensor] = None							,			snake_case__				:			bool = True							,			):
			"""simple docstring"""





			if attention_mask is not None and attention_mask.ndim == 2:
					# assume that mask is expressed as:
					#   (1 = keep,      0 = discard)
					# convert mask into a bias that can be added to attention scores:
					#       (keep = +0,     discard = -10000.0)
					A				=(1 - attention_mask.to(hidden_states.dtype )) * -10_000.0
					A				=attention_mask.unsqueeze(1 )

			# convert encoder_attention_mask to a bias the same way we do for attention_mask
			if encoder_attention_mask is not None and encoder_attention_mask.ndim == 2:
					A				=(1 - encoder_attention_mask.to(hidden_states.dtype )) * -10_000.0
					A				=encoder_attention_mask.unsqueeze(1 )

			# 1. Input
			if self.is_input_continuous:
					A			,    A			,    A			,    A				=hidden_states.shape
					A				=hidden_states

					A				=self.norm(snake_case__ )
					if not self.use_linear_projection:
							A				=self.proj_in(snake_case__ )
							A				=hidden_states.shape[1]
							A				=hidden_states.permute(0							,			2							,			3							,			1 ).reshape(snake_case__							,			height * width							,			snake_case__ )
					else:
							A				=hidden_states.shape[1]
							A				=hidden_states.permute(0							,			2							,			3							,			1 ).reshape(snake_case__							,			height * width							,			snake_case__ )
							A				=self.proj_in(snake_case__ )
			elif self.is_input_vectorized:
					A				=self.latent_image_embedding(snake_case__ )
			elif self.is_input_patches:
					A				=self.pos_embed(snake_case__ )

			# 2. Blocks
			for block in self.transformer_blocks:
					A				=block(
					    snake_case__							,			attention_mask=snake_case__							,			encoder_hidden_states=snake_case__							,			encoder_attention_mask=snake_case__							,			timestep=snake_case__							,			cross_attention_kwargs=snake_case__							,			class_labels=snake_case__							,			)

			# 3. Output
			if self.is_input_continuous:
					if not self.use_linear_projection:
							A				=hidden_states.reshape(snake_case__							,			snake_case__							,			snake_case__							,			snake_case__ ).permute(0							,			3							,			1							,			2 ).contiguous()
							A				=self.proj_out(snake_case__ )
					else:
							A				=self.proj_out(snake_case__ )
							A				=hidden_states.reshape(snake_case__							,			snake_case__							,			snake_case__							,			snake_case__ ).permute(0							,			3							,			1							,			2 ).contiguous()

					A				=hidden_states + residual
			elif self.is_input_vectorized:
					A				=self.norm_out(snake_case__ )
					A				=self.out(snake_case__ )
					# (batch, self.num_vector_embeds - 1, self.num_latent_pixels)
					A				=logits.permute(0							,			2							,			1 )

					# log(p(x_0))
					A				=F.log_softmax(logits.double()							,			dim=1 ).float()
			elif self.is_input_patches:
					# TODO: cleanup!
					A				=self.transformer_blocks[0].norma.emb(
					    snake_case__							,			snake_case__							,			hidden_dtype=hidden_states.dtype )
					A			,    A				=self.proj_out_a(F.silu(snake_case__ ) ).chunk(2							,			dim=1 )
					A				=self.norm_out(snake_case__ ) * (1 + scale[:, None]) + shift[:, None]
					A				=self.proj_out_a(snake_case__ )

					# unpatchify
					A				=A				=int(hidden_states.shape[1] ** 0.5 )
					A				=hidden_states.reshape(
					    shape=(-1, height, width, self.patch_size, self.patch_size, self.out_channels) )
					A				=torch.einsum("nhwpqc->nchpwq"							,			snake_case__ )
					A				=hidden_states.reshape(
					    shape=(-1, self.out_channels, height * self.patch_size, width * self.patch_size) )

			if not return_dict:
					return (output,)

			return TransformeraDModelOutput(sample=snake_case__ )






import functools
from math import sqrt
from time import time

def    UpperCamelCase_ (	a_  ,				*a_  ,				**a_		)     ->List[Any]:
		A				=time()
		A				=func(*a_  ,				**a_		)
		A				=time()
		if int(end - start		) > 0:
				print(f'''{func.__name__} runtime: {(end - start):0.4f} s'''		)
		else:
				print(f'''{func.__name__} runtime: {(end - start) * 1000:0.4f} ms'''		)
		return output

def    UpperCamelCase_ (	a_		)     ->list[int]:
		if n < 0:
				raise Exception("n is negative"		)
		if n == 0:
				return [0]
		A				=[0, 1]
		for _ in range(n - 1		):
				fib.append(fib[-1] + fib[-2]		)
		return fib

def    UpperCamelCase_ (	a_		)     ->list[int]:

		def fib_recursive_term(a_		) -> int:
				if i < 0:
						raise Exception("n is negative"		)
				if i < 2:
						return i
				return fib_recursive_term(i - 1		) + fib_recursive_term(i - 2		)

		if n < 0:
				raise Exception("n is negative"		)
		return [fib_recursive_term(a_		) for i in range(n + 1		)]

def    UpperCamelCase_ (	a_		)     ->list[int]:

		@functools.cache
		def fib_recursive_term(a_		) -> int:
				if i < 0:
						raise Exception("n is negative"		)
				if i < 2:
						return i
				return fib_recursive_term(i - 1		) + fib_recursive_term(i - 2		)

		if n < 0:
				raise Exception("n is negative"		)
		return [fib_recursive_term(a_		) for i in range(n + 1		)]

def    UpperCamelCase_ (	a_		)     ->list[int]:
		if n < 0:
				raise Exception("n is negative"		)
		# Cache must be outside recursuive function
		# other it will reset every time it calls itself.
		A				={0: 0, 1: 1, 2: 1}  # Prefilled cache

		def rec_fn_memoized(a_		) -> int:
				if num in cache:
						return cache[num]

				A				=rec_fn_memoized(num - 1		) + rec_fn_memoized(num - 2		)
				A				=value
				return value

		return [rec_fn_memoized(a_		) for i in range(n + 1		)]

def    UpperCamelCase_ (	a_		)     ->list[int]:
		if n < 0:
				raise Exception("n is negative"		)
		if n >= 1475:
				raise Exception("n is too large"		)
		A				=sqrt(5		)
		A				=(1 + sqrt_a) / 2
		return [round(phi**i / sqrt_a		) for i in range(n + 1		)]


if __name__ == "__main__":
	__a							  =						3_0
	time_func(fib_iterative, num)
	time_func(fib_recursive, num)  # Around 3s runtime
	time_func(fib_recursive_cached, num)  # Around 0ms runtime
	time_func(fib_memoization, num)
	time_func(fib_binet, num)




