




"""simple docstring"""


from typing import Optional

import numpy as np
import torch
from torch import nn
from transformers import GPTaConfig, GPTaLMHeadModel
from transformers.modeling_utils import ModuleUtilsMixin

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin






class 					lowercase(     UpperCAmelCase_       ,			UpperCAmelCase_       ,			UpperCAmelCase_ ):







 '''simple docstring'''





 lowercase__ 					=					[r"""h\.\d+\.attn\.bias""", r"""h\.\d+\.attn\.masked_bias"""]


 @register_to_config
 def __init__(				self:		int,      a_:		str,      a_:		Tuple,      a_:		Dict = None,      a_:		str = 50_257,      a_:		Tuple = 1_024,      a_:		int = 768,      a_:		Dict = 12,      a_:		List[Any] = 12,      a_:		Tuple = None,      a_:		int = "gelu_new",      a_:		Union[str, Any] = 0.1,      a_:		int = 0.1,      a_:		Optional[int] = 0.1,      a_:		int = 1E-5,      a_:		Optional[Any] = 0.02,      a_:		List[Any] = True,      a_:		str = True,      a_:		Optional[int] = False,      a_:		Any = False,      ):




   '''simple docstring'''



   super().__init__()

   _snake_case							:		Optional[int]								=  prefix_length

   if prefix_inner_dim != n_embd and prefix_hidden_dim is None:
     raise ValueError(
         f"`prefix_hidden_dim` cannot be `None` when `prefix_inner_dim`: {prefix_hidden_dim} and"
         f" `n_embd`: {n_embd} are not equal."		)

   _snake_case							:		int								=  prefix_inner_dim
   _snake_case							:		List[Any]								=  prefix_hidden_dim

   _snake_case							:		int								=  (
       nn.Linear(self.prefix_inner_dim,      self.prefix_hidden_dim		)
       if self.prefix_hidden_dim is not None
       else nn.Identity()
   )
   _snake_case							:		Tuple								=  (
       nn.Linear(self.prefix_hidden_dim,      __lowercase		) if self.prefix_hidden_dim is not None else nn.Identity()
   )

   _snake_case							:		Union[str, Any]								=  GPTaConfig(
       vocab_size=__lowercase,      n_positions=__lowercase,      n_embd=__lowercase,      n_layer=__lowercase,      n_head=__lowercase,      n_inner=__lowercase,      activation_function=__lowercase,      resid_pdrop=__lowercase,      embd_pdrop=__lowercase,      attn_pdrop=__lowercase,      layer_norm_epsilon=__lowercase,      initializer_range=__lowercase,      scale_attn_weights=__lowercase,      use_cache=__lowercase,      scale_attn_by_inverse_layer_idx=__lowercase,      reorder_and_upcast_attn=__lowercase,      )
   _snake_case							:		List[str]								=  GPTaLMHeadModel(__lowercase		)


 def  UpperCamelCase_   (				self:		Tuple,      a_:		int,      a_:		int,      a_:		str = None,      a_:		Any = None,      ):




   '''simple docstring'''



   _snake_case							:		Any								=  self.transformer.transformer.wte(__lowercase		)
   _snake_case							:		Union[str, Any]								=  self.encode_prefix(__lowercase		)
   _snake_case							:		List[str]								=  self.decode_prefix(__lowercase		)
   _snake_case							:		str								=  torch.cat((prefix_embeds, embedding_text),      dim=1		)

   if labels is not None:
     _snake_case							:		List[Any]								=  self.get_dummy_token(input_ids.shape[0],      input_ids.device		)
     _snake_case							:		int								=  torch.cat((dummy_token, input_ids),      dim=1		)
   _snake_case							:		int								=  self.transformer(inputs_embeds=__lowercase,      labels=__lowercase,      attention_mask=__lowercase		)
   if self.prefix_hidden_dim is not None:
     return out, hidden
   else:
     return out


 def  UpperCamelCase_   (				self:		Tuple,      a_:		List[Any],      a_:		Dict		):




   '''simple docstring'''



   return torch.zeros(__lowercase,      self.prefix_length,      dtype=torch.intaa,      device=__lowercase		)


 def  UpperCamelCase_   (				self:		Dict,      a_:		str		):




   '''simple docstring'''



   return self.encode_prefix(__lowercase		)


 @torch.no_grad()
 def  UpperCamelCase_   (				self:		List[str],      a_:		Optional[Any],      a_:		Union[str, Any],      a_:		List[str]		):




   '''simple docstring'''



   _snake_case							:		Tuple								=  torch.split(__lowercase,      1,      dim=0		)
   _snake_case							:		Tuple								=  []
   _snake_case							:		Union[str, Any]								=  []
   for feature in features:
     _snake_case							:		Optional[Any]								=  self.decode_prefix(feature.to(__lowercase		)		)  # back to the clip feature
     # Only support beam search for now
     _snake_case							:		List[str]								=  self.generate_beam(
         input_embeds=__lowercase,      device=__lowercase,      eos_token_id=__lowercase		)
     generated_tokens.append(output_tokens[0]		)
     generated_seq_lengths.append(seq_lengths[0]		)
   _snake_case							:		Optional[Any]								=  torch.stack(__lowercase		)
   _snake_case							:		str								=  torch.stack(__lowercase		)
   return generated_tokens, generated_seq_lengths


 @torch.no_grad()
 def  UpperCamelCase_   (				self:		Dict,      a_:		List[str]=None,      a_:		List[str]=None,      a_:		Any=None,      a_:		Optional[Any] = 5,      a_:		Union[str, Any] = 67,      a_:		str = 1.0,      a_:		Union[str, Any] = None,      ):




   '''simple docstring'''



   _snake_case							:		Optional[Any]								=  eos_token_id
   _snake_case							:		str								=  None
   _snake_case							:		Optional[int]								=  None
   _snake_case							:		List[Any]								=  torch.ones(__lowercase,      device=__lowercase,      dtype=torch.int		)
   _snake_case							:		List[str]								=  torch.zeros(__lowercase,      device=__lowercase,      dtype=torch.bool		)

   if input_embeds is not None:
     _snake_case							:		Dict								=  input_embeds
   else:
     _snake_case							:		int								=  self.transformer.transformer.wte(__lowercase		)

   for i in range(__lowercase		):
     _snake_case							:		List[str]								=  self.transformer(inputs_embeds=__lowercase		)
     _snake_case							:		List[str]								=  outputs.logits
     _snake_case							:		Union[str, Any]								=  logits[:, -1, :] / (temperature if temperature > 0 else 1.0)
     _snake_case							:		List[str]								=  logits.softmax(-1		).log()

     if scores is None:
       _snake_case							:		Optional[Any]								=  logits.topk(__lowercase,      -1		)
       _snake_case							:		List[str]								=  generated.expand(__lowercase,      *generated.shape[1:]		)
       _snake_case							:		Optional[int]								=  next_tokens.permute(1,      0		), scores.squeeze(0		)
       if tokens is None:
         _snake_case							:		str								=  next_tokens
       else:
         _snake_case							:		List[Any]								=  tokens.expand(__lowercase,      *tokens.shape[1:]		)
         _snake_case							:		List[str]								=  torch.cat((tokens, next_tokens),      dim=1		)
     else:
       _snake_case							:		Dict								=  -float(np.inf		)
       _snake_case							:		Tuple								=  0
       _snake_case							:		List[str]								=  scores[:, None] + logits
       seq_lengths[~is_stopped] += 1
       _snake_case							:		Optional[Any]								=  scores_sum / seq_lengths[:, None]
       _snake_case							:		Optional[Any]								=  scores_sum_average.view(-1		).topk(__lowercase,      -1		)
       _snake_case							:		str								=  next_tokens // scores_sum.shape[1]
       _snake_case							:		Tuple								=  seq_lengths[next_tokens_source]
       _snake_case							:		str								=  next_tokens % scores_sum.shape[1]
       _snake_case							:		int								=  next_tokens.unsqueeze(1		)
       _snake_case							:		Any								=  tokens[next_tokens_source]
       _snake_case							:		Optional[int]								=  torch.cat((tokens, next_tokens),      dim=1		)
       _snake_case							:		Optional[Any]								=  generated[next_tokens_source]
       _snake_case							:		str								=  scores_sum_average * seq_lengths
       _snake_case							:		Union[str, Any]								=  is_stopped[next_tokens_source]

     _snake_case							:		Optional[int]								=  self.transformer.transformer.wte(next_tokens.squeeze()		).view(generated.shape[0],      1,      -1		)
     _snake_case							:		int								=  torch.cat((generated, next_token_embed),      dim=1		)
     _snake_case							:		List[str]								=  is_stopped + next_tokens.eq(__lowercase		).squeeze()
     if is_stopped.all():
       break

   _snake_case							:		Any								=  scores / seq_lengths
   _snake_case							:		Optional[int]								=  scores.argsort(descending=__lowercase		)
   # tokens tensors are already padded to max_seq_length
   _snake_case							:		int								=  [tokens[i] for i in order]
   _snake_case							:		str								=  torch.stack(__lowercase,      dim=0		)
   _snake_case							:		List[str]								=  torch.tensor([seq_lengths[i] for i in order],      dtype=seq_lengths.dtype		)
   return output_texts, seq_lengths



import glob
import os
import random
from string import ascii_lowercase, digits

import cva
import numpy as np

# Parrameters
__lowercase							 =					(720, 1280)  # Height, Width
__lowercase							 =					(0.4, 0.6)  # if height or width lower than this scale, drop it.
__lowercase							 =					1 / 100
__lowercase							 =					''''''
__lowercase							 =					''''''
__lowercase							 =					''''''
__lowercase							 =					250



def lowerCamelCase  (   ):
    '''simple docstring'''







    __UpperCamelCase  ,       __UpperCamelCase     :List[Any]			  =		get_dataset(SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	)
    for index in range(SCREAMING_SNAKE_CASE	):
        __UpperCamelCase     :Optional[Any]			  =		random.sample(range(len(SCREAMING_SNAKE_CASE	)	)  ,			4	)
        __UpperCamelCase  ,       __UpperCamelCase  ,       __UpperCamelCase     :str			  =		update_image_and_anno(
            SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			filter_scale=SCREAMING_SNAKE_CASE  ,			)

        # Get random string code: '7b7ad245cdff75241935e4dd860f3bad'
        __UpperCamelCase     :List[Any]			  =		random_chars(32	)
        __UpperCamelCase     :List[str]			  =		path.split(os.sep	)[-1].rsplit('''.'''  ,			1	)[0]
        __UpperCamelCase     :Tuple			  =		f"""{OUTPUT_DIR}/{file_name}_MOSAIC_{letter_code}"""
        cva.imwrite(f"""{file_root}.jpg"""  ,			SCREAMING_SNAKE_CASE  ,			[cva.IMWRITE_JPEG_QUALITY, 85]	)
        print(f"""Succeeded {index+1}/{NUMBER_IMAGES} with {file_name}"""	)
        __UpperCamelCase     :Optional[Any]			  =		[]
        for anno in new_annos:
            __UpperCamelCase     :int			  =		anno[3] - anno[1]
            __UpperCamelCase     :Optional[int]			  =		anno[4] - anno[2]
            __UpperCamelCase     :int			  =		anno[1] + width / 2
            __UpperCamelCase     :List[str]			  =		anno[2] + height / 2
            __UpperCamelCase     :str			  =		f"""{anno[0]} {x_center} {y_center} {width} {height}"""
            annos_list.append(SCREAMING_SNAKE_CASE	)
        with open(f"""{file_root}.txt"""  ,			'''w'''	) as outfile:
            outfile.write('''\n'''.join(line for line in annos_list	)	)



def lowerCamelCase  (   SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	):
    '''simple docstring'''







    __UpperCamelCase     :str			  =		[]
    __UpperCamelCase     :str			  =		[]
    for label_file in glob.glob(os.path.join(SCREAMING_SNAKE_CASE  ,			'''*.txt'''	)	):
        __UpperCamelCase     :Any			  =		label_file.split(os.sep	)[-1].rsplit('''.'''  ,			1	)[0]
        with open(SCREAMING_SNAKE_CASE	) as in_file:
            __UpperCamelCase     :str			  =		in_file.readlines()
        __UpperCamelCase     :Optional[int]			  =		os.path.join(SCREAMING_SNAKE_CASE  ,			f"""{label_name}.jpg"""	)

        __UpperCamelCase     :int			  =		[]
        for obj_list in obj_lists:
            __UpperCamelCase     :Optional[int]			  =		obj_list.rstrip('''\n'''	).split(''' '''	)
            __UpperCamelCase     :Any			  =		float(obj[1]	) - float(obj[3]	) / 2
            __UpperCamelCase     :List[str]			  =		float(obj[2]	) - float(obj[4]	) / 2
            __UpperCamelCase     :Dict			  =		float(obj[1]	) + float(obj[3]	) / 2
            __UpperCamelCase     :List[str]			  =		float(obj[2]	) + float(obj[4]	) / 2

            boxes.append([int(obj[0]	), xmin, ymin, xmax, ymax]	)
        if not boxes:
            continue
        img_paths.append(SCREAMING_SNAKE_CASE	)
        labels.append(SCREAMING_SNAKE_CASE	)
    return img_paths, labels



def lowerCamelCase  (   SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE = 0.0  ,			):
    '''simple docstring'''







    __UpperCamelCase     :List[str]			  =		np.zeros([output_size[0], output_size[1], 3]  ,			dtype=np.uinta	)
    __UpperCamelCase     :List[Any]			  =		scale_range[0] + random.random() * (scale_range[1] - scale_range[0])
    __UpperCamelCase     :int			  =		scale_range[0] + random.random() * (scale_range[1] - scale_range[0])
    __UpperCamelCase     :Optional[int]			  =		int(scale_x * output_size[1]	)
    __UpperCamelCase     :Any			  =		int(scale_y * output_size[0]	)

    __UpperCamelCase     :List[str]			  =		[]
    __UpperCamelCase     :Dict			  =		[]
    for i, index in enumerate(SCREAMING_SNAKE_CASE	):
        __UpperCamelCase     :Any			  =		all_img_list[index]
        path_list.append(SCREAMING_SNAKE_CASE	)
        __UpperCamelCase     :Any			  =		all_annos[index]
        __UpperCamelCase     :Union[str, Any]			  =		cva.imread(SCREAMING_SNAKE_CASE	)
        if i == 0:  # top-left
            __UpperCamelCase     :str			  =		cva.resize(SCREAMING_SNAKE_CASE  ,			(divid_point_x, divid_point_y)	)
            __UpperCamelCase     :Union[str, Any]			  =		img
            for bbox in img_annos:
                __UpperCamelCase     :Union[str, Any]			  =		bbox[1] * scale_x
                __UpperCamelCase     :Optional[Any]			  =		bbox[2] * scale_y
                __UpperCamelCase     :int			  =		bbox[3] * scale_x
                __UpperCamelCase     :Union[str, Any]			  =		bbox[4] * scale_y
                new_anno.append([bbox[0], xmin, ymin, xmax, ymax]	)
        elif i == 1:  # top-right
            __UpperCamelCase     :str			  =		cva.resize(SCREAMING_SNAKE_CASE  ,			(output_size[1] - divid_point_x, divid_point_y)	)
            __UpperCamelCase     :List[str]			  =		img
            for bbox in img_annos:
                __UpperCamelCase     :str			  =		scale_x + bbox[1] * (1 - scale_x)
                __UpperCamelCase     :Dict			  =		bbox[2] * scale_y
                __UpperCamelCase     :Optional[Any]			  =		scale_x + bbox[3] * (1 - scale_x)
                __UpperCamelCase     :List[Any]			  =		bbox[4] * scale_y
                new_anno.append([bbox[0], xmin, ymin, xmax, ymax]	)
        elif i == 2:  # bottom-left
            __UpperCamelCase     :str			  =		cva.resize(SCREAMING_SNAKE_CASE  ,			(divid_point_x, output_size[0] - divid_point_y)	)
            __UpperCamelCase     :Optional[int]			  =		img
            for bbox in img_annos:
                __UpperCamelCase     :Tuple			  =		bbox[1] * scale_x
                __UpperCamelCase     :Optional[Any]			  =		scale_y + bbox[2] * (1 - scale_y)
                __UpperCamelCase     :Tuple			  =		bbox[3] * scale_x
                __UpperCamelCase     :Dict			  =		scale_y + bbox[4] * (1 - scale_y)
                new_anno.append([bbox[0], xmin, ymin, xmax, ymax]	)
        else:  # bottom-right
            __UpperCamelCase     :Optional[int]			  =		cva.resize(
                SCREAMING_SNAKE_CASE  ,			(output_size[1] - divid_point_x, output_size[0] - divid_point_y)	)
            __UpperCamelCase     :Optional[int]			  =		img
            for bbox in img_annos:
                __UpperCamelCase     :Optional[Any]			  =		scale_x + bbox[1] * (1 - scale_x)
                __UpperCamelCase     :Optional[int]			  =		scale_y + bbox[2] * (1 - scale_y)
                __UpperCamelCase     :Optional[Any]			  =		scale_x + bbox[3] * (1 - scale_x)
                __UpperCamelCase     :int			  =		scale_y + bbox[4] * (1 - scale_y)
                new_anno.append([bbox[0], xmin, ymin, xmax, ymax]	)

    # Remove bounding box small than scale of filter
    if filter_scale > 0:
        __UpperCamelCase     :List[Any]			  =		[
            anno
            for anno in new_anno
            if filter_scale < (anno[3] - anno[1]) and filter_scale < (anno[4] - anno[2])
        ]

    return output_img, new_anno, path_list[0]



def lowerCamelCase  (   SCREAMING_SNAKE_CASE	):
    '''simple docstring'''







    assert number_char > 1, "The number of character should greater than 1"
    __UpperCamelCase     :Optional[Any]			  =		ascii_lowercase + digits
    return "".join(random.choice(SCREAMING_SNAKE_CASE	) for _ in range(SCREAMING_SNAKE_CASE	)	)


if __name__ == "__main__":
    main()
    print('''DONE ✅''')





