





'''simple docstring'''







from dataclasses import dataclass
from typing import Any, Dict, Optional

import torch
import torch.nn.functional as F
from torch import nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..models.embeddings import ImagePositionalEmbeddings
from ..utils import BaseOutput, deprecate
from .attention import BasicTransformerBlock
from .embeddings import PatchEmbed
from .modeling_utils import ModelMixin





@dataclass
class     _UpperCAmelCase							(						lowerCAmelCase_   ):
		a   :   torch.FloatTensor





class     _UpperCAmelCase							(						lowerCAmelCase_	,					lowerCAmelCase_   ):
		@register_to_config
		def __init__(  self,__SCREAMING_SNAKE_CASE = 16,__SCREAMING_SNAKE_CASE = 88,__SCREAMING_SNAKE_CASE = None,__SCREAMING_SNAKE_CASE = None,__SCREAMING_SNAKE_CASE = 1,__SCREAMING_SNAKE_CASE = 0.0,__SCREAMING_SNAKE_CASE = 32,__SCREAMING_SNAKE_CASE = None,__SCREAMING_SNAKE_CASE = False,__SCREAMING_SNAKE_CASE = None,__SCREAMING_SNAKE_CASE = None,__SCREAMING_SNAKE_CASE = None,__SCREAMING_SNAKE_CASE = "geglu",__SCREAMING_SNAKE_CASE = None,__SCREAMING_SNAKE_CASE = False,__SCREAMING_SNAKE_CASE = False,__SCREAMING_SNAKE_CASE = False,__SCREAMING_SNAKE_CASE = "layer_norm",__SCREAMING_SNAKE_CASE = True,):



					'''simple docstring'''

					super().__init__()
					__lowerCAmelCase											=				use_linear_projection
					__lowerCAmelCase											=				num_attention_heads
					__lowerCAmelCase											=				attention_head_dim
					__lowerCAmelCase											=				num_attention_heads * attention_head_dim

					# 1. Transformer2DModel can process both standard continuous images of shape `(batch_size, num_channels, width, height)` as well as quantized image embeddings of shape `(batch_size, num_image_vectors)`
					# Define whether input is continuous or discrete depending on configuration
					__lowerCAmelCase											=				(in_channels is not None) and (patch_size is None)
					__lowerCAmelCase											=				num_vector_embeds is not None
					__lowerCAmelCase											=				in_channels is not None and patch_size is not None

					if norm_type == "layer_norm" and num_embeds_ada_norm is not None:
								__lowerCAmelCase											=				(
								    f'The configuration file of this model: {self.__class__} is outdated. `norm_type` is either not set or'
								    """ incorrectly set to `'layer_norm'`.Make sure to set `norm_type` to `'ada_norm'` in the config."""
								    """ Please make sure to update the config accordingly as leaving `norm_type` might led to incorrect"""
								    """ results in future versions. If you have downloaded this checkpoint from the Hugging Face Hub, it"""
								    """ would be very nice if you could open a Pull request for the `transformer/config.json` file"""
								)
								deprecate("""norm_type!=num_embeds_ada_norm""","""1.0.0""",__SCREAMING_SNAKE_CASE,standard_warn=__SCREAMING_SNAKE_CASE			)
								__lowerCAmelCase											=				"""ada_norm"""

					if self.is_input_continuous and self.is_input_vectorized:
								raise ValueError(
								    f'Cannot define both `in_channels`: {in_channels} and `num_vector_embeds`: {num_vector_embeds}. Make'
								    """ sure that either `in_channels` or `num_vector_embeds` is None."""			)
					elif self.is_input_vectorized and self.is_input_patches:
								raise ValueError(
								    f'Cannot define both `num_vector_embeds`: {num_vector_embeds} and `patch_size`: {patch_size}. Make'
								    """ sure that either `num_vector_embeds` or `num_patches` is None."""			)
					elif not self.is_input_continuous and not self.is_input_vectorized and not self.is_input_patches:
								raise ValueError(
								    f'Has to define `in_channels`: {in_channels}, `num_vector_embeds`: {num_vector_embeds}, or patch_size:'
								    f' {patch_size}. Make sure that `in_channels`, `num_vector_embeds` or `num_patches` is not None.'			)

					# 2. Define input layers
					if self.is_input_continuous:
								__lowerCAmelCase											=				in_channels

								__lowerCAmelCase											=				torch.nn.GroupNorm(num_groups=__SCREAMING_SNAKE_CASE,num_channels=__SCREAMING_SNAKE_CASE,eps=1e-6,affine=__SCREAMING_SNAKE_CASE			)
								if use_linear_projection:
											__lowerCAmelCase											=				nn.Linear(__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE			)
								else:
											__lowerCAmelCase											=				nn.Convad(__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,kernel_size=1,stride=1,padding=0			)
					elif self.is_input_vectorized:
								assert sample_size is not None, "Transformer2DModel over discrete input must provide sample_size"
								assert num_vector_embeds is not None, "Transformer2DModel over discrete input must provide num_embed"

								__lowerCAmelCase											=				sample_size
								__lowerCAmelCase											=				sample_size
								__lowerCAmelCase											=				num_vector_embeds
								__lowerCAmelCase											=				self.height * self.width

								__lowerCAmelCase											=				ImagePositionalEmbeddings(
								    num_embed=__SCREAMING_SNAKE_CASE,embed_dim=__SCREAMING_SNAKE_CASE,height=self.height,width=self.width			)
					elif self.is_input_patches:
								assert sample_size is not None, "Transformer2DModel over patched input must provide sample_size"

								__lowerCAmelCase											=				sample_size
								__lowerCAmelCase											=				sample_size

								__lowerCAmelCase											=				patch_size
								__lowerCAmelCase											=				PatchEmbed(
								    height=__SCREAMING_SNAKE_CASE,width=__SCREAMING_SNAKE_CASE,patch_size=__SCREAMING_SNAKE_CASE,in_channels=__SCREAMING_SNAKE_CASE,embed_dim=__SCREAMING_SNAKE_CASE,)

					# 3. Define transformers blocks
					__lowerCAmelCase											=				nn.ModuleList(
					    [
					        BasicTransformerBlock(
					            __SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,dropout=__SCREAMING_SNAKE_CASE,cross_attention_dim=__SCREAMING_SNAKE_CASE,activation_fn=__SCREAMING_SNAKE_CASE,num_embeds_ada_norm=__SCREAMING_SNAKE_CASE,attention_bias=__SCREAMING_SNAKE_CASE,only_cross_attention=__SCREAMING_SNAKE_CASE,upcast_attention=__SCREAMING_SNAKE_CASE,norm_type=__SCREAMING_SNAKE_CASE,norm_elementwise_affine=__SCREAMING_SNAKE_CASE,)
					        for d in range(__SCREAMING_SNAKE_CASE			)
					    ]			)

					# 4. Define output layers
					__lowerCAmelCase											=				in_channels if out_channels is None else out_channels
					if self.is_input_continuous:
								# TODO: should use out_channels for continuous projections
								if use_linear_projection:
											__lowerCAmelCase											=				nn.Linear(__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE			)
								else:
											__lowerCAmelCase											=				nn.Convad(__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,kernel_size=1,stride=1,padding=0			)
					elif self.is_input_vectorized:
								__lowerCAmelCase											=				nn.LayerNorm(__SCREAMING_SNAKE_CASE			)
								__lowerCAmelCase											=				nn.Linear(__SCREAMING_SNAKE_CASE,self.num_vector_embeds - 1			)
					elif self.is_input_patches:
								__lowerCAmelCase											=				nn.LayerNorm(__SCREAMING_SNAKE_CASE,elementwise_affine=__SCREAMING_SNAKE_CASE,eps=1e-6			)
								__lowerCAmelCase											=				nn.Linear(__SCREAMING_SNAKE_CASE,2 * inner_dim			)
								__lowerCAmelCase											=				nn.Linear(__SCREAMING_SNAKE_CASE,patch_size * patch_size * self.out_channels			)







		def   lowerCamelCase__			(  self,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE = None,__SCREAMING_SNAKE_CASE = None,__SCREAMING_SNAKE_CASE = None,__SCREAMING_SNAKE_CASE = None,__SCREAMING_SNAKE_CASE = None,__SCREAMING_SNAKE_CASE = None,__SCREAMING_SNAKE_CASE = True,):



					'''simple docstring'''

					if attention_mask is not None and attention_mask.ndim == 2:
								# assume that mask is expressed as:
								#   (1 = keep,      0 = discard)
								# convert mask into a bias that can be added to attention scores:
								#       (keep = +0,     discard = -10000.0)
								__lowerCAmelCase											=				(1 - attention_mask.to(hidden_states.dtype			)) * -1_0000.0
								__lowerCAmelCase											=				attention_mask.unsqueeze(1			)

					# convert encoder_attention_mask to a bias the same way we do for attention_mask
					if encoder_attention_mask is not None and encoder_attention_mask.ndim == 2:
								__lowerCAmelCase											=				(1 - encoder_attention_mask.to(hidden_states.dtype			)) * -1_0000.0
								__lowerCAmelCase											=				encoder_attention_mask.unsqueeze(1			)

					# 1. Input
					if self.is_input_continuous:
								__lowerCAmelCase						,       __lowerCAmelCase						,       __lowerCAmelCase						,       __lowerCAmelCase											=				hidden_states.shape
								__lowerCAmelCase											=				hidden_states

								__lowerCAmelCase											=				self.norm(__SCREAMING_SNAKE_CASE			)
								if not self.use_linear_projection:
											__lowerCAmelCase											=				self.proj_in(__SCREAMING_SNAKE_CASE			)
											__lowerCAmelCase											=				hidden_states.shape[1]
											__lowerCAmelCase											=				hidden_states.permute(0,2,3,1			).reshape(__SCREAMING_SNAKE_CASE,height * width,__SCREAMING_SNAKE_CASE			)
								else:
											__lowerCAmelCase											=				hidden_states.shape[1]
											__lowerCAmelCase											=				hidden_states.permute(0,2,3,1			).reshape(__SCREAMING_SNAKE_CASE,height * width,__SCREAMING_SNAKE_CASE			)
											__lowerCAmelCase											=				self.proj_in(__SCREAMING_SNAKE_CASE			)
					elif self.is_input_vectorized:
								__lowerCAmelCase											=				self.latent_image_embedding(__SCREAMING_SNAKE_CASE			)
					elif self.is_input_patches:
								__lowerCAmelCase											=				self.pos_embed(__SCREAMING_SNAKE_CASE			)

					# 2. Blocks
					for block in self.transformer_blocks:
								__lowerCAmelCase											=				block(
								    __SCREAMING_SNAKE_CASE,attention_mask=__SCREAMING_SNAKE_CASE,encoder_hidden_states=__SCREAMING_SNAKE_CASE,encoder_attention_mask=__SCREAMING_SNAKE_CASE,timestep=__SCREAMING_SNAKE_CASE,cross_attention_kwargs=__SCREAMING_SNAKE_CASE,class_labels=__SCREAMING_SNAKE_CASE,)

					# 3. Output
					if self.is_input_continuous:
								if not self.use_linear_projection:
											__lowerCAmelCase											=				hidden_states.reshape(__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE			).permute(0,3,1,2			).contiguous()
											__lowerCAmelCase											=				self.proj_out(__SCREAMING_SNAKE_CASE			)
								else:
											__lowerCAmelCase											=				self.proj_out(__SCREAMING_SNAKE_CASE			)
											__lowerCAmelCase											=				hidden_states.reshape(__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE			).permute(0,3,1,2			).contiguous()

								__lowerCAmelCase											=				hidden_states + residual
					elif self.is_input_vectorized:
								__lowerCAmelCase											=				self.norm_out(__SCREAMING_SNAKE_CASE			)
								__lowerCAmelCase											=				self.out(__SCREAMING_SNAKE_CASE			)
								# (batch, self.num_vector_embeds - 1, self.num_latent_pixels)
								__lowerCAmelCase											=				logits.permute(0,2,1			)

								# log(p(x_0))
								__lowerCAmelCase											=				F.log_softmax(logits.double(),dim=1			).float()
					elif self.is_input_patches:
								# TODO: cleanup!
								__lowerCAmelCase											=				self.transformer_blocks[0].norma.emb(
								    __SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,hidden_dtype=hidden_states.dtype			)
								__lowerCAmelCase						,       __lowerCAmelCase											=				self.proj_out_a(F.silu(__SCREAMING_SNAKE_CASE			)			).chunk(2,dim=1			)
								__lowerCAmelCase											=				self.norm_out(__SCREAMING_SNAKE_CASE			) * (1 + scale[:, None]) + shift[:, None]
								__lowerCAmelCase											=				self.proj_out_a(__SCREAMING_SNAKE_CASE			)

								# unpatchify
								__lowerCAmelCase											=				__lowerCAmelCase											=				int(hidden_states.shape[1] ** 0.5			)
								__lowerCAmelCase											=				hidden_states.reshape(
								    shape=(-1, height, width, self.patch_size, self.patch_size, self.out_channels)			)
								__lowerCAmelCase											=				torch.einsum("""nhwpqc->nchpwq""",__SCREAMING_SNAKE_CASE			)
								__lowerCAmelCase											=				hidden_states.reshape(
								    shape=(-1, self.out_channels, height * self.patch_size, width * self.patch_size)			)

					if not return_dict:
								return (output,)

					return TransformeraDModelOutput(sample=__SCREAMING_SNAKE_CASE			)








'''simple docstring'''







import logging
import os
from dataclasses import dataclass, field
from importlib import import_module
from typing import Dict, List, Optional, Tuple

import numpy as np
from seqeval.metrics import classification_report, fa_score, precision_score, recall_score
from utils_ner import Split, TFTokenClassificationDataset, TokenClassificationTask

from transformers import (
    AutoConfig,
    AutoTokenizer,
    EvalPrediction,
    HfArgumentParser,
    TFAutoModelForTokenClassification,
    TFTrainer,
    TFTrainingArguments,
)
from transformers.utils import logging as hf_logging


hf_logging.set_verbosity_info()
hf_logging.enable_default_handler()
hf_logging.enable_explicit_format()


_a					: str      =    logging.getLogger(__name__)





@dataclass
class     _UpperCAmelCase							:
		a   :   str     =field(
		    metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models"""}   )
		a   :   Optional[str]     =field(
		    default=lowerCAmelCase_	,					metadata={"""help""": """Pretrained config name or path if not the same as model_name"""}   )
		a   :   Optional[str]     =field(
		    default="""NER"""	,					metadata={"""help""": """Task type to fine tune in training (e.g. NER, POS, etc)"""}   )
		a   :   Optional[str]     =field(
		    default=lowerCAmelCase_	,					metadata={"""help""": """Pretrained tokenizer name or path if not the same as model_name"""}   )
		a   :   bool     =field(default=lowerCAmelCase_	,					metadata={"""help""": """Set this flag to use fast tokenization."""}   )
		# If you want to tweak more attributes on your tokenizer, you should do it in a distinct script,
		# or just modify its tokenizer_config.json.
		a   :   Optional[str]     =field(
		    default=lowerCAmelCase_	,					metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co"""}	,					)





@dataclass
class     _UpperCAmelCase							:
		a   :   str     =field(
		    metadata={"""help""": """The input data dir. Should contain the .txt files for a CoNLL-2003-formatted task."""}   )
		a   :   Optional[str]     =field(
		    metadata={"""help""": """Path to a file containing all labels. If not specified, CoNLL-2003 labels are used."""}   )
		a   :   int     =field(
		    default=1_28	,					metadata={
		        """help""": (
		            """The maximum total input sequence length after tokenization. Sequences longer """
		            """than this will be truncated, sequences shorter will be padded."""
		        )
		    }	,					)
		a   :   bool     =field(
		    default=lowerCAmelCase_	,					metadata={"""help""": """Overwrite the cached training and evaluation sets"""}   )





def    _lowerCAmelCase						(						)					->					List[str]:
			# See all possible arguments in src/transformers/training_args.py
			# or by passing the --help flag to this script.
			# We now keep distinct sets of args, for a cleaner separation of concerns.
			__lowerCAmelCase											=				HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments)	)
			__lowerCAmelCase						,       __lowerCAmelCase						,       __lowerCAmelCase											=				parser.parse_args_into_dataclasses()

			if (
			    os.path.exists(training_args.output_dir	)
			    and os.listdir(training_args.output_dir	)
			    and training_args.do_train
			    and not training_args.overwrite_output_dir
			):
						raise ValueError(
						    f'Output directory ({training_args.output_dir}) already exists and is not empty. Use'
						    """ --overwrite_output_dir to overcome."""	)

			__lowerCAmelCase											=				import_module("""tasks"""	)

			try:
						__lowerCAmelCase											=				getattr(lowercase					,	model_args.task_type	)
						__lowerCAmelCase											=				token_classification_task_clazz()
			except AttributeError:
						raise ValueError(
						    f'Task {model_args.task_type} needs to be defined as a TokenClassificationTask subclass in {module}. '
						    f'Available tasks classes are: {TokenClassificationTask.__subclasses__()}'	)

			# Setup logging
			logging.basicConfig(
			    format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s"""					,	datefmt="""%m/%d/%Y %H:%M:%S"""					,	level=logging.INFO					,	)
			logger.info(
			    """n_replicas: %s, distributed training: %s, 16-bits training: %s"""					,	training_args.n_replicas					,	bool(training_args.n_replicas > 1	)					,	training_args.fpaa					,	)
			logger.info("""Training/evaluation parameters %s"""					,	lowercase	)

			# Prepare Token Classification task
			__lowerCAmelCase											=				token_classification_task.get_labels(data_args.labels	)
			__lowerCAmelCase											=				dict(enumerate(lowercase	)	)
			__lowerCAmelCase											=				len(lowercase	)

			# Load pretrained model and tokenizer
			#
			# Distributed training:
			# The .from_pretrained methods guarantee that only one local process can concurrently
			# download model & vocab.

			__lowerCAmelCase											=				AutoConfig.from_pretrained(
			    model_args.config_name if model_args.config_name else model_args.model_name_or_path					,	num_labels=lowercase					,	idalabel=lowercase					,	labelaid={label: i for i, label in enumerate(lowercase	)}					,	cache_dir=model_args.cache_dir					,	)
			__lowerCAmelCase											=				AutoTokenizer.from_pretrained(
			    model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path					,	cache_dir=model_args.cache_dir					,	use_fast=model_args.use_fast					,	)

			with training_args.strategy.scope():
						__lowerCAmelCase											=				TFAutoModelForTokenClassification.from_pretrained(
						    model_args.model_name_or_path					,	from_pt=bool(""".bin""" in model_args.model_name_or_path	)					,	config=lowercase					,	cache_dir=model_args.cache_dir					,	)

			# Get datasets
			__lowerCAmelCase											=				(
			    TFTokenClassificationDataset(
			        token_classification_task=lowercase					,	data_dir=data_args.data_dir					,	tokenizer=lowercase					,	labels=lowercase					,	model_type=config.model_type					,	max_seq_length=data_args.max_seq_length					,	overwrite_cache=data_args.overwrite_cache					,	mode=Split.train					,	)
			    if training_args.do_train
			    else None
			)
			__lowerCAmelCase											=				(
			    TFTokenClassificationDataset(
			        token_classification_task=lowercase					,	data_dir=data_args.data_dir					,	tokenizer=lowercase					,	labels=lowercase					,	model_type=config.model_type					,	max_seq_length=data_args.max_seq_length					,	overwrite_cache=data_args.overwrite_cache					,	mode=Split.dev					,	)
			    if training_args.do_eval
			    else None
			)

			def align_predictions(lowercase					,	lowercase	) -> Tuple[List[int], List[int]]:
						__lowerCAmelCase											=				np.argmax(lowercase					,	axis=2	)
						__lowerCAmelCase						,       __lowerCAmelCase											=				preds.shape
						__lowerCAmelCase											=				[[] for _ in range(lowercase	)]
						__lowerCAmelCase											=				[[] for _ in range(lowercase	)]

						for i in range(lowercase	):
									for j in range(lowercase	):
												if label_ids[i, j] != -100:
															out_label_list[i].append(label_map[label_ids[i][j]]	)
															preds_list[i].append(label_map[preds[i][j]]	)

						return preds_list, out_label_list

			def compute_metrics(lowercase	) -> Dict:
						__lowerCAmelCase						,       __lowerCAmelCase											=				align_predictions(p.predictions					,	p.label_ids	)

						return {
						    "precision": precision_score(lowercase					,	lowercase	),
						    "recall": recall_score(lowercase					,	lowercase	),
						    "f1": fa_score(lowercase					,	lowercase	),
						}

			# Initialize our Trainer
			__lowerCAmelCase											=				TFTrainer(
			    model=lowercase					,	args=lowercase					,	train_dataset=train_dataset.get_dataset() if train_dataset else None					,	eval_dataset=eval_dataset.get_dataset() if eval_dataset else None					,	compute_metrics=lowercase					,	)

			# Training
			if training_args.do_train:
						trainer.train()
						trainer.save_model()
						tokenizer.save_pretrained(training_args.output_dir	)

			# Evaluation
			__lowerCAmelCase											=				{}
			if training_args.do_eval:
						logger.info("""*** Evaluate ***"""	)

						__lowerCAmelCase											=				trainer.evaluate()
						__lowerCAmelCase											=				os.path.join(training_args.output_dir					,	"""eval_results.txt"""	)

						with open(lowercase					,	"""w"""	) as writer:
									logger.info("""***** Eval results *****"""	)

									for key, value in result.items():
												logger.info("""  %s = %s"""					,	lowercase					,	lowercase	)
												writer.write("""%s = %s\n""" % (key, value)	)

									results.update(lowercase	)

    # Predict
			if training_args.do_predict:
						__lowerCAmelCase											=				TFTokenClassificationDataset(
						    token_classification_task=lowercase					,	data_dir=data_args.data_dir					,	tokenizer=lowercase					,	labels=lowercase					,	model_type=config.model_type					,	max_seq_length=data_args.max_seq_length					,	overwrite_cache=data_args.overwrite_cache					,	mode=Split.test					,	)

						__lowerCAmelCase						,       __lowerCAmelCase						,       __lowerCAmelCase											=				trainer.predict(test_dataset.get_dataset()	)
						__lowerCAmelCase						,       __lowerCAmelCase											=				align_predictions(lowercase					,	lowercase	)
						__lowerCAmelCase											=				classification_report(lowercase					,	lowercase	)

						logger.info("""\n%s"""					,	lowercase	)

						__lowerCAmelCase											=				os.path.join(training_args.output_dir					,	"""test_results.txt"""	)

						with open(lowercase					,	"""w"""	) as writer:
									writer.write("""%s\n""" % report	)

						# Save predictions
						__lowerCAmelCase											=				os.path.join(training_args.output_dir					,	"""test_predictions.txt"""	)

						with open(lowercase					,	"""w"""	) as writer:
									with open(os.path.join(data_args.data_dir					,	"""test.txt"""	)					,	"""r"""	) as f:
												__lowerCAmelCase											=				0

												for line in f:
															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]:
																		__lowerCAmelCase											=				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]	)

			return results


if __name__ == "__main__":
	main()

