
import inspect
import re
from hashlib import shaaaa
from typing import Dict, List

from .arrow import arrow
from .audiofolder import audiofolder
from .csv import csv
from .imagefolder import imagefolder
from .json import json
from .pandas import pandas
from .parquet import parquet
from .sql import sql  # noqa F401
from .text import text


def 			UpperCamelCase					(    __lowerCamelCase     : List[str]    ):
       snake_case       :   List[str]							=      []
       for line in lines:
              snake_case       :   List[Any]							=      re.sub(r"#.*"				,					""				,					__lowerCamelCase    )  # remove comments
              if line:
                     filtered_lines.append(__lowerCamelCase    )
       snake_case       :   Optional[Any]							=      "\n".join(__lowerCamelCase    )

       # Make a hash from all this code
       snake_case       :   Tuple							=      full_str.encode("utf-8"    )
       return shaaaa(__lowerCamelCase    ).hexdigest()


# get importable module names and hash for caching
__lowerCamelCase							=       {
    """csv""": (csv.__name__, _hash_python_lines(inspect.getsource(csv).splitlines())),
    """json""": (json.__name__, _hash_python_lines(inspect.getsource(json).splitlines())),
    """pandas""": (pandas.__name__, _hash_python_lines(inspect.getsource(pandas).splitlines())),
    """parquet""": (parquet.__name__, _hash_python_lines(inspect.getsource(parquet).splitlines())),
    """arrow""": (arrow.__name__, _hash_python_lines(inspect.getsource(arrow).splitlines())),
    """text""": (text.__name__, _hash_python_lines(inspect.getsource(text).splitlines())),
    """imagefolder""": (imagefolder.__name__, _hash_python_lines(inspect.getsource(imagefolder).splitlines())),
    """audiofolder""": (audiofolder.__name__, _hash_python_lines(inspect.getsource(audiofolder).splitlines())),
}

# Used to infer the module to use based on the data files extensions
__lowerCamelCase							=       {
    """.csv""": ("""csv""", {}),
    """.tsv""": ("""csv""", {"""sep""": """\t"""}),
    """.json""": ("""json""", {}),
    """.jsonl""": ("""json""", {}),
    """.parquet""": ("""parquet""", {}),
    """.arrow""": ("""arrow""", {}),
    """.txt""": ("""text""", {}),
}
_EXTENSION_TO_MODULE.update({ext: ("""imagefolder""", {}) for ext in imagefolder.ImageFolder.EXTENSIONS})
_EXTENSION_TO_MODULE.update({ext.upper(): ("""imagefolder""", {}) for ext in imagefolder.ImageFolder.EXTENSIONS})
_EXTENSION_TO_MODULE.update({ext: ("""audiofolder""", {}) for ext in audiofolder.AudioFolder.EXTENSIONS})
_EXTENSION_TO_MODULE.update({ext.upper(): ("""audiofolder""", {}) for ext in audiofolder.AudioFolder.EXTENSIONS})
__lowerCamelCase							=       {"""imagefolder""", """audiofolder"""}

# Used to filter data files based on extensions given a module name
__lowerCamelCase							=       {}
for _ext, (_module, _) in _EXTENSION_TO_MODULE.items():
   _MODULE_TO_EXTENSIONS.setdefault(_module, []).append(_ext)

_MODULE_TO_EXTENSIONS["imagefolder"].append(""".zip""")




_MODULE_TO_EXTENSIONS["audiofolder"].append(""".zip""")







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,
    FlaxCrossAttnUpBlockaD,
    FlaxDownBlockaD,
    FlaxUNetMidBlockaDCrossAttn,
    FlaxUpBlockaD,
)






@flax.struct.dataclass
class   UpperCAmelCase		(						A_ ):
   A__					:		jnp.ndarray






@flax_register_to_config
class   UpperCAmelCase		(						nn.Module       ,A_       ,A_ ):
   A__					:		int						     =     32
   A__					:		int						     =     4
   A__					:		int						     =     4
   A__					:		Tuple[str]						     =     (
       "CrossAttnDownBlock2D",
       "CrossAttnDownBlock2D",
       "CrossAttnDownBlock2D",
       "DownBlock2D",
   )
   A__					:		Tuple[str]						     =     ("UpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D")
   A__					:		Union[bool, Tuple[bool]]						     =     False
   A__					:		Tuple[int]						     =     (3_20, 6_40, 12_80, 12_80)
   A__					:		int						     =     2
   A__					:		Union[int, Tuple[int]]						     =     8
   A__					:		Optional[Union[int, Tuple[int]]]						     =     None
   A__					:		int						     =     12_80
   A__					:		float						     =     0.0
   A__					:		bool						     =     False
   A__					:		jnp.dtype						     =     jnp.floataa
   A__					:		bool						     =     True
   A__					:		int						     =     0
   A__					:		bool						     =     False



   def 				_SCREAMING_SNAKE_CASE   (self		:					Optional[int]    ,						snake_case__		:					jax.random.KeyArray )		->	FrozenDict:


          '''simple docstring'''


          snake_case       :   Dict							=      (1, self.in_channels, self.sample_size, self.sample_size)
          snake_case       :   Any							=      jnp.zeros(snake_case__    ,						dtype=jnp.floataa )
          snake_case       :   List[str]							=      jnp.ones((1,)    ,						dtype=jnp.intaa )
          snake_case       :   str							=      jnp.zeros((1, 1, self.cross_attention_dim)    ,						dtype=jnp.floataa )

          snake_case				,					snake_case       :   Optional[int]							=      jax.random.split(snake_case__ )
          snake_case       :   Union[str, Any]							=      {"params": params_rng, "dropout": dropout_rng}

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



   def 				_SCREAMING_SNAKE_CASE   (self		:					str )		->	Tuple:


          '''simple docstring'''


          snake_case       :   str							=      self.block_out_channels
          snake_case       :   Optional[Any]							=      block_out_channels[0] * 4

          if self.num_attention_heads is not None:
                 raise ValueError(
                     "At the moment it is not possible to define the number of attention heads via `num_attention_heads` because of a naming issue as described in https://github.com/huggingface/diffusers/issues/2011#issuecomment-1547958131. Passing `num_attention_heads` will only be supported in diffusers v0.19." )

          # 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       :   Tuple							=      self.num_attention_heads or self.attention_head_dim

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

          # time
          snake_case       :   Union[str, Any]							=      FlaxTimesteps(
              block_out_channels[0]    ,						flip_sin_to_cos=self.flip_sin_to_cos    ,						freq_shift=self.config.freq_shift )
          snake_case       :   Dict							=      FlaxTimestepEmbedding(snake_case__    ,						dtype=self.dtype )

          snake_case       :   List[str]							=      self.only_cross_attention
          if isinstance(snake_case__    ,						snake_case__ ):
                 snake_case       :   List[Any]							=      (only_cross_attention,) * len(self.down_block_types )

          if isinstance(snake_case__    ,						snake_case__ ):
                 snake_case       :   List[Any]							=      (num_attention_heads,) * len(self.down_block_types )

          # down
          snake_case       :   List[Any]							=      []
          snake_case       :   Optional[int]							=      block_out_channels[0]
          for i, down_block_type in enumerate(self.down_block_types ):
                 snake_case       :   List[Any]							=      output_channel
                 snake_case       :   Dict							=      block_out_channels[i]
                 snake_case       :   Optional[Any]							=      i == len(snake_case__ ) - 1

                 if down_block_type == "CrossAttnDownBlock2D":
                        snake_case       :   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]    ,						use_memory_efficient_attention=self.use_memory_efficient_attention    ,						dtype=self.dtype    ,						)
                 else:
                        snake_case       :   Union[str, Any]							=      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__ )
          snake_case       :   Dict							=      down_blocks

          # mid
          snake_case       :   Optional[int]							=      FlaxUNetMidBlockaDCrossAttn(
              in_channels=block_out_channels[-1]    ,						dropout=self.dropout    ,						num_attention_heads=num_attention_heads[-1]    ,						use_linear_projection=self.use_linear_projection    ,						use_memory_efficient_attention=self.use_memory_efficient_attention    ,						dtype=self.dtype    ,						)

          # up
          snake_case       :   Optional[Any]							=      []
          snake_case       :   Optional[int]							=      list(reversed(snake_case__ ) )
          snake_case       :   Dict							=      list(reversed(snake_case__ ) )
          snake_case       :   Tuple							=      list(reversed(snake_case__ ) )
          snake_case       :   Optional[Any]							=      reversed_block_out_channels[0]
          for i, up_block_type in enumerate(self.up_block_types ):
                 snake_case       :   Optional[int]							=      output_channel
                 snake_case       :   List[Any]							=      reversed_block_out_channels[i]
                 snake_case       :   Union[str, Any]							=      reversed_block_out_channels[min(i + 1    ,						len(snake_case__ ) - 1 )]

                 snake_case       :   int							=      i == len(snake_case__ ) - 1

                 if up_block_type == "CrossAttnUpBlock2D":
                        snake_case       :   Any							=      FlaxCrossAttnUpBlockaD(
                            in_channels=snake_case__    ,						out_channels=snake_case__    ,						prev_output_channel=snake_case__    ,						num_layers=self.layers_per_block + 1    ,						num_attention_heads=reversed_num_attention_heads[i]    ,						add_upsample=not is_final_block    ,						dropout=self.dropout    ,						use_linear_projection=self.use_linear_projection    ,						only_cross_attention=only_cross_attention[i]    ,						use_memory_efficient_attention=self.use_memory_efficient_attention    ,						dtype=self.dtype    ,						)
                 else:
                        snake_case       :   Optional[int]							=      FlaxUpBlockaD(
                            in_channels=snake_case__    ,						out_channels=snake_case__    ,						prev_output_channel=snake_case__    ,						num_layers=self.layers_per_block + 1    ,						add_upsample=not is_final_block    ,						dropout=self.dropout    ,						dtype=self.dtype    ,						)

                 up_blocks.append(snake_case__ )
                 snake_case       :   Optional[int]							=      output_channel
          snake_case       :   Tuple							=      up_blocks

          # out
          snake_case       :   Optional[int]							=      nn.GroupNorm(num_groups=32    ,						epsilon=1e-5 )
          snake_case       :   List[str]							=      nn.Conv(
              self.out_channels    ,						kernel_size=(3, 3)    ,						strides=(1, 1)    ,						padding=((1, 1), (1, 1))    ,						dtype=self.dtype    ,						)



   def __call__(self		:					Dict    ,						snake_case__		:					Dict    ,						snake_case__		:					Dict    ,						snake_case__		:					Optional[int]    ,						snake_case__		:					Tuple=None    ,						snake_case__		:					Union[str, Any]=None    ,						snake_case__		:					bool = True    ,						snake_case__		:					bool = False    ,						)		->	Union[FlaxUNetaDConditionOutput, Tuple]:


          '''simple docstring'''


          if not isinstance(snake_case__    ,						jnp.ndarray ):
                 snake_case       :   List[Any]							=      jnp.array([timesteps]    ,						dtype=jnp.intaa )
          elif isinstance(snake_case__    ,						jnp.ndarray ) and len(timesteps.shape ) == 0:
                 snake_case       :   Any							=      timesteps.astype(dtype=jnp.floataa )
                 snake_case       :   int							=      jnp.expand_dims(snake_case__    ,						0 )

          snake_case       :   str							=      self.time_proj(snake_case__ )
          snake_case       :   str							=      self.time_embedding(snake_case__ )

          # 2. pre-process
          snake_case       :   int							=      jnp.transpose(snake_case__    ,						(0, 2, 3, 1) )
          snake_case       :   List[Any]							=      self.conv_in(snake_case__ )

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

          if down_block_additional_residuals is not None:
                 snake_case       :   Tuple							=      ()

                 for down_block_res_sample, down_block_additional_residual in zip(
                     snake_case__    ,						snake_case__ ):
                        down_block_res_sample += down_block_additional_residual
                        new_down_block_res_samples += (down_block_res_sample,)

                 snake_case       :   Optional[int]							=      new_down_block_res_samples

          # 4. mid
          snake_case       :   Optional[int]							=      self.mid_block(snake_case__    ,						snake_case__    ,						snake_case__    ,						deterministic=not train )

          if mid_block_additional_residual is not None:
                 sample += mid_block_additional_residual

          # 5. up
          for up_block in self.up_blocks:
                 snake_case       :   int							=      down_block_res_samples[-(self.layers_per_block + 1) :]
                 snake_case       :   Optional[Any]							=      down_block_res_samples[: -(self.layers_per_block + 1)]
                 if isinstance(snake_case__    ,						snake_case__ ):
                        snake_case       :   Optional[Any]							=      up_block(
                            snake_case__    ,						temb=snake_case__    ,						encoder_hidden_states=snake_case__    ,						res_hidden_states_tuple=snake_case__    ,						deterministic=not train    ,						)
                 else:
                        snake_case       :   Dict							=      up_block(snake_case__    ,						temb=snake_case__    ,						res_hidden_states_tuple=snake_case__    ,						deterministic=not train )

        # 6. post-process
          snake_case       :   List[str]							=      self.conv_norm_out(snake_case__ )
          snake_case       :   Any							=      nn.silu(snake_case__ )
          snake_case       :   Optional[int]							=      self.conv_out(snake_case__ )
          snake_case       :   Union[str, Any]							=      jnp.transpose(snake_case__    ,						(0, 3, 1, 2) )

          if not return_dict:
                 return (sample,)

          return FlaxUNetaDConditionOutput(sample=snake_case__ )





