


'''simple docstring'''






import argparse
import itertools
import operator
import sys
from collections import OrderedDict

from run_eval import datetime_now, run_generate

from utils import ROUGE_KEYS


# A table of supported tasks and the list of scores in the order of importance to be sorted by.
# To add a new task, simply list the score names that `run_eval.run_generate()` returns
_A      =     {
    """translation""": ["""bleu"""],
    """summarization""": ROUGE_KEYS,
}


def A_					(				__SCREAMING_SNAKE_CASE :					str       )     ->  Dict:
					__SCREAMING_SNAKE_CASE					:							Tuple			   = search.split()
					__SCREAMING_SNAKE_CASE					:							List[Any]			   = dict((g.split('''='''       ) for g in groups)       )
					__SCREAMING_SNAKE_CASE					:							List[Any]			   = list(entries.keys()       )
					__SCREAMING_SNAKE_CASE					:							Dict			   = [[f"""--{k} {v}""" for v in vs.split(''':'''       )] for k, vs in entries.items()]
					__SCREAMING_SNAKE_CASE					:							int			   = [list(__SCREAMING_SNAKE_CASE       ) for x in itertools.product(*__SCREAMING_SNAKE_CASE       )]
					return matrix, entry_names


def A_					(				)     ->  int:
					__SCREAMING_SNAKE_CASE					:							Optional[Any]			   = sys.argv[0]

					__SCREAMING_SNAKE_CASE					:							Optional[Any]			   = argparse.ArgumentParser(
					    usage=(
					        '''\n\nImportant: this script accepts all arguments `run_eval.py` accepts and then a few extra, therefore'''
					        ''' refer to `run_eval.py -h` for the complete list.'''
					    )       )
					parser.add_argument(
					    '''--search'''	,   type=__SCREAMING_SNAKE_CASE	,   required=__SCREAMING_SNAKE_CASE	,   help='''param space to search, e.g. "num_beams=5:10 length_penalty=0.8:1.0:1.2"'''	,   )
					parser.add_argument(
					    '''--bs'''	,   type=__SCREAMING_SNAKE_CASE	,   default=8	,   required=__SCREAMING_SNAKE_CASE	,   help='''initial batch size (may get reduced if it\'s too big)'''       )
					parser.add_argument('''--task'''	,   type=__SCREAMING_SNAKE_CASE	,   help='''used for task_specific_params + metrics'''       )
					parser.add_argument(
					    '''--info'''	,   nargs='''?'''	,   type=__SCREAMING_SNAKE_CASE	,   const=datetime_now()	,   help=(
					        '''add custom notes to be printed before the results table. If no value is passed, the current datetime'''
					        ''' string will be used.'''
					    )	,   )
					__SCREAMING_SNAKE_CASE					:							str			   = parser.parse_known_args()
					# we share some of the args
					args_main.extend(['''--task''', args.task]       )
					__SCREAMING_SNAKE_CASE					:							List[Any]			   = [prog] + args_main

					# to support variations like translation_en_to_de"
					__SCREAMING_SNAKE_CASE					:							List[str]			   = '''translation''' if '''translation''' in args.task else '''summarization'''

					__SCREAMING_SNAKE_CASE					:							Tuple			   = parse_search_arg(args.search       )
					__SCREAMING_SNAKE_CASE					:							Union[str, Any]			   = task_score_names[task]  # score cols first
					__SCREAMING_SNAKE_CASE					:							Optional[int]			   = {col: len(str(__SCREAMING_SNAKE_CASE       )       ) for col in col_names}
					__SCREAMING_SNAKE_CASE					:							Dict			   = []
					for r in matrix:
										__SCREAMING_SNAKE_CASE					:							List[str]			   = dict((x.replace('''--'''	,   ''''''       ).split() for x in r)       )
										__SCREAMING_SNAKE_CASE					:							Tuple			   = ''' '''.join(__SCREAMING_SNAKE_CASE       ).split()
										args_exp.extend(['''--bs''', str(args.bs       )]       )  # in case we need to reduce its size due to CUDA OOM
										__SCREAMING_SNAKE_CASE					:							Dict			   = args_normal + args_exp

										# XXX: need to trap CUDA OOM and lower args.bs if that happens and retry

										__SCREAMING_SNAKE_CASE					:							Union[str, Any]			   = run_generate(verbose=__SCREAMING_SNAKE_CASE       )
										# make sure scores are first in the table
										__SCREAMING_SNAKE_CASE					:							Any			   = OrderedDict()
										for score in task_score_names[task]:
															__SCREAMING_SNAKE_CASE					:							int			   = scores[score]
										result.update(__SCREAMING_SNAKE_CASE       )
										results.append(__SCREAMING_SNAKE_CASE       )

										# find widest entries
										for k, v in result.items():
															__SCREAMING_SNAKE_CASE					:							List[Any]			   = len(str(__SCREAMING_SNAKE_CASE       )       )
															if l > col_widths[k]:
																				__SCREAMING_SNAKE_CASE					:							Any			   = l

					__SCREAMING_SNAKE_CASE					:							Optional[int]			   = sorted(__SCREAMING_SNAKE_CASE	,   key=operator.itemgetter(*task_score_names[task]       )	,   reverse=__SCREAMING_SNAKE_CASE       )
					print(''' | '''.join([f"""{col:{col_widths[col]}}""" for col in col_names]       )       )
					print(''' | '''.join([f"""{"-"*col_widths[col]}""" for col in col_names]       )       )
					for row in results_sorted:
										print(''' | '''.join([f"""{row[col]:{col_widths[col]}}""" for col in col_names]       )       )

					__SCREAMING_SNAKE_CASE					:							Any			   = results_sorted[0]
					for score in task_score_names[task]:
										del best[score]
					__SCREAMING_SNAKE_CASE					:							Union[str, Any]			   = [f"""--{k} {v}""" for k, v in best.items()]
					__SCREAMING_SNAKE_CASE					:							Tuple			   = ['''--bs''', str(args.bs       )]
					if args.info:
										print(f"""\nInfo: {args.info}"""       )
					print('''\nBest score args:'''       )
					print(''' '''.join(args_main + best_args + dyn_args       )       )

					return results_sorted


if __name__ == "__main__":
						# Usage:
						# [normal-run_eval_search.py cmd plus] \
						# --search="num_beams=1:5:10 length_penalty=0.8:1:1.2 early_stopping=true:false"
						#
						# Example:
						# PYTHONPATH="src:examples/seq2seq" python examples/seq2seq/run_eval_search.py $MODEL_NAME \
						# $DATA_DIR/val.source $SAVE_DIR/test_translations.txt --reference_path $DATA_DIR/val.target \
						# --score_path $SAVE_DIR/test_bleu.json --bs $BS --task translation \
						# --search="num_beams=1:5:10 length_penalty=0.8:1:1.2 early_stopping=true:false"
						run_search()






'''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      SCREAMING_SNAKE_CASE_	( snake_case		):
			__a					:      torch.FloatTensor






class      SCREAMING_SNAKE_CASE_	( snake_case					,					snake_case		):




			@register_to_config
			def __init__(      self					,      lowercase = 1_6					,      lowercase = 8_8					,      lowercase = None					,      lowercase = None					,      lowercase = 1					,      lowercase = 0.0					,      lowercase = 3_2					,      lowercase = None					,      lowercase = False					,      lowercase = None					,      lowercase = None					,      lowercase = None					,      lowercase = "geglu"					,      lowercase = None					,      lowercase = False					,      lowercase = False					,      lowercase = False					,      lowercase = "layer_norm"					,      lowercase = True					,      )			->						Optional[int]:


								'''simple docstring'''






								super().__init__()
								__SCREAMING_SNAKE_CASE					:							Union[str, Any]			   = use_linear_projection
								__SCREAMING_SNAKE_CASE					:							Dict			   = num_attention_heads
								__SCREAMING_SNAKE_CASE					:							Optional[int]			   = attention_head_dim
								__SCREAMING_SNAKE_CASE					:							Any			   = 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
								__SCREAMING_SNAKE_CASE					:							List[Any]			   = (in_channels is not None) and (patch_size is None)
								__SCREAMING_SNAKE_CASE					:							List[str]			   = num_vector_embeds is not None
								__SCREAMING_SNAKE_CASE					:							Any			   = in_channels is not None and patch_size is not None

								if norm_type == "layer_norm" and num_embeds_ada_norm is not None:
													__SCREAMING_SNAKE_CASE					:							Any			   = (
													    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'''					,      lowercase					,      standard_warn=lowercase       )
													__SCREAMING_SNAKE_CASE					:							str			   = '''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:
													__SCREAMING_SNAKE_CASE					:							List[Any]			   = in_channels

													__SCREAMING_SNAKE_CASE					:							Optional[int]			   = torch.nn.GroupNorm(num_groups=lowercase					,      num_channels=lowercase					,      eps=1e-6					,      affine=lowercase       )
													if use_linear_projection:
																		__SCREAMING_SNAKE_CASE					:							List[Any]			   = nn.Linear(lowercase					,      lowercase       )
													else:
																		__SCREAMING_SNAKE_CASE					:							Any			   = nn.Convad(lowercase					,      lowercase					,      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"

													__SCREAMING_SNAKE_CASE					:							Union[str, Any]			   = sample_size
													__SCREAMING_SNAKE_CASE					:							str			   = sample_size
													__SCREAMING_SNAKE_CASE					:							int			   = num_vector_embeds
													__SCREAMING_SNAKE_CASE					:							str			   = self.height * self.width

													__SCREAMING_SNAKE_CASE					:							int			   = ImagePositionalEmbeddings(
													    num_embed=lowercase					,      embed_dim=lowercase					,      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"

													__SCREAMING_SNAKE_CASE					:							Any			   = sample_size
													__SCREAMING_SNAKE_CASE					:							Any			   = sample_size

													__SCREAMING_SNAKE_CASE					:							int			   = patch_size
													__SCREAMING_SNAKE_CASE					:							Any			   = PatchEmbed(
													    height=lowercase					,      width=lowercase					,      patch_size=lowercase					,      in_channels=lowercase					,      embed_dim=lowercase					,      )

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

								# 4. Define output layers
								__SCREAMING_SNAKE_CASE					:							List[str]			   = 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:
																		__SCREAMING_SNAKE_CASE					:							str			   = nn.Linear(lowercase					,      lowercase       )
													else:
																		__SCREAMING_SNAKE_CASE					:							Union[str, Any]			   = nn.Convad(lowercase					,      lowercase					,      kernel_size=1					,      stride=1					,      padding=0       )
								elif self.is_input_vectorized:
													__SCREAMING_SNAKE_CASE					:							Optional[Any]			   = nn.LayerNorm(lowercase       )
													__SCREAMING_SNAKE_CASE					:							int			   = nn.Linear(lowercase					,      self.num_vector_embeds - 1       )
								elif self.is_input_patches:
													__SCREAMING_SNAKE_CASE					:							Union[str, Any]			   = nn.LayerNorm(lowercase					,      elementwise_affine=lowercase					,      eps=1e-6       )
													__SCREAMING_SNAKE_CASE					:							Any			   = nn.Linear(lowercase					,      2 * inner_dim       )
													__SCREAMING_SNAKE_CASE					:							Any			   = nn.Linear(lowercase					,      patch_size * patch_size * self.out_channels       )




			def 			_snake_case   (      self					,      lowercase					,      lowercase = None					,      lowercase = None					,      lowercase = None					,      lowercase = None					,      lowercase = None					,      lowercase = None					,      lowercase = True					,      )			->						Optional[Any]:


								'''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)
													__SCREAMING_SNAKE_CASE					:							Optional[int]			   = (1 - attention_mask.to(hidden_states.dtype       )) * -1_0_0_0_0.0
													__SCREAMING_SNAKE_CASE					:							Dict			   = 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:
													__SCREAMING_SNAKE_CASE					:							Optional[Any]			   = (1 - encoder_attention_mask.to(hidden_states.dtype       )) * -1_0_0_0_0.0
													__SCREAMING_SNAKE_CASE					:							List[Any]			   = encoder_attention_mask.unsqueeze(1       )

								# 1. Input
								if self.is_input_continuous:
													__SCREAMING_SNAKE_CASE   ,   __SCREAMING_SNAKE_CASE   ,   __SCREAMING_SNAKE_CASE   ,   __SCREAMING_SNAKE_CASE					:							Optional[Any]			   = hidden_states.shape
													__SCREAMING_SNAKE_CASE					:							Dict			   = hidden_states

													__SCREAMING_SNAKE_CASE					:							str			   = self.norm(lowercase       )
													if not self.use_linear_projection:
																		__SCREAMING_SNAKE_CASE					:							int			   = self.proj_in(lowercase       )
																		__SCREAMING_SNAKE_CASE					:							List[str]			   = hidden_states.shape[1]
																		__SCREAMING_SNAKE_CASE					:							List[str]			   = hidden_states.permute(0					,      2					,      3					,      1       ).reshape(lowercase					,      height * width					,      lowercase       )
													else:
																		__SCREAMING_SNAKE_CASE					:							Tuple			   = hidden_states.shape[1]
																		__SCREAMING_SNAKE_CASE					:							Optional[int]			   = hidden_states.permute(0					,      2					,      3					,      1       ).reshape(lowercase					,      height * width					,      lowercase       )
																		__SCREAMING_SNAKE_CASE					:							Optional[int]			   = self.proj_in(lowercase       )
								elif self.is_input_vectorized:
													__SCREAMING_SNAKE_CASE					:							Union[str, Any]			   = self.latent_image_embedding(lowercase       )
								elif self.is_input_patches:
													__SCREAMING_SNAKE_CASE					:							int			   = self.pos_embed(lowercase       )

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

								# 3. Output
								if self.is_input_continuous:
													if not self.use_linear_projection:
																		__SCREAMING_SNAKE_CASE					:							Tuple			   = hidden_states.reshape(lowercase					,      lowercase					,      lowercase					,      lowercase       ).permute(0					,      3					,      1					,      2       ).contiguous()
																		__SCREAMING_SNAKE_CASE					:							Union[str, Any]			   = self.proj_out(lowercase       )
													else:
																		__SCREAMING_SNAKE_CASE					:							str			   = self.proj_out(lowercase       )
																		__SCREAMING_SNAKE_CASE					:							str			   = hidden_states.reshape(lowercase					,      lowercase					,      lowercase					,      lowercase       ).permute(0					,      3					,      1					,      2       ).contiguous()

													__SCREAMING_SNAKE_CASE					:							str			   = hidden_states + residual
								elif self.is_input_vectorized:
													__SCREAMING_SNAKE_CASE					:							Dict			   = self.norm_out(lowercase       )
													__SCREAMING_SNAKE_CASE					:							str			   = self.out(lowercase       )
													# (batch, self.num_vector_embeds - 1, self.num_latent_pixels)
													__SCREAMING_SNAKE_CASE					:							Optional[int]			   = logits.permute(0					,      2					,      1       )

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

													# unpatchify
													__SCREAMING_SNAKE_CASE					:							List[str]			   = int(hidden_states.shape[1] ** 0.5       )
													__SCREAMING_SNAKE_CASE					:							int			   = hidden_states.reshape(
													    shape=(-1, height, width, self.patch_size, self.patch_size, self.out_channels)       )
													__SCREAMING_SNAKE_CASE					:							int			   = torch.einsum('''nhwpqc->nchpwq'''					,      lowercase       )
													__SCREAMING_SNAKE_CASE					:							Any			   = 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=lowercase       )


