



"""simple docstring"""

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    __lowercase     (					_UpperCAmelCase):







						"""simple docstring"""







						_A		:							jnp.ndarray







@flax_register_to_config
class    __lowercase     (					nn.Module					,    _UpperCAmelCase					,    _UpperCAmelCase):







						"""simple docstring"""







						_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]							       =		(320, 640, 1280, 1280)
						_A		:							int							       =		2
						_A		:							Union[int, Tuple[int]]							       =		8
						_A		:							Optional[Union[int, Tuple[int]]]							       =		None
						_A		:							int							       =		1280
						_A		:							float							       =		0.0
						_A		:							bool							       =		False
						_A		:							jnp.dtype							       =		jnp.floataa
						_A		:							bool							       =		True
						_A		:							int							       =		0
						_A		:							bool							       =		False







						def 						__UpperCamelCase							(self   ,		lowercase__       ):
											# init input tensors
											snake_case_					:		str 					=					(1, self.in_channels, self.sample_size, self.sample_size)
											snake_case_					:		Any 					=					jnp.zeros(lowercase__   ,		dtype=jnp.floataa       )
											snake_case_					:		Optional[Any] 					=					jnp.ones((1,)   ,		dtype=jnp.intaa       )
											snake_case_					:		Optional[int] 					=					jnp.zeros((1, 1, self.cross_attention_dim)   ,		dtype=jnp.floataa       )

											snake_case_					,			snake_case_					:		str 					=					jax.random.split(lowercase__       )
											snake_case_					:		Optional[int] 					=					{"""params""": params_rng, """dropout""": dropout_rng}

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







						def 						__UpperCamelCase							(self       ):
											snake_case_					:		Any 					=					self.block_out_channels
											snake_case_					:		Union[str, 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_					:		Dict 					=					self.num_attention_heads or self.attention_head_dim

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

											# time
											snake_case_					:		Any 					=					FlaxTimesteps(
											    block_out_channels[0]   ,		flip_sin_to_cos=self.flip_sin_to_cos   ,		freq_shift=self.config.freq_shift       )
											snake_case_					:		str 					=					FlaxTimestepEmbedding(lowercase__   ,		dtype=self.dtype       )

											snake_case_					:		Optional[Any] 					=					self.only_cross_attention
											if isinstance(lowercase__   ,		lowercase__       ):
																snake_case_					:		int 					=					(only_cross_attention,) * len(self.down_block_types       )

											if isinstance(lowercase__   ,		lowercase__       ):
																snake_case_					:		List[str] 					=					(num_attention_heads,) * len(self.down_block_types       )

											# down
											snake_case_					:		str 					=					[]
											snake_case_					:		Tuple 					=					block_out_channels[0]
											for i, down_block_type in enumerate(self.down_block_types       ):
																snake_case_					:		Tuple 					=					output_channel
																snake_case_					:		Any 					=					block_out_channels[i]
																snake_case_					:		int 					=					i == len(lowercase__       ) - 1

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

																down_blocks.append(lowercase__       )
											snake_case_					:		Optional[int] 					=					down_blocks

											# mid
											snake_case_					:		str 					=					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_					:		int 					=					[]
											snake_case_					:		Any 					=					list(reversed(lowercase__       )       )
											snake_case_					:		Tuple 					=					list(reversed(lowercase__       )       )
											snake_case_					:		str 					=					list(reversed(lowercase__       )       )
											snake_case_					:		Optional[Any] 					=					reversed_block_out_channels[0]
											for i, up_block_type in enumerate(self.up_block_types       ):
																snake_case_					:		Optional[Any] 					=					output_channel
																snake_case_					:		Union[str, Any] 					=					reversed_block_out_channels[i]
																snake_case_					:		str 					=					reversed_block_out_channels[min(i + 1   ,		len(lowercase__       ) - 1       )]

																snake_case_					:		Tuple 					=					i == len(lowercase__       ) - 1

																if up_block_type == "CrossAttnUpBlock2D":
																					snake_case_					:		Dict 					=					FlaxCrossAttnUpBlockaD(
																					    in_channels=lowercase__   ,		out_channels=lowercase__   ,		prev_output_channel=lowercase__   ,		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_					:		List[str] 					=					FlaxUpBlockaD(
																					    in_channels=lowercase__   ,		out_channels=lowercase__   ,		prev_output_channel=lowercase__   ,		num_layers=self.layers_per_block + 1   ,		add_upsample=not is_final_block   ,		dropout=self.dropout   ,		dtype=self.dtype   ,		)

																up_blocks.append(lowercase__       )
																snake_case_					:		Optional[Any] 					=					output_channel
											snake_case_					:		Optional[int] 					=					up_blocks

											# out
											snake_case_					:		Dict 					=					nn.GroupNorm(num_groups=32   ,		epsilon=1e-5       )
											snake_case_					:		Dict 					=					nn.Conv(
											    self.out_channels   ,		kernel_size=(3, 3)   ,		strides=(1, 1)   ,		padding=((1, 1), (1, 1))   ,		dtype=self.dtype   ,		)







						def __call__(self   ,		lowercase__   ,		lowercase__   ,		lowercase__   ,		lowercase__=None   ,		lowercase__=None   ,		lowercase__ = True   ,		lowercase__ = False   ,		):
											# 1. time
											if not isinstance(lowercase__   ,		jnp.ndarray       ):
																snake_case_					:		Dict 					=					jnp.array([timesteps]   ,		dtype=jnp.intaa       )
											elif isinstance(lowercase__   ,		jnp.ndarray       ) and len(timesteps.shape       ) == 0:
																snake_case_					:		Any 					=					timesteps.astype(dtype=jnp.floataa       )
																snake_case_					:		List[Any] 					=					jnp.expand_dims(lowercase__   ,		0       )

											snake_case_					:		List[str] 					=					self.time_proj(lowercase__       )
											snake_case_					:		Any 					=					self.time_embedding(lowercase__       )

											# 2. pre-process
											snake_case_					:		int 					=					jnp.transpose(lowercase__   ,		(0, 2, 3, 1)       )
											snake_case_					:		Any 					=					self.conv_in(lowercase__       )

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

											if down_block_additional_residuals is not None:
																snake_case_					:		Optional[Any] 					=					()

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

																snake_case_					:		str 					=					new_down_block_res_samples

											# 4. mid
											snake_case_					:		Tuple 					=					self.mid_block(lowercase__   ,		lowercase__   ,		lowercase__   ,		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_					:		Optional[Any] 					=					down_block_res_samples[-(self.layers_per_block + 1) :]
																snake_case_					:		List[Any] 					=					down_block_res_samples[: -(self.layers_per_block + 1)]
																if isinstance(lowercase__   ,		lowercase__       ):
																					snake_case_					:		Union[str, Any] 					=					up_block(
																					    lowercase__   ,		temb=lowercase__   ,		encoder_hidden_states=lowercase__   ,		res_hidden_states_tuple=lowercase__   ,		deterministic=not train   ,		)
																else:
																					snake_case_					:		List[Any] 					=					up_block(lowercase__   ,		temb=lowercase__   ,		res_hidden_states_tuple=lowercase__   ,		deterministic=not train       )

        # 6. post-process
											snake_case_					:		List[Any] 					=					self.conv_norm_out(lowercase__       )
											snake_case_					:		Tuple 					=					nn.silu(lowercase__       )
											snake_case_					:		List[Any] 					=					self.conv_out(lowercase__       )
											snake_case_					:		Optional[Any] 					=					jnp.transpose(lowercase__   ,		(0, 3, 1, 2)       )

											if not return_dict:
																return (sample,)

											return FlaxUNetaDConditionOutput(sample=lowercase__       )









"""simple docstring"""

import logging
import os
from typing import List, TextIO, Union

from conllu import parse_incr
from utils_ner import InputExample, Split, TokenClassificationTask


a_			   =					logging.getLogger(__name__)







class    __lowercase     (					_UpperCAmelCase):







						"""simple docstring"""







						def __init__(self   ,		lowercase__=-1       ):
											# in NER datasets, the last column is usually reserved for NER label
											snake_case_					:		Union[str, Any] 					=					label_idx







						def 						__UpperCamelCase							(self   ,		lowercase__   ,		lowercase__       ):
											if isinstance(lowercase__   ,		lowercase__       ):
																snake_case_					:		List[str] 					=					mode.value
											snake_case_					:		List[Any] 					=					os.path.join(lowercase__   ,		f'{mode}.txt'       )
											snake_case_					:		Tuple 					=					1
											snake_case_					:		Any 					=					[]
											with open(lowercase__   ,		encoding="""utf-8"""       ) as f:
																snake_case_					:		str 					=					[]
																snake_case_					:		List[Any] 					=					[]
																for line in f:
																					if line.startswith("""-DOCSTART-"""       ) or line == "" or line == "\n":
																										if words:
																															examples.append(InputExample(guid=f'{mode}-{guid_index}'   ,		words=lowercase__   ,		labels=lowercase__       )       )
																															guid_index += 1
																															snake_case_					:		Optional[Any] 					=					[]
																															snake_case_					:		int 					=					[]
																					else:
																										snake_case_					:		Optional[Any] 					=					line.split(""" """       )
																										words.append(splits[0]       )
																										if len(lowercase__       ) > 1:
																															labels.append(splits[self.label_idx].replace("""\n"""   ,		""""""       )       )
																										else:
																															# Examples could have no label for mode = "test"
																															labels.append("""O"""       )
																if words:
																					examples.append(InputExample(guid=f'{mode}-{guid_index}'   ,		words=lowercase__   ,		labels=lowercase__       )       )
											return examples







						def 						__UpperCamelCase							(self   ,		lowercase__   ,		lowercase__   ,		lowercase__       ):
											snake_case_					:		str 					=					0
											for line in test_input_reader:
																if line.startswith("""-DOCSTART-"""       ) or line == "" or line == "\n":
																					writer.write(lowercase__       )
																					if not preds_list[example_id]:
																										example_id += 1
																elif preds_list[example_id]:
																					snake_case_					:		Optional[int] 					=					line.split()[0] + """ """ + preds_list[example_id].pop(0       ) + """\n"""
																					writer.write(lowercase__       )
																else:
																					logger.warning("""Maximum sequence length exceeded: No prediction for '%s'."""   ,		line.split()[0]       )







						def 						__UpperCamelCase							(self   ,		lowercase__       ):
											if path:
																with open(lowercase__   ,		"""r"""       ) as f:
																					snake_case_					:		Dict 					=					f.read().splitlines()
																if "O" not in labels:
																					snake_case_					:		List[Any] 					=					["""O"""] + labels
																return labels
											else:
																return ["O", "B-MISC", "I-MISC", "B-PER", "I-PER", "B-ORG", "I-ORG", "B-LOC", "I-LOC"]







class    __lowercase     (					_UpperCAmelCase):







						"""simple docstring"""







						def __init__(self       ):
											# in CONLL2003 dataset chunk column is second-to-last
											super().__init__(label_idx=-2       )







						def 						__UpperCamelCase							(self   ,		lowercase__       ):
											if path:
																with open(lowercase__   ,		"""r"""       ) as f:
																					snake_case_					:		Any 					=					f.read().splitlines()
																if "O" not in labels:
																					snake_case_					:		Tuple 					=					["""O"""] + labels
																return labels
											else:
																return [
																    "O",
																    "B-ADVP",
																    "B-INTJ",
																    "B-LST",
																    "B-PRT",
																    "B-NP",
																    "B-SBAR",
																    "B-VP",
																    "B-ADJP",
																    "B-CONJP",
																    "B-PP",
																    "I-ADVP",
																    "I-INTJ",
																    "I-LST",
																    "I-PRT",
																    "I-NP",
																    "I-SBAR",
																    "I-VP",
																    "I-ADJP",
																    "I-CONJP",
																    "I-PP",
																]







class    __lowercase     (					_UpperCAmelCase):







						"""simple docstring"""







						def 						__UpperCamelCase							(self   ,		lowercase__   ,		lowercase__       ):
											if isinstance(lowercase__   ,		lowercase__       ):
																snake_case_					:		List[Any] 					=					mode.value
											snake_case_					:		Optional[int] 					=					os.path.join(lowercase__   ,		f'{mode}.txt'       )
											snake_case_					:		Tuple 					=					1
											snake_case_					:		str 					=					[]

											with open(lowercase__   ,		encoding="""utf-8"""       ) as f:
																for sentence in parse_incr(lowercase__       ):
																					snake_case_					:		Tuple 					=					[]
																					snake_case_					:		Any 					=					[]
																					for token in sentence:
																										words.append(token["""form"""]       )
																										labels.append(token["""upos"""]       )
																					assert len(lowercase__       ) == len(lowercase__       )
																					if words:
																										examples.append(InputExample(guid=f'{mode}-{guid_index}'   ,		words=lowercase__   ,		labels=lowercase__       )       )
																										guid_index += 1
											return examples







						def 						__UpperCamelCase							(self   ,		lowercase__   ,		lowercase__   ,		lowercase__       ):
											snake_case_					:		Dict 					=					0
											for sentence in parse_incr(lowercase__       ):
																snake_case_					:		int 					=					preds_list[example_id]
																snake_case_					:		Dict 					=					""""""
																for token in sentence:
																					out += f'{token["form"]} ({token["upos"]}|{s_p.pop(0       )}) '
																out += "\n"
																writer.write(lowercase__       )
																example_id += 1







						def 						__UpperCamelCase							(self   ,		lowercase__       ):
											if path:
																with open(lowercase__   ,		"""r"""       ) as f:
																					return f.read().splitlines()
											else:
																return [
																    "ADJ",
																    "ADP",
																    "ADV",
																    "AUX",
																    "CCONJ",
																    "DET",
																    "INTJ",
																    "NOUN",
																    "NUM",
																    "PART",
																    "PRON",
																    "PROPN",
																    "PUNCT",
																    "SCONJ",
																    "SYM",
																    "VERB",
																    "X",
																]




