


from dataclasses import dataclass
from typing import Optional

import numpy as np
import torch
import torch.nn as nn

from ..utils import BaseOutput, is_torch_version, randn_tensor
from .attention_processor import SpatialNorm
from .unet_ad_blocks import UNetMidBlockaD, get_down_block, get_up_block







@dataclass
class     __lowercase    (				__UpperCamelCase   ):



		"""simple docstring"""

		UpperCamelCase			: List[Any]           =						4_2







class     __lowercase    (				nn.Module   ):



		"""simple docstring"""

		def __init__(      self						,	A=3						,	A=3						,	A=("DownEncoderBlock2D",)						,	A=(64,)						,	A=2						,	A=32						,	A="silu"						,	A=True						,	)   ->      Tuple:







					'''simple docstring'''







					super().__init__()
					lowerCamelCase  						=   layers_per_block

					lowerCamelCase  						=   torch.nn.Convad(
					    A						,	block_out_channels[0]						,	kernel_size=3						,	stride=1						,	padding=1						,	)

					lowerCamelCase  						=   None
					lowerCamelCase  						=   nn.ModuleList([]     )

					# down
					lowerCamelCase  						=   block_out_channels[0]
					for i, down_block_type in enumerate(A     ):
								lowerCamelCase  						=   output_channel
								lowerCamelCase  						=   block_out_channels[i]
								lowerCamelCase  						=   i == len(A     ) - 1

								lowerCamelCase  						=   get_down_block(
								    A						,	num_layers=self.layers_per_block						,	in_channels=A						,	out_channels=A						,	add_downsample=not is_final_block						,	resnet_eps=1e-6						,	downsample_padding=0						,	resnet_act_fn=A						,	resnet_groups=A						,	attention_head_dim=A						,	temb_channels=A						,	)
								self.down_blocks.append(A     )

					# mid
					lowerCamelCase  						=   UNetMidBlockaD(
					    in_channels=block_out_channels[-1]						,	resnet_eps=1e-6						,	resnet_act_fn=A						,	output_scale_factor=1						,	resnet_time_scale_shift="""default"""						,	attention_head_dim=block_out_channels[-1]						,	resnet_groups=A						,	temb_channels=A						,	)

					# out
					lowerCamelCase  						=   nn.GroupNorm(num_channels=block_out_channels[-1]						,	num_groups=A						,	eps=1e-6     )
					lowerCamelCase  						=   nn.SiLU()

					lowerCamelCase  						=   2 * out_channels if double_z else out_channels
					lowerCamelCase  						=   nn.Convad(block_out_channels[-1]						,	A						,	3						,	padding=1     )

					lowerCamelCase  						=   False







		def  __A					(      self						,	A     )   ->      Union[str, Any]:







					'''simple docstring'''







					lowerCamelCase  						=   x
					lowerCamelCase  						=   self.conv_in(A     )

					if self.training and self.gradient_checkpointing:

								def create_custom_forward(A     ):
											def custom_forward(*A     ):
														return module(*A     )

											return custom_forward

								# down
								if is_torch_version(""">="""						,	"""1.11.0"""     ):
											for down_block in self.down_blocks:
														lowerCamelCase  						=   torch.utils.checkpoint.checkpoint(
														    create_custom_forward(A     )						,	A						,	use_reentrant=A     )
											# middle
											lowerCamelCase  						=   torch.utils.checkpoint.checkpoint(
											    create_custom_forward(self.mid_block     )						,	A						,	use_reentrant=A     )
								else:
											for down_block in self.down_blocks:
														lowerCamelCase  						=   torch.utils.checkpoint.checkpoint(create_custom_forward(A     )						,	A     )
											# middle
											lowerCamelCase  						=   torch.utils.checkpoint.checkpoint(create_custom_forward(self.mid_block     )						,	A     )

					else:
								# down
								for down_block in self.down_blocks:
											lowerCamelCase  						=   down_block(A     )

								# middle
								lowerCamelCase  						=   self.mid_block(A     )

					# post-process
					lowerCamelCase  						=   self.conv_norm_out(A     )
					lowerCamelCase  						=   self.conv_act(A     )
					lowerCamelCase  						=   self.conv_out(A     )

					return sample







class     __lowercase    (				nn.Module   ):



		"""simple docstring"""

		def __init__(      self						,	A=3						,	A=3						,	A=("UpDecoderBlock2D",)						,	A=(64,)						,	A=2						,	A=32						,	A="silu"						,	A="group"						,	)   ->      Any:







					'''simple docstring'''







					super().__init__()
					lowerCamelCase  						=   layers_per_block

					lowerCamelCase  						=   nn.Convad(
					    A						,	block_out_channels[-1]						,	kernel_size=3						,	stride=1						,	padding=1						,	)

					lowerCamelCase  						=   None
					lowerCamelCase  						=   nn.ModuleList([]     )

					lowerCamelCase  						=   in_channels if norm_type == 'spatial' else None

					# mid
					lowerCamelCase  						=   UNetMidBlockaD(
					    in_channels=block_out_channels[-1]						,	resnet_eps=1e-6						,	resnet_act_fn=A						,	output_scale_factor=1						,	resnet_time_scale_shift="""default""" if norm_type == """group""" else norm_type						,	attention_head_dim=block_out_channels[-1]						,	resnet_groups=A						,	temb_channels=A						,	)

					# up
					lowerCamelCase  						=   list(reversed(A     )     )
					lowerCamelCase  						=   reversed_block_out_channels[0]
					for i, up_block_type in enumerate(A     ):
								lowerCamelCase  						=   output_channel
								lowerCamelCase  						=   reversed_block_out_channels[i]

								lowerCamelCase  						=   i == len(A     ) - 1

								lowerCamelCase  						=   get_up_block(
								    A						,	num_layers=self.layers_per_block + 1						,	in_channels=A						,	out_channels=A						,	prev_output_channel=A						,	add_upsample=not is_final_block						,	resnet_eps=1e-6						,	resnet_act_fn=A						,	resnet_groups=A						,	attention_head_dim=A						,	temb_channels=A						,	resnet_time_scale_shift=A						,	)
								self.up_blocks.append(A     )
								lowerCamelCase  						=   output_channel

					# out
					if norm_type == "spatial":
								lowerCamelCase  						=   SpatialNorm(block_out_channels[0]						,	A     )
					else:
								lowerCamelCase  						=   nn.GroupNorm(num_channels=block_out_channels[0]						,	num_groups=A						,	eps=1e-6     )
					lowerCamelCase  						=   nn.SiLU()
					lowerCamelCase  						=   nn.Convad(block_out_channels[0]						,	A						,	3						,	padding=1     )

					lowerCamelCase  						=   False







		def  __A					(      self						,	A						,	A=None     )   ->      List[str]:







					'''simple docstring'''







					lowerCamelCase  						=   z
					lowerCamelCase  						=   self.conv_in(A     )

					lowerCamelCase  						=   next(iter(self.up_blocks.parameters()     )     ).dtype
					if self.training and self.gradient_checkpointing:

								def create_custom_forward(A     ):
											def custom_forward(*A     ):
														return module(*A     )

											return custom_forward

								if is_torch_version(""">="""						,	"""1.11.0"""     ):
											# middle
											lowerCamelCase  						=   torch.utils.checkpoint.checkpoint(
											    create_custom_forward(self.mid_block     )						,	A						,	A						,	use_reentrant=A     )
											lowerCamelCase  						=   sample.to(A     )

											# up
											for up_block in self.up_blocks:
														lowerCamelCase  						=   torch.utils.checkpoint.checkpoint(
														    create_custom_forward(A     )						,	A						,	A						,	use_reentrant=A     )
								else:
											# middle
											lowerCamelCase  						=   torch.utils.checkpoint.checkpoint(
											    create_custom_forward(self.mid_block     )						,	A						,	A     )
											lowerCamelCase  						=   sample.to(A     )

											# up
											for up_block in self.up_blocks:
														lowerCamelCase  						=   torch.utils.checkpoint.checkpoint(create_custom_forward(A     )						,	A						,	A     )
					else:
								# middle
								lowerCamelCase  						=   self.mid_block(A						,	A     )
								lowerCamelCase  						=   sample.to(A     )

								# up
								for up_block in self.up_blocks:
											lowerCamelCase  						=   up_block(A						,	A     )

        # post-process
					if latent_embeds is None:
								lowerCamelCase  						=   self.conv_norm_out(A     )
					else:
								lowerCamelCase  						=   self.conv_norm_out(A						,	A     )
					lowerCamelCase  						=   self.conv_act(A     )
					lowerCamelCase  						=   self.conv_out(A     )

					return sample







class     __lowercase    (				nn.Module   ):



		"""simple docstring"""

		def __init__(      self						,	A						,	A						,	A						,	A=None						,	A="random"						,	A=False						,	A=True     )   ->      Optional[int]:







					'''simple docstring'''







					super().__init__()
					lowerCamelCase  						=   n_e
					lowerCamelCase  						=   vq_embed_dim
					lowerCamelCase  						=   beta
					lowerCamelCase  						=   legacy

					lowerCamelCase  						=   nn.Embedding(self.n_e						,	self.vq_embed_dim     )
					self.embedding.weight.data.uniform_(-1.0 / self.n_e						,	1.0 / self.n_e     )

					lowerCamelCase  						=   remap
					if self.remap is not None:
								self.register_buffer("""used"""						,	torch.tensor(np.load(self.remap     )     )     )
								lowerCamelCase  						=   self.used.shape[0]
								lowerCamelCase  						=   unknown_index  # "random" or "extra" or integer
								if self.unknown_index == "extra":
											lowerCamelCase  						=   self.re_embed
											lowerCamelCase  						=   self.re_embed + 1
								print(
								    F'Remapping {self.n_e} indices to {self.re_embed} indices. '
								    F'Using {self.unknown_index} for unknown indices.'     )
					else:
								lowerCamelCase  						=   n_e

					lowerCamelCase  						=   sane_index_shape







		def  __A					(      self						,	A     )   ->      Dict:







					'''simple docstring'''







					lowerCamelCase  						=   inds.shape
					assert len(A     ) > 1
					lowerCamelCase  						=   inds.reshape(ishape[0]						,	-1     )
					lowerCamelCase  						=   self.used.to(A     )
					lowerCamelCase  						=   (inds[:, :, None] == used[None, None, ...]).long()
					lowerCamelCase  						=   match.argmax(-1     )
					lowerCamelCase  						=   match.sum(2     ) < 1
					if self.unknown_index == "random":
								lowerCamelCase  						=   torch.randint(0						,	self.re_embed						,	size=new[unknown].shape     ).to(device=new.device     )
					else:
								lowerCamelCase  						=   self.unknown_index
					return new.reshape(A     )







		def  __A					(      self						,	A     )   ->      Optional[int]:







					'''simple docstring'''







					lowerCamelCase  						=   inds.shape
					assert len(A     ) > 1
					lowerCamelCase  						=   inds.reshape(ishape[0]						,	-1     )
					lowerCamelCase  						=   self.used.to(A     )
					if self.re_embed > self.used.shape[0]:  # extra token
								lowerCamelCase  						=   0  # simply set to zero
					lowerCamelCase  						=   torch.gather(used[None, :][inds.shape[0] * [0], :]						,	1						,	A     )
					return back.reshape(A     )







		def  __A					(      self						,	A     )   ->      Optional[int]:







					'''simple docstring'''







					lowerCamelCase  						=   z.permute(0						,	2						,	3						,	1     ).contiguous()
					lowerCamelCase  						=   z.view(-1						,	self.vq_embed_dim     )

					# distances from z to embeddings e_j (z - e)^2 = z^2 + e^2 - 2 e * z
					lowerCamelCase  						=   torch.argmin(torch.cdist(A						,	self.embedding.weight     )						,	dim=1     )

					lowerCamelCase  						=   self.embedding(A     ).view(z.shape     )
					lowerCamelCase  						=   None
					lowerCamelCase  						=   None

					# compute loss for embedding
					if not self.legacy:
								lowerCamelCase  						=   self.beta * torch.mean((z_q.detach() - z) ** 2     ) + torch.mean((z_q - z.detach()) ** 2     )
					else:
								lowerCamelCase  						=   torch.mean((z_q.detach() - z) ** 2     ) + self.beta * torch.mean((z_q - z.detach()) ** 2     )

					# preserve gradients
					lowerCamelCase  						=   z + (z_q - z).detach()

					# reshape back to match original input shape
					lowerCamelCase  						=   z_q.permute(0						,	3						,	1						,	2     ).contiguous()

					if self.remap is not None:
								lowerCamelCase  						=   min_encoding_indices.reshape(z.shape[0]						,	-1     )  # add batch axis
								lowerCamelCase  						=   self.remap_to_used(A     )
								lowerCamelCase  						=   min_encoding_indices.reshape(-1						,	1     )  # flatten

					if self.sane_index_shape:
								lowerCamelCase  						=   min_encoding_indices.reshape(z_q.shape[0]						,	z_q.shape[2]						,	z_q.shape[3]     )

					return z_q, loss, (perplexity, min_encodings, min_encoding_indices)







		def  __A					(      self						,	A						,	A     )   ->      List[Any]:







					'''simple docstring'''







					if self.remap is not None:
								lowerCamelCase  						=   indices.reshape(shape[0]						,	-1     )  # add batch axis
								lowerCamelCase  						=   self.unmap_to_all(A     )
								lowerCamelCase  						=   indices.reshape(-1     )  # flatten again

					# get quantized latent vectors
					lowerCamelCase  						=   self.embedding(A     )

					if shape is not None:
								lowerCamelCase  						=   z_q.view(A     )
								# reshape back to match original input shape
								lowerCamelCase  						=   z_q.permute(0						,	3						,	1						,	2     ).contiguous()

					return z_q







class     __lowercase    (				__UpperCamelCase   ):



		"""simple docstring"""

		def __init__(      self						,	A						,	A=False     )   ->      List[str]:







					'''simple docstring'''







					lowerCamelCase  						=   parameters
					lowerCamelCase  						=   torch.chunk(A						,	2						,	dim=1     )
					lowerCamelCase  						=   torch.clamp(self.logvar						,	-30.0						,	20.0     )
					lowerCamelCase  						=   deterministic
					lowerCamelCase  						=   torch.exp(0.5 * self.logvar     )
					lowerCamelCase  						=   torch.exp(self.logvar     )
					if self.deterministic:
								lowerCamelCase  						=   torch.zeros_like(
								    self.mean						,	device=self.parameters.device						,	dtype=self.parameters.dtype     )







		def  __A					(      self						,	A = None     )   ->      torch.FloatTensor:







					'''simple docstring'''







					lowerCamelCase  						=   randn_tensor(
					    self.mean.shape						,	generator=A						,	device=self.parameters.device						,	dtype=self.parameters.dtype     )
					lowerCamelCase  						=   self.mean + self.std * sample
					return x







		def  __A					(      self						,	A=None     )   ->      Optional[Any]:







					'''simple docstring'''







					if self.deterministic:
								return torch.Tensor([0.0]     )
					else:
								if other is None:
											return 0.5 * torch.sum(torch.pow(self.mean						,	2     ) + self.var - 1.0 - self.logvar						,	dim=[1, 2, 3]     )
								else:
											return 0.5 * torch.sum(
											    torch.pow(self.mean - other.mean						,	2     ) / other.var
											    + self.var / other.var
											    - 1.0
											    - self.logvar
											    + other.logvar						,	dim=[1, 2, 3]						,	)







		def  __A					(      self						,	A						,	A=[1, 2, 3]     )   ->      List[str]:







					'''simple docstring'''







					if self.deterministic:
								return torch.Tensor([0.0]     )
					lowerCamelCase  						=   np.log(2.0 * np.pi     )
					return 0.5 * torch.sum(logtwopi + self.logvar + torch.pow(sample - self.mean						,	2     ) / self.var						,	dim=A     )







		def  __A					(      self     )   ->      List[str]:







					'''simple docstring'''







					return self.mean








import argparse
import json
import os
import time
import zipfile

from get_ci_error_statistics import download_artifact, get_artifacts_links

from transformers import logging


_UpperCAmelCase				:       Optional[int]	   =				logging.get_logger(__name__)


def        SCREAMING_SNAKE_CASE      (       _UpperCAmelCase						,	_UpperCAmelCase )			->     Dict:
    lowerCamelCase__    :      str					 =		set()
    lowerCamelCase__    :      Any					 =		[]

    def parse_line(_UpperCAmelCase ):
        for line in fp:
            if isinstance(_UpperCAmelCase						,	_UpperCAmelCase ):
                lowerCamelCase__    :      Any					 =		line.decode('UTF-8' )
            if "warnings summary (final)" in line:
                continue
            # This means we are outside the body of a warning
            elif not line.startswith(' ' ):
                # process a single warning and move it to `selected_warnings`.
                if len(_UpperCAmelCase ) > 0:
                    lowerCamelCase__    :      str					 =		'\n'.join(_UpperCAmelCase )
                    # Only keep the warnings specified in `targets`
                    if any(F""": {x}: """ in warning for x in targets ):
                        selected_warnings.add(_UpperCAmelCase )
                    buffer.clear()
                continue
            else:
                lowerCamelCase__    :      List[str]					 =		line.strip()
                buffer.append(_UpperCAmelCase )

    if from_gh:
        for filename in os.listdir(_UpperCAmelCase ):
            lowerCamelCase__    :      Dict					 =		os.path.join(_UpperCAmelCase						,	_UpperCAmelCase )
            if not os.path.isdir(_UpperCAmelCase ):
                # read the file
                if filename != "warnings.txt":
                    continue
                with open(_UpperCAmelCase ) as fp:
                    parse_line(_UpperCAmelCase )
    else:
        try:
            with zipfile.ZipFile(_UpperCAmelCase ) as z:
                for filename in z.namelist():
                    if not os.path.isdir(_UpperCAmelCase ):
                        # read the file
                        if filename != "warnings.txt":
                            continue
                        with z.open(_UpperCAmelCase ) as fp:
                            parse_line(_UpperCAmelCase )
        except Exception:
            logger.warning(
                F"""{artifact_path} is either an invalid zip file or something else wrong. This file is skipped.""" )

    return selected_warnings


def        SCREAMING_SNAKE_CASE      (       _UpperCAmelCase						,	_UpperCAmelCase )			->     Union[str, Any]:
    lowerCamelCase__    :      Tuple					 =		set()

    lowerCamelCase__    :      Optional[int]					 =		[os.path.join(_UpperCAmelCase						,	_UpperCAmelCase ) for p in os.listdir(_UpperCAmelCase ) if (p.endswith('.zip' ) or from_gh)]
    for p in paths:
        selected_warnings.update(extract_warnings_from_single_artifact(_UpperCAmelCase						,	_UpperCAmelCase ) )

    return selected_warnings


if __name__ == "__main__":


    def        SCREAMING_SNAKE_CASE      (       _UpperCAmelCase )			->     Tuple:
        return values.split(',' )

    _UpperCAmelCase				:       List[str]	   =				argparse.ArgumentParser()
    # Required parameters
    parser.add_argument("""--workflow_run_id""", type=str, required=True, help="""A GitHub Actions workflow run id.""")
    parser.add_argument(
        """--output_dir""",
        type=str,
        required=True,
        help="""Where to store the downloaded artifacts and other result files.""",
    )
    parser.add_argument("""--token""", default=None, type=str, help="""A token that has actions:read permission.""")
    # optional parameters
    parser.add_argument(
        """--targets""",
        default="""DeprecationWarning,UserWarning,FutureWarning""",
        type=list_str,
        help="""Comma-separated list of target warning(s) which we want to extract.""",
    )
    parser.add_argument(
        """--from_gh""",
        action="""store_true""",
        help="""If running from a GitHub action workflow and collecting warnings from its artifacts.""",
    )

    _UpperCAmelCase				:       Union[str, Any]	   =				parser.parse_args()

    _UpperCAmelCase				:       Dict	   =				args.from_gh
    if from_gh:
        # The artifacts have to be downloaded using `actions/download-artifact@v3`
        pass
    else:
        os.makedirs(args.output_dir, exist_ok=True)

        # get download links
        _UpperCAmelCase				:       Union[str, Any]	   =				get_artifacts_links(args.workflow_run_id, token=args.token)
        with open(os.path.join(args.output_dir, """artifacts.json"""), """w""", encoding="""UTF-8""") as fp:
            json.dump(artifacts, fp, ensure_ascii=False, indent=4)

        # download artifacts
        for idx, (name, url) in enumerate(artifacts.items()):
            print(name)
            print(url)
            print("""=""" * 80)
            download_artifact(name, url, args.output_dir, args.token)
            # Be gentle to GitHub
            time.sleep(1)

    # extract warnings from artifacts
    _UpperCAmelCase				:       Dict	   =				extract_warnings(args.output_dir, args.targets)
    _UpperCAmelCase				:       Optional[Any]	   =				sorted(selected_warnings)
    with open(os.path.join(args.output_dir, """selected_warnings.json"""), """w""", encoding="""UTF-8""") as fp:
        json.dump(selected_warnings, fp, ensure_ascii=False, indent=4)





