





from __future__ import annotations

import unittest

from transformers import BlenderbotConfig, BlenderbotTokenizer, is_tf_available
from transformers.testing_utils import require_tf, require_tokenizers, slow
from transformers.utils import cached_property

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
  import tensorflow as tf

  from transformers import TFAutoModelForSeqaSeqLM, TFBlenderbotForConditionalGeneration, TFBlenderbotModel






@require_tf
class     lowercase__       :

   '''simple docstring'''


   a						:							Optional[int]   		=		BlenderbotConfig
   a						:							List[Any]   		=		{}
   a						:							int   		=		"gelu"




   def __init__(    self,		__magic_name__,		__magic_name__=13,		__magic_name__=7,		__magic_name__=True,		__magic_name__=False,		__magic_name__=99,		__magic_name__=32,		__magic_name__=2,		__magic_name__=4,		__magic_name__=37,		__magic_name__=0.1,		__magic_name__=0.1,		__magic_name__=20,		__magic_name__=2,		__magic_name__=1,		__magic_name__=0,		) ->      List[Any]:






       """simple docstring"""
       UpperCamelCase__							:				Dict          =      parent
       UpperCamelCase__							:				Dict          =      batch_size
       UpperCamelCase__							:				Any          =      seq_length
       UpperCamelCase__							:				Union[str, Any]          =      is_training
       UpperCamelCase__							:				List[str]          =      use_labels
       UpperCamelCase__							:				List[str]          =      vocab_size
       UpperCamelCase__							:				Optional[Any]          =      hidden_size
       UpperCamelCase__							:				List[Any]          =      num_hidden_layers
       UpperCamelCase__							:				Union[str, Any]          =      num_attention_heads
       UpperCamelCase__							:				List[Any]          =      intermediate_size

       UpperCamelCase__							:				Dict          =      hidden_dropout_prob
       UpperCamelCase__							:				int          =      attention_probs_dropout_prob
       UpperCamelCase__							:				int          =      max_position_embeddings
       UpperCamelCase__							:				Union[str, Any]          =      eos_token_id
       UpperCamelCase__							:				List[Any]          =      pad_token_id
       UpperCamelCase__							:				Dict          =      bos_token_id




   def       UpperCamelCase__  (    self							) ->      Dict:






       """simple docstring"""
       UpperCamelCase__							:				Dict          =      ids_tensor([self.batch_size, self.seq_length - 1],		self.vocab_size							)
       UpperCamelCase__							:				Optional[int]          =      tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size							),		1							)
       UpperCamelCase__							:				Any          =      tf.concat([input_ids, eos_tensor],		axis=1							)

       UpperCamelCase__							:				int          =      ids_tensor([self.batch_size, self.seq_length],		self.vocab_size							)

       UpperCamelCase__							:				List[str]          =      self.config_cls(
           vocab_size=self.vocab_size,		d_model=self.hidden_size,		encoder_layers=self.num_hidden_layers,		decoder_layers=self.num_hidden_layers,		encoder_attention_heads=self.num_attention_heads,		decoder_attention_heads=self.num_attention_heads,		encoder_ffn_dim=self.intermediate_size,		decoder_ffn_dim=self.intermediate_size,		dropout=self.hidden_dropout_prob,		attention_dropout=self.attention_probs_dropout_prob,		max_position_embeddings=self.max_position_embeddings,		eos_token_ids=[2],		bos_token_id=self.bos_token_id,		pad_token_id=self.pad_token_id,		decoder_start_token_id=self.pad_token_id,		**self.config_updates,		)
       UpperCamelCase__							:				Optional[Any]          =      prepare_blenderbot_inputs_dict(__magic_name__,		__magic_name__,		__magic_name__							)
       return config, inputs_dict




   def       UpperCamelCase__  (    self,		__magic_name__,		__magic_name__							) ->      Union[str, Any]:






       """simple docstring"""
       UpperCamelCase__							:				int          =      TFBlenderbotModel(config=__magic_name__							).get_decoder()
       UpperCamelCase__							:				Any          =      inputs_dict['''input_ids''']

       UpperCamelCase__							:				List[Any]          =      input_ids[:1, :]
       UpperCamelCase__							:				Optional[int]          =      inputs_dict['''attention_mask'''][:1, :]
       UpperCamelCase__							:				Union[str, Any]          =      inputs_dict['''head_mask''']
       UpperCamelCase__							:				Any          =      1

       # first forward pass
       UpperCamelCase__							:				str          =      model(__magic_name__,		attention_mask=__magic_name__,		head_mask=__magic_name__,		use_cache=__magic_name__							)

       UpperCamelCase__   ,UpperCamelCase__							:				Optional[int]          =      outputs.to_tuple()

       # create hypothetical next token and extent to next_input_ids
       UpperCamelCase__							:				Dict          =      ids_tensor((self.batch_size, 3),		config.vocab_size							)
       UpperCamelCase__							:				str          =      tf.cast(ids_tensor((self.batch_size, 3),		2							),		tf.inta							)

       # append to next input_ids and
       UpperCamelCase__							:				Tuple          =      tf.concat([input_ids, next_tokens],		axis=-1							)
       UpperCamelCase__							:				Any          =      tf.concat([attention_mask, next_attn_mask],		axis=-1							)

       UpperCamelCase__							:				Optional[int]          =      model(__magic_name__,		attention_mask=__magic_name__							)[0]
       UpperCamelCase__							:				int          =      model(__magic_name__,		attention_mask=__magic_name__,		past_key_values=__magic_name__							)[0]

       self.parent.assertEqual(next_tokens.shape[1],		output_from_past.shape[1]							)

       # select random slice
       UpperCamelCase__							:				Optional[int]          =      int(ids_tensor((1,),		output_from_past.shape[-1]							)							)
       UpperCamelCase__							:				Optional[int]          =      output_from_no_past[:, -3:, random_slice_idx]
       UpperCamelCase__							:				List[Any]          =      output_from_past[:, :, random_slice_idx]

       # test that outputs are equal for slice
       tf.debugging.assert_near(__magic_name__,		__magic_name__,		rtol=1E-3							)




def 		lowerCAmelCase_    (  __UpperCAmelCase:			str		,   __UpperCAmelCase:			List[str]		,   __UpperCAmelCase:			str		,   __UpperCAmelCase:			Any=None		,   __UpperCAmelCase:			Dict=None		,   __UpperCAmelCase:			int=None		,   __UpperCAmelCase:			List[str]=None		,   __UpperCAmelCase:			List[Any]=None		,   )      ->	Union[str, Any]:
    if attention_mask is None:
        UpperCamelCase__							:				List[Any]          =      tf.cast(tf.math.not_equal(__UpperCAmelCase		,   config.pad_token_id      )		,   tf.inta      )
    if decoder_attention_mask is None:
        UpperCamelCase__							:				Optional[Any]          =      tf.concat(
            [
                tf.ones(decoder_input_ids[:, :1].shape		,   dtype=tf.inta      ),
                tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:]		,   config.pad_token_id      )		,   tf.inta      ),
            ]		,   axis=-1		,   )
    if head_mask is None:
        UpperCamelCase__							:				Union[str, Any]          =      tf.ones((config.encoder_layers, config.encoder_attention_heads)      )
    if decoder_head_mask is None:
        UpperCamelCase__							:				Dict          =      tf.ones((config.decoder_layers, config.decoder_attention_heads)      )
    if cross_attn_head_mask is None:
        UpperCamelCase__							:				Union[str, Any]          =      tf.ones((config.decoder_layers, config.decoder_attention_heads)      )
    return {
        "input_ids": input_ids,
        "decoder_input_ids": decoder_input_ids,
        "attention_mask": attention_mask,
        "decoder_attention_mask": decoder_attention_mask,
        "head_mask": head_mask,
        "decoder_head_mask": decoder_head_mask,
        "cross_attn_head_mask": cross_attn_head_mask,
    }






@require_tf
class     lowercase__       (	__lowerCamelCase			,				__lowerCamelCase			,				unittest.TestCase				):

   '''simple docstring'''


   a						:							Optional[int]   		=		(TFBlenderbotForConditionalGeneration, TFBlenderbotModel) if is_tf_available() else ()
   a						:							Union[str, Any]   		=		(TFBlenderbotForConditionalGeneration,) if is_tf_available() else ()
   a						:							Union[str, Any]   		=		(
       {
           "conversational": TFBlenderbotForConditionalGeneration,
           "feature-extraction": TFBlenderbotModel,
           "summarization": TFBlenderbotForConditionalGeneration,
           "text2text-generation": TFBlenderbotForConditionalGeneration,
           "translation": TFBlenderbotForConditionalGeneration,
       }
       if is_tf_available()
       else {}
   )
   a						:							Optional[int]   		=		True
   a						:							str   		=		False
   a						:							Optional[int]   		=		False




   def       UpperCamelCase__  (    self							) ->      Dict:






       """simple docstring"""
       UpperCamelCase__							:				str          =      TFBlenderbotModelTester(self							)
       UpperCamelCase__							:				Dict          =      ConfigTester(self,		config_class=__magic_name__							)




   def       UpperCamelCase__  (    self							) ->      int:






       """simple docstring"""
       self.config_tester.run_common_tests()




   def       UpperCamelCase__  (    self							) ->      Tuple:






       """simple docstring"""
       UpperCamelCase__							:				Optional[int]          =      self.model_tester.prepare_config_and_inputs_for_common()
       self.model_tester.check_decoder_model_past_large_inputs(*__magic_name__							)






@require_tokenizers
@require_tf
class     lowercase__       (	unittest.TestCase				):

   '''simple docstring'''


   a						:							str   		=		["My friends are cool but they eat too many carbs."]
   a						:							List[Any]   		=		"facebook/blenderbot-400M-distill"




   @cached_property
   def       UpperCamelCase__  (    self							) ->      Tuple:






       """simple docstring"""
       return BlenderbotTokenizer.from_pretrained(self.model_name							)




   @cached_property
   def       UpperCamelCase__  (    self							) ->      str:






       """simple docstring"""
       UpperCamelCase__							:				List[str]          =      TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name							)
       return model




   @slow
   def       UpperCamelCase__  (    self							) ->      List[Any]:






       """simple docstring"""
       UpperCamelCase__							:				Any          =      self.tokenizer(self.src_text,		return_tensors='''tf'''							)
       UpperCamelCase__							:				Union[str, Any]          =      self.model.generate(
           model_inputs.input_ids,		)
       UpperCamelCase__							:				Tuple          =      self.tokenizer.batch_decode(generated_ids.numpy(),		skip_special_tokens=__magic_name__							)[0]
       assert (
           generated_words
           == " That's unfortunate. Are they trying to lose weight or are they just trying to be healthier?"
       )








import os
from glob import glob

import imageio
import torch
import torchvision
import wandb
from img_processing import custom_to_pil, loop_post_process, preprocess, preprocess_vqgan
from loaders import load_vqgan
from PIL import Image
from torch import nn

from transformers import CLIPModel, CLIPTokenizerFast
from utils import get_device, get_timestamp, show_pil






class     lowercase__       :

   '''simple docstring'''


   def __init__(    self,		__magic_name__ = "cpu",		__magic_name__ = "openai/clip-vit-large-patch14"							) ->      None:






       """simple docstring"""
       UpperCamelCase__							:				List[str]          =      device
       UpperCamelCase__							:				Union[str, Any]          =      CLIPTokenizerFast.from_pretrained(__magic_name__							)
       UpperCamelCase__							:				Tuple          =      [0.4814_5466, 0.457_8275, 0.4082_1073]
       UpperCamelCase__							:				Union[str, Any]          =      [0.2686_2954, 0.2613_0258, 0.2757_7711]
       UpperCamelCase__							:				Dict          =      torchvision.transforms.Normalize(self.image_mean,		self.image_std							)
       UpperCamelCase__							:				List[str]          =      torchvision.transforms.Resize(224							)
       UpperCamelCase__							:				Union[str, Any]          =      torchvision.transforms.CenterCrop(224							)




   def       UpperCamelCase__  (    self,		__magic_name__							) ->      List[Any]:






       """simple docstring"""
       UpperCamelCase__							:				Optional[Any]          =      self.resize(__magic_name__							)
       UpperCamelCase__							:				Dict          =      self.center_crop(__magic_name__							)
       UpperCamelCase__							:				List[str]          =      self.normalize(__magic_name__							)
       return images




   def __call__(    self,		__magic_name__=None,		__magic_name__=None,		**__magic_name__							) ->      Union[str, Any]:






       """simple docstring"""
       UpperCamelCase__							:				Optional[Any]          =      self.tokenizer(text=__magic_name__,		**__magic_name__							)
       UpperCamelCase__							:				List[Any]          =      self.preprocess_img(__magic_name__							)
       UpperCamelCase__							:				Optional[Any]          =      {key: value.to(self.device							) for (key, value) in encoding.items()}
       return encoding






class     lowercase__       (	nn.Module				):

   '''simple docstring'''


   def __init__(    self,		__magic_name__=10,		__magic_name__=0.01,		__magic_name__=None,		__magic_name__=None,		__magic_name__=None,		__magic_name__=None,		__magic_name__=None,		__magic_name__=None,		__magic_name__=False,		__magic_name__=True,		__magic_name__="image",		__magic_name__=True,		__magic_name__=False,		__magic_name__=False,		__magic_name__=False,		) ->      None:






       """simple docstring"""
       super().__init__()
       UpperCamelCase__							:				Dict          =      None
       UpperCamelCase__							:				Tuple          =      device if device else get_device()
       if vqgan:
           UpperCamelCase__							:				Union[str, Any]          =      vqgan
       else:
           UpperCamelCase__							:				Any          =      load_vqgan(self.device,		conf_path=__magic_name__,		ckpt_path=__magic_name__							)
       self.vqgan.eval()
       if clip:
           UpperCamelCase__							:				Optional[Any]          =      clip
       else:
           UpperCamelCase__							:				Any          =      CLIPModel.from_pretrained('''openai/clip-vit-base-patch32'''							)
       self.clip.to(self.device							)
       UpperCamelCase__							:				str          =      ProcessorGradientFlow(device=self.device							)

       UpperCamelCase__							:				Union[str, Any]          =      iterations
       UpperCamelCase__							:				Tuple          =      lr
       UpperCamelCase__							:				Optional[int]          =      log
       UpperCamelCase__							:				List[Any]          =      make_grid
       UpperCamelCase__							:				Optional[Any]          =      return_val
       UpperCamelCase__							:				str          =      quantize
       UpperCamelCase__							:				int          =      self.vqgan.decoder.z_shape




   def       UpperCamelCase__  (    self,		__magic_name__=None,		__magic_name__=None,		__magic_name__=5,		__magic_name__=True							) ->      Optional[Any]:






       """simple docstring"""
       UpperCamelCase__							:				Optional[int]          =      []
       if output_path is None:
           UpperCamelCase__							:				List[str]          =      '''./animation.gif'''
       if input_path is None:
           UpperCamelCase__							:				Union[str, Any]          =      self.save_path
       UpperCamelCase__							:				Tuple          =      sorted(glob(input_path + '''/*'''							)							)
       if not len(__magic_name__							):
           raise ValueError(
               '''No images found in save path, aborting (did you pass save_intermediate=True to the generate'''
               ''' function?)'''							)
       if len(__magic_name__							) == 1:
           print('''Only one image found in save path, (did you pass save_intermediate=True to the generate function?)'''							)
       UpperCamelCase__							:				Dict          =      total_duration / len(__magic_name__							)
       UpperCamelCase__							:				List[Any]          =      [frame_duration] * len(__magic_name__							)
       if extend_frames:
           UpperCamelCase__							:				List[Any]          =      1.5
           UpperCamelCase__							:				Any          =      3
       for file_name in paths:
           if file_name.endswith('''.png'''							):
               images.append(imageio.imread(__magic_name__							)							)
       imageio.mimsave(__magic_name__,		__magic_name__,		duration=__magic_name__							)
       print(f"gif saved to {output_path}"							)




   def       UpperCamelCase__  (    self,		__magic_name__=None,		__magic_name__=None							) ->      Any:






       """simple docstring"""
       if not (path or img):
           raise ValueError('''Input either path or tensor'''							)
       if img is not None:
           raise NotImplementedError
       UpperCamelCase__							:				List[Any]          =      preprocess(Image.open(__magic_name__							),		target_image_size=256							).to(self.device							)
       UpperCamelCase__							:				str          =      preprocess_vqgan(__magic_name__							)
       UpperCamelCase__   ,*UpperCamelCase__							:				Union[str, Any]          =      self.vqgan.encode(__magic_name__							)
       return z




   def       UpperCamelCase__  (    self,		__magic_name__							) ->      Any:






       """simple docstring"""
       UpperCamelCase__							:				Optional[Any]          =      self.latent.detach().requires_grad_()
       UpperCamelCase__							:				Any          =      base_latent + transform_vector
       if self.quantize:
           UpperCamelCase__   ,*UpperCamelCase__							:				int          =      self.vqgan.quantize(__magic_name__							)
       else:
           UpperCamelCase__							:				Optional[int]          =      trans_latent
       return self.vqgan.decode(__magic_name__							)




   def       UpperCamelCase__  (    self,		__magic_name__,		__magic_name__,		__magic_name__=None							) ->      Tuple:






       """simple docstring"""
       UpperCamelCase__							:				Optional[int]          =      self.clip_preprocessor(text=__magic_name__,		images=__magic_name__,		return_tensors='''pt''',		padding=__magic_name__							)
       UpperCamelCase__							:				Optional[int]          =      self.clip(**__magic_name__							)
       UpperCamelCase__							:				Tuple          =      clip_outputs.logits_per_image
       if weights is not None:
           UpperCamelCase__							:				List[Any]          =      similarity_logits * weights
       return similarity_logits.sum()




   def       UpperCamelCase__  (    self,		__magic_name__,		__magic_name__,		__magic_name__							) ->      Any:






       """simple docstring"""
       UpperCamelCase__							:				List[str]          =      self._get_clip_similarity(pos_prompts['''prompts'''],		__magic_name__,		weights=(1 / pos_prompts['''weights'''])							)
       if neg_prompts:
           UpperCamelCase__							:				Tuple          =      self._get_clip_similarity(neg_prompts['''prompts'''],		__magic_name__,		weights=neg_prompts['''weights''']							)
       else:
           UpperCamelCase__							:				Optional[int]          =      torch.tensor([1],		device=self.device							)
       UpperCamelCase__							:				Tuple          =      -torch.log(__magic_name__							) + torch.log(__magic_name__							)
       return loss




   def       UpperCamelCase__  (    self,		__magic_name__,		__magic_name__,		__magic_name__							) ->      Optional[Any]:






       """simple docstring"""
       UpperCamelCase__							:				List[str]          =      torch.randn_like(self.latent,		requires_grad=__magic_name__,		device=self.device							)
       UpperCamelCase__							:				Optional[int]          =      torch.optim.Adam([vector],		lr=self.lr							)

       for i in range(self.iterations							):
           optim.zero_grad()
           UpperCamelCase__							:				Tuple          =      self._add_vector(__magic_name__							)
           UpperCamelCase__							:				Any          =      loop_post_process(__magic_name__							)
           UpperCamelCase__							:				Union[str, Any]          =      self._get_CLIP_loss(__magic_name__,		__magic_name__,		__magic_name__							)
           print('''CLIP loss''',		__magic_name__							)
           if self.log:
               wandb.log({'''CLIP Loss''': clip_loss}							)
           clip_loss.backward(retain_graph=__magic_name__							)
           optim.step()
           if self.return_val == "image":
               yield custom_to_pil(transformed_img[0]							)
           else:
               yield vector




   def       UpperCamelCase__  (    self,		__magic_name__,		__magic_name__,		__magic_name__							) ->      List[str]:






       """simple docstring"""
       wandb.init(reinit=__magic_name__,		project='''face-editor'''							)
       wandb.config.update({'''Positive Prompts''': positive_prompts}							)
       wandb.config.update({'''Negative Prompts''': negative_prompts}							)
       wandb.config.update({'''lr''': self.lr, '''iterations''': self.iterations}							)
       if image_path:
           UpperCamelCase__							:				List[str]          =      Image.open(__magic_name__							)
           UpperCamelCase__							:				List[Any]          =      image.resize((256, 256)							)
           wandb.log('''Original Image''',		wandb.Image(__magic_name__							)							)




   def       UpperCamelCase__  (    self,		__magic_name__							) ->      Optional[int]:






       """simple docstring"""
       if not prompts:
           return []
       UpperCamelCase__							:				int          =      []
       UpperCamelCase__							:				str          =      []
       if isinstance(__magic_name__,		__magic_name__							):
           UpperCamelCase__							:				Optional[Any]          =      [prompt.strip() for prompt in prompts.split('''|'''							)]
       for prompt in prompts:
           if isinstance(__magic_name__,		(tuple, list)							):
               UpperCamelCase__							:				Optional[int]          =      prompt[0]
               UpperCamelCase__							:				Dict          =      float(prompt[1]							)
           elif ":" in prompt:
               UpperCamelCase__   ,UpperCamelCase__							:				Optional[int]          =      prompt.split(''':'''							)
               UpperCamelCase__							:				List[Any]          =      float(__magic_name__							)
           else:
               UpperCamelCase__							:				List[str]          =      prompt
               UpperCamelCase__							:				Any          =      1.0
           processed_prompts.append(__magic_name__							)
           weights.append(__magic_name__							)
       return {
           "prompts": processed_prompts,
           "weights": torch.tensor(__magic_name__,		device=self.device							),
       }




   def       UpperCamelCase__  (    self,		__magic_name__,		__magic_name__=None,		__magic_name__=None,		__magic_name__=True,		__magic_name__=False,		__magic_name__=True,		__magic_name__=True,		__magic_name__=None,		) ->      str:






       """simple docstring"""
       if image_path:
           UpperCamelCase__							:				Union[str, Any]          =      self._get_latent(__magic_name__							)
       else:
           UpperCamelCase__							:				Dict          =      torch.randn(self.latent_dim,		device=self.device							)
       if self.log:
           self._init_logging(__magic_name__,		__magic_name__,		__magic_name__							)

       assert pos_prompts, "You must provide at least one positive prompt."
       UpperCamelCase__							:				Optional[Any]          =      self.process_prompts(__magic_name__							)
       UpperCamelCase__							:				Union[str, Any]          =      self.process_prompts(__magic_name__							)
       if save_final and save_path is None:
           UpperCamelCase__							:				str          =      os.path.join('''./outputs/''',		'''_'''.join(pos_prompts['''prompts''']							)							)
       if not os.path.exists(__magic_name__							):
           os.makedirs(__magic_name__							)
       else:
           UpperCamelCase__							:				int          =      save_path + '''_''' + get_timestamp()
           os.makedirs(__magic_name__							)
       UpperCamelCase__							:				Optional[Any]          =      save_path

       UpperCamelCase__							:				str          =      self.vqgan.decode(self.latent							)[0]
       if show_intermediate:
           print('''Original Image'''							)
           show_pil(custom_to_pil(__magic_name__							)							)

       UpperCamelCase__							:				Optional[Any]          =      loop_post_process(__magic_name__							)
       for iter, transformed_img in enumerate(self._optimize_CLIP(__magic_name__,		__magic_name__,		__magic_name__							)							):
           if show_intermediate:
               show_pil(__magic_name__							)
           if save_intermediate:
               transformed_img.save(os.path.join(self.save_path,		f"iter_{iter:03d}.png"							)							)
           if self.log:
               wandb.log({'''Image''': wandb.Image(__magic_name__							)}							)
       if show_final:
           show_pil(__magic_name__							)
       if save_final:
           transformed_img.save(os.path.join(self.save_path,		f"iter_{iter:03d}_final.png"							)							)

