


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 lowerCAmelCase_		(				UpperCamelCase_ ):

 '''simple docstring'''

 __snake_case				=						4_2
 __snake_case				=						4_2







class lowerCAmelCase_		(				nn.Module ):

 '''simple docstring'''

 __snake_case				=						4_2
 __snake_case				=						(1_6, 3_2, 9_6, 2_5_6)
 __snake_case				=						jnp.floataa



 def 		UpperCamelCase__  (			self      ):
       snake_case_ =   nn.Conv(
           self.block_out_channels[0]	,   kernel_size=(3, 3)	,   padding=((1, 1), (1, 1))	,   dtype=self.dtype	,   )

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

       snake_case_ =   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	,   _UpperCAmelCase      ):
       snake_case_ =   self.conv_in(__a      )
       snake_case_ =   nn.silu(__a      )

       for block in self.blocks:
             snake_case_ =   block(__a      )
             snake_case_ =   nn.silu(__a      )

       snake_case_ =   self.conv_out(__a      )

       return embedding







@flax_register_to_config
class lowerCAmelCase_		(				nn.Module      ,		UpperCamelCase_      ,		UpperCamelCase_ ):

 '''simple docstring'''

 __snake_case				=						3_2
 __snake_case				=						4
 __snake_case				=						(
     "CrossAttnDownBlock2D",
     "CrossAttnDownBlock2D",
     "CrossAttnDownBlock2D",
     "DownBlock2D",
 )
 __snake_case				=						False
 __snake_case				=						(3_2_0, 6_4_0, 1_2_8_0, 1_2_8_0)
 __snake_case				=						2
 __snake_case				=						8
 __snake_case				=						None
 __snake_case				=						1_2_8_0
 __snake_case				=						0.0
 __snake_case				=						False
 __snake_case				=						jnp.floataa
 __snake_case				=						True
 __snake_case				=						0
 __snake_case				=						"rgb"
 __snake_case				=						(1_6, 3_2, 9_6, 2_5_6)



 def 		UpperCamelCase__  (			self	,   _UpperCAmelCase      ):
       snake_case_ =   (1, self.in_channels, self.sample_size, self.sample_size)
       snake_case_ =   jnp.zeros(__a	,   dtype=jnp.floataa      )
       snake_case_ =   jnp.ones((1,)	,   dtype=jnp.intaa      )
       snake_case_ =   jnp.zeros((1, 1, self.cross_attention_dim)	,   dtype=jnp.floataa      )
       snake_case_ =   (1, 3, self.sample_size * 8, self.sample_size * 8)
       snake_case_ =   jnp.zeros(__a	,   dtype=jnp.floataa      )

       snake_case_ =   jax.random.split(__a      )
       snake_case_ =   {'params': params_rng, 'dropout': dropout_rng}

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



 def 		UpperCamelCase__  (			self      ):
       snake_case_ =   self.block_out_channels
       snake_case_ =   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.
       snake_case_ =   self.num_attention_heads or self.attention_head_dim

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

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

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

       snake_case_ =   self.only_cross_attention
       if isinstance(__a	,   __a      ):
             snake_case_ =   (only_cross_attention,) * len(self.down_block_types      )

       if isinstance(__a	,   __a      ):
             snake_case_ =   (num_attention_heads,) * len(self.down_block_types      )

       # down
       snake_case_ =   []
       snake_case_ =   []

       snake_case_ =   block_out_channels[0]

       snake_case_ =   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      ):
             snake_case_ =   output_channel
             snake_case_ =   block_out_channels[i]
             snake_case_ =   i == len(__a      ) - 1

             if down_block_type == "CrossAttnDownBlock2D":
                   snake_case_ =   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:
                   snake_case_ =   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      ):
                   snake_case_ =   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:
                   snake_case_ =   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      )

       snake_case_ =   down_blocks
       snake_case_ =   controlnet_down_blocks

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

       snake_case_ =   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	,   _UpperCAmelCase	,   _UpperCAmelCase	,   _UpperCAmelCase	,   _UpperCAmelCase	,   _UpperCAmelCase = 1.0	,   _UpperCAmelCase = True	,   _UpperCAmelCase = False	,   ):
       snake_case_ =   self.controlnet_conditioning_channel_order
       if channel_order == "bgr":
             snake_case_ =   jnp.flip(__a	,   axis=1      )

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

       snake_case_ =   self.time_proj(__a      )
       snake_case_ =   self.time_embedding(__a      )

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

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

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

       # 4. mid
       snake_case_ =   self.mid_block(__a	,   __a	,   __a	,   deterministic=not train      )

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

       snake_case_ =   controlnet_down_block_res_samples

       snake_case_ =   self.controlnet_mid_block(__a      )

       # 6. scaling
       snake_case_ =   [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      )



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  __lowerCAmelCase					(SCREAMING_SNAKE_CASE=None      ,      SCREAMING_SNAKE_CASE=None      )->						Tuple:




      """simple docstring"""

      return field(default_factory=lambda: default      ,      metadata=SCREAMING_SNAKE_CASE      )







@dataclass
class lowerCAmelCase_		:

 '''simple docstring'''

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






def  __lowerCAmelCase					(SCREAMING_SNAKE_CASE      )->						Optional[Any]:




      """simple docstring"""

      try:
            int(SCREAMING_SNAKE_CASE      )
            return True
      except ValueError:
            return False






def  __lowerCAmelCase					(SCREAMING_SNAKE_CASE      )->						Tuple:




      """simple docstring"""

      try:
            float(SCREAMING_SNAKE_CASE      )
            return True
      except ValueError:
            return False







class lowerCAmelCase_		:

 '''simple docstring'''

 def __init__(			self	,   _UpperCAmelCase      ):
       snake_case_ =   args
       snake_case_ =   defaultdict(lambda: {"bsz": [], "seq_len": [], "result": {}}      )

       with open(self.args.csv_file	,   newline=''''''      ) as csv_file:
             snake_case_ =   csv.DictReader(_UpperCAmelCase      )
             for row in reader:
                   snake_case_ =   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
                         snake_case_ =   int(row['''result''']      )
                   elif can_convert_to_float(row['''result''']      ):
                         # value is not None
                         snake_case_ =   float(row['''result''']      )





 def 		UpperCamelCase__  (			self      ):
       snake_case_ ,					snake_case_ =   plt.subplots()
       snake_case_ =   '''Time usage''' if self.args.is_time else '''Memory usage'''
       snake_case_ =   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()      ):
             snake_case_ =   sorted(set(self.result_dict[model_name]['''bsz''']      )      )
             snake_case_ =   sorted(set(self.result_dict[model_name]['''seq_len''']      )      )
             snake_case_ =   self.result_dict[model_name]['''result''']

             ((snake_case_) ,					(snake_case_)) =   (
                 (batch_sizes, sequence_lengths) if self.args.plot_along_batch else (sequence_lengths, batch_sizes)
             )

             snake_case_ =   (
                 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:
                         snake_case_ =   np.asarray(
                             [results[(x, inner_loop_value)] for x in x_axis_array if (x, inner_loop_value) in results]	,   dtype=_UpperCAmelCase	,   )
                   else:
                         snake_case_ =   np.asarray(
                             [results[(inner_loop_value, x)] for x in x_axis_array if (inner_loop_value, x) in results]	,   dtype=np.floataa	,   )

                   ((snake_case_) ,					(snake_case_)) =   (
                       ('''batch_size''', '''len''') if self.args.plot_along_batch else ('''in #tokens''', '''bsz''')
                   )

                   snake_case_ =   np.asarray(_UpperCAmelCase	,   _UpperCAmelCase      )[: len(_UpperCAmelCase      )]
                   plt.scatter(
                       _UpperCAmelCase	,   _UpperCAmelCase	,   label=F'''{label_model_name} - {inner_loop_label}: {inner_loop_value}'''      )
                   plt.plot(_UpperCAmelCase	,   _UpperCAmelCase	,   '''--'''      )

             title_str += F''' {label_model_name} vs.'''

       snake_case_ =   title_str[:-4]
       snake_case_ =   '''Time in s''' if self.args.is_time else '''Memory in MB'''

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

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






def  __lowerCAmelCase					()->						int:




      """simple docstring"""

      snake_case_ =   HfArgumentParser(SCREAMING_SNAKE_CASE      )
      snake_case_ =   parser.parse_args_into_dataclasses()[0]
      snake_case_ =   Plot(args=SCREAMING_SNAKE_CASE      )
      plot.plot()


if __name__ == "__main__":
   main()