





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        __magic_name__	(				lowerCamelCase__				):





     """simple docstring"""



     __UpperCamelCase            =  42
     __UpperCamelCase            =  42




class        __magic_name__	(				nn.Module				):





     """simple docstring"""



     __UpperCamelCase            =  42
     __UpperCamelCase            =  (16, 32, 96, 2_56)
     __UpperCamelCase            =  jnp.floataa



     def     SCREAMING_SNAKE_CASE		(				self							:Optional[int]	):





        '''simple docstring'''




        A_       :					Any        =      nn.Conv(
            self.block_out_channels[0]      ,   kernel_size=(3, 3)      ,   padding=((1, 1), (1, 1))      ,   dtype=self.dtype      ,   )

        A_       :					Union[str, Any]        =      []
        for i in range(len(self.block_out_channels	) - 1	):
           A_       :					Union[str, Any]        =      self.block_out_channels[i]
           A_       :					str        =      self.block_out_channels[i + 1]
           A_       :					Optional[Any]        =      nn.Conv(
               snake_case      ,   kernel_size=(3, 3)      ,   padding=((1, 1), (1, 1))      ,   dtype=self.dtype      ,   )
           blocks.append(snake_case	)
           A_       :					Tuple        =      nn.Conv(
               snake_case      ,   kernel_size=(3, 3)      ,   strides=(2, 2)      ,   padding=((1, 1), (1, 1))      ,   dtype=self.dtype      ,   )
           blocks.append(snake_case	)
        A_       :					Union[str, Any]        =      blocks

        A_       :					Optional[Any]        =      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							:str      ,   snake_case							:Optional[Any]	):





        '''simple docstring'''




        A_       :					Optional[Any]        =      self.conv_in(snake_case	)
        A_       :					int        =      nn.silu(snake_case	)

        for block in self.blocks:
           A_       :					Union[str, Any]        =      block(snake_case	)
           A_       :					Tuple        =      nn.silu(snake_case	)

        A_       :					List[str]        =      self.conv_out(snake_case	)

        return embedding




@flax_register_to_config
class        __magic_name__	(				nn.Module     ,					lowerCamelCase__     ,					lowerCamelCase__				):





     """simple docstring"""



     __UpperCamelCase            =  32
     __UpperCamelCase            =  4
     __UpperCamelCase            =  (
         "CrossAttnDownBlock2D",
         "CrossAttnDownBlock2D",
         "CrossAttnDownBlock2D",
         "DownBlock2D",
     )
     __UpperCamelCase            =  False
     __UpperCamelCase            =  (3_20, 6_40, 12_80, 12_80)
     __UpperCamelCase            =  2
     __UpperCamelCase            =  8
     __UpperCamelCase            =  None
     __UpperCamelCase            =  12_80
     __UpperCamelCase            =  0.0
     __UpperCamelCase            =  False
     __UpperCamelCase            =  jnp.floataa
     __UpperCamelCase            =  True
     __UpperCamelCase            =  0
     __UpperCamelCase            =  "rgb"
     __UpperCamelCase            =  (16, 32, 96, 2_56)



     def     SCREAMING_SNAKE_CASE		(				self							:Optional[int]      ,   snake_case							:jax.random.KeyArray	):





        '''simple docstring'''




        A_       :					Dict        =      (1, self.in_channels, self.sample_size, self.sample_size)
        A_       :					List[str]        =      jnp.zeros(snake_case      ,   dtype=jnp.floataa	)
        A_       :					Tuple        =      jnp.ones((1,)      ,   dtype=jnp.intaa	)
        A_       :					int        =      jnp.zeros((1, 1, self.cross_attention_dim)      ,   dtype=jnp.floataa	)
        A_       :					Tuple        =      (1, 3, self.sample_size * 8, self.sample_size * 8)
        A_       :					int        =      jnp.zeros(snake_case      ,   dtype=jnp.floataa	)

        A_   ,    A_       :					Any        =      jax.random.split(snake_case	)
        A_       :					Any        =      {"params": params_rng, "dropout": dropout_rng}

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



     def     SCREAMING_SNAKE_CASE		(				self							:List[Any]	):





        '''simple docstring'''




        A_       :					Any        =      self.block_out_channels
        A_       :					Tuple        =      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_       :					int        =      self.num_attention_heads or self.attention_head_dim

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

        # time
        A_       :					List[str]        =      FlaxTimesteps(
            block_out_channels[0]      ,   flip_sin_to_cos=self.flip_sin_to_cos      ,   freq_shift=self.config.freq_shift	)
        A_       :					Tuple        =      FlaxTimestepEmbedding(snake_case      ,   dtype=self.dtype	)

        A_       :					int        =      FlaxControlNetConditioningEmbedding(
            conditioning_embedding_channels=block_out_channels[0]      ,   block_out_channels=self.conditioning_embedding_out_channels      ,   )

        A_       :					Tuple        =      self.only_cross_attention
        if isinstance(snake_case      ,   snake_case	):
           A_       :					Optional[Any]        =      (only_cross_attention,) * len(self.down_block_types	)

        if isinstance(snake_case      ,   snake_case	):
           A_       :					int        =      (num_attention_heads,) * len(self.down_block_types	)

        # down
        A_       :					List[str]        =      []
        A_       :					Union[str, Any]        =      []

        A_       :					str        =      block_out_channels[0]

        A_       :					Dict        =      nn.Conv(
            snake_case      ,   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(snake_case	)

        for i, down_block_type in enumerate(self.down_block_types	):
           A_       :					Any        =      output_channel
           A_       :					Union[str, Any]        =      block_out_channels[i]
           A_       :					List[Any]        =      i == len(snake_case	) - 1

           if down_block_type == "CrossAttnDownBlock2D":
              A_       :					List[Any]        =      FlaxCrossAttnDownBlockaD(
                  in_channels=snake_case      ,   out_channels=snake_case      ,   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_       :					Optional[int]        =      FlaxDownBlockaD(
                  in_channels=snake_case      ,   out_channels=snake_case      ,   dropout=self.dropout      ,   num_layers=self.layers_per_block      ,   add_downsample=not is_final_block      ,   dtype=self.dtype      ,   )

           down_blocks.append(snake_case	)

           for _ in range(self.layers_per_block	):
              A_       :					List[str]        =      nn.Conv(
                  snake_case      ,   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(snake_case	)

           if not is_final_block:
              A_       :					Optional[Any]        =      nn.Conv(
                  snake_case      ,   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(snake_case	)

        A_       :					Optional[Any]        =      down_blocks
        A_       :					str        =      controlnet_down_blocks

        # mid
        A_       :					List[str]        =      block_out_channels[-1]
        A_       :					List[str]        =      FlaxUNetMidBlockaDCrossAttn(
            in_channels=snake_case      ,   dropout=self.dropout      ,   num_attention_heads=num_attention_heads[-1]      ,   use_linear_projection=self.use_linear_projection      ,   dtype=self.dtype      ,   )

        A_       :					Any        =      nn.Conv(
            snake_case      ,   kernel_size=(1, 1)      ,   padding="VALID"      ,   kernel_init=nn.initializers.zeros_init()      ,   bias_init=nn.initializers.zeros_init()      ,   dtype=self.dtype      ,   )





     def __call__(				self							:int      ,   snake_case							:List[Any]      ,   snake_case							:Dict      ,   snake_case							:Tuple      ,   snake_case							:int      ,   snake_case							:float = 1.0      ,   snake_case							:bool = True      ,   snake_case							:bool = False      ,   ):





        '''simple docstring'''




        A_       :					List[Any]        =      self.controlnet_conditioning_channel_order
        if channel_order == "bgr":
           A_       :					Optional[Any]        =      jnp.flip(snake_case      ,   axis=1	)

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

        A_       :					Any        =      self.time_proj(snake_case	)
        A_       :					Dict        =      self.time_embedding(snake_case	)

        # 2. pre-process
        A_       :					int        =      jnp.transpose(snake_case      ,   (0, 2, 3, 1)	)
        A_       :					Tuple        =      self.conv_in(snake_case	)

        A_       :					Dict        =      jnp.transpose(snake_case      ,   (0, 2, 3, 1)	)
        A_       :					int        =      self.controlnet_cond_embedding(snake_case	)
        sample += controlnet_cond

        # 3. down
        A_       :					Optional[int]        =      (sample,)
        for down_block in self.down_blocks:
           if isinstance(snake_case      ,   snake_case	):
              A_   ,    A_       :					int        =      down_block(snake_case      ,   snake_case      ,   snake_case      ,   deterministic=not train	)
           else:
              A_   ,    A_       :					Any        =      down_block(snake_case      ,   snake_case      ,   deterministic=not train	)
           down_block_res_samples += res_samples

        # 4. mid
        A_       :					str        =      self.mid_block(snake_case      ,   snake_case      ,   snake_case      ,   deterministic=not train	)

        # 5. contronet blocks
        A_       :					int        =      ()
        for down_block_res_sample, controlnet_block in zip(snake_case      ,   self.controlnet_down_blocks	):
           A_       :					Union[str, Any]        =      controlnet_block(snake_case	)
           controlnet_down_block_res_samples += (down_block_res_sample,)

        A_       :					Optional[int]        =      controlnet_down_block_res_samples

        A_       :					Optional[Any]        =      self.controlnet_mid_block(snake_case	)

        # 6. scaling
        A_       :					int        =      [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=snake_case      ,   mid_block_res_sample=snake_case	)







import csv
from collections import defaultdict
from dataclasses import dataclass, field
from typing import List, Optional

import matplotlib.pyplot as plt
import numpy as np
from matplotlib.ticker import ScalarFormatter

from transformers import HfArgumentParser

def   __snake_case    (       _lowerCAmelCase			:			Optional[int]=None					,					_lowerCAmelCase			:			List[Any]=None						)			->    Optional[Any]:
   return field(default_factory=lambda: default					,					metadata=_lowerCAmelCase						)


@dataclass
class        __magic_name__	:





     """simple docstring"""



     __UpperCamelCase            =  field(
         metadata={'''help''': '''The csv file to plot.'''}     ,					)
     __UpperCamelCase            =  field(
         default=lowerCamelCase__     ,					metadata={'''help''': '''Whether to plot along batch size or sequence length. Defaults to sequence length.'''}     ,					)
     __UpperCamelCase            =  field(
         default=lowerCamelCase__     ,					metadata={'''help''': '''Whether the csv file has time results or memory results. Defaults to memory results.'''}     ,					)
     __UpperCamelCase            =  field(
         default=lowerCamelCase__     ,					metadata={'''help''': '''Disable logarithmic scale when plotting'''}     ,					)
     __UpperCamelCase            =  field(
         default=lowerCamelCase__     ,					metadata={
             '''help''': '''Whether the csv file has training results or inference results. Defaults to inference results.'''
         }     ,					)
     __UpperCamelCase            =  field(
         default=lowerCamelCase__     ,					metadata={'''help''': '''Filename under which the plot will be saved. If unused no plot is saved.'''}     ,					)
     __UpperCamelCase            =  list_field(
         default=lowerCamelCase__     ,					metadata={'''help''': '''List of model names that are used instead of the ones in the csv file.'''}				)




def   __snake_case    (       _lowerCAmelCase			:			int						)			->    Optional[Any]:
   try:
      int(_lowerCAmelCase						)
      return True
   except ValueError:
      return False

def   __snake_case    (       _lowerCAmelCase			:			str						)			->    Union[str, Any]:
   try:
      float(_lowerCAmelCase						)
      return True
   except ValueError:
      return False


class        __magic_name__	:





     """simple docstring"""



     def __init__(				self							:Any      ,   snake_case							:List[Any]	):





        '''simple docstring'''




        A_       :					Any        =      args
        A_       :					Dict        =      defaultdict(lambda: {"bsz": [], "seq_len": [], "result": {}}	)

        with open(self.args.csv_file      ,   newline=""	) as csv_file:
           A_       :					Union[str, Any]        =      csv.DictReader(snake_case	)
           for row in reader:
              A_       :					Any        =      row["model"]
              self.result_dict[model_name]["bsz"].append(int(row["batch_size"]	)	)
              self.result_dict[model_name]["seq_len"].append(int(row["sequence_length"]	)	)
              if can_convert_to_int(row["result"]	):
                 # value is not None
                 A_       :					List[Any]        =      int(row["result"]	)
              elif can_convert_to_float(row["result"]	):
                 # value is not None
                 A_       :					Optional[Any]        =      float(row["result"]	)





     def     SCREAMING_SNAKE_CASE		(				self							:Any	):





        '''simple docstring'''




        A_   ,    A_       :					List[str]        =      plt.subplots()
        A_       :					List[str]        =      "Time usage" if self.args.is_time else "Memory usage"
        A_       :					Optional[Any]        =      title_str + " for training" if self.args.is_train else title_str + " for inference"

        if not self.args.no_log_scale:
           # set logarithm scales
           ax.set_xscale("log"	)
           ax.set_yscale("log"	)

        for axis in [ax.xaxis, ax.yaxis]:
           axis.set_major_formatter(ScalarFormatter()	)

        for model_name_idx, model_name in enumerate(self.result_dict.keys()	):
           A_       :					int        =      sorted(set(self.result_dict[model_name]["bsz"]	)	)
           A_       :					List[str]        =      sorted(set(self.result_dict[model_name]["seq_len"]	)	)
           A_       :					Dict        =      self.result_dict[model_name]["result"]

           ((A_)   ,    (A_))       :					Optional[Any]        =      (
               (batch_sizes, sequence_lengths) if self.args.plot_along_batch else (sequence_lengths, batch_sizes)
           )

           A_       :					List[Any]        =      (
               model_name if self.args.short_model_names is None else self.args.short_model_names[model_name_idx]
           )

           for inner_loop_value in inner_loop_array:
              if self.args.plot_along_batch:
                 A_       :					Dict        =      np.asarray(
                     [results[(x, inner_loop_value)] for x in x_axis_array if (x, inner_loop_value) in results]      ,   dtype=snake_case      ,   )
              else:
                 A_       :					Union[str, Any]        =      np.asarray(
                     [results[(inner_loop_value, x)] for x in x_axis_array if (inner_loop_value, x) in results]      ,   dtype=np.floataa      ,   )

              ((A_)   ,    (A_))       :					Optional[Any]        =      (
                  ("batch_size", "len") if self.args.plot_along_batch else ("in #tokens", "bsz")
              )

              A_       :					List[Any]        =      np.asarray(snake_case      ,   snake_case	)[: len(snake_case	)]
              plt.scatter(
                  snake_case      ,   snake_case      ,   label=f"{label_model_name} - {inner_loop_label}: {inner_loop_value}"	)
              plt.plot(snake_case      ,   snake_case      ,   "--"	)

           title_str += f" {label_model_name} vs."

        A_       :					Optional[int]        =      title_str[:-4]
        A_       :					List[str]        =      "Time in s" if self.args.is_time else "Memory in MB"

        # plot
        plt.title(snake_case	)
        plt.xlabel(snake_case	)
        plt.ylabel(snake_case	)
        plt.legend()

        if self.args.figure_png_file is not None:
           plt.savefig(self.args.figure_png_file	)
        else:
           plt.show()




def   __snake_case    (       )			->    Any:
   A_       :					Any        =      HfArgumentParser(_lowerCAmelCase						)
   A_       :					str        =      parser.parse_args_into_dataclasses()[0]
   A_       :					Optional[Any]        =      Plot(args=_lowerCAmelCase						)
   plot.plot()


if __name__ == "__main__":
 main()
