





'''simple docstring'''

import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import BlenderbotConfig, is_flax_available
from transformers.testing_utils import jax_device, require_flax, slow

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor


if is_flax_available():
   import os

   # The slow tests are often failing with OOM error on GPU
   # This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
   # but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
   lowercase__		:     Any			=							'platform'

   import jax
   import jax.numpy as jnp

   from transformers import BlenderbotTokenizer
   from transformers.models.blenderbot.modeling_flax_blenderbot import (
       FlaxBlenderbotForConditionalGeneration,
       FlaxBlenderbotModel,
       shift_tokens_right,
   )







def    a__							(							lowercase   :	Tuple,   lowercase   :	Optional[int],   lowercase   :	Any=None,   lowercase   :	Optional[Any]=None,   lowercase   :	Dict=None,   lowercase   :	List[str]=None,   lowercase   :	Any=None,   lowercase   :	Tuple=None,   )						->						Tuple:





     """simple docstring"""

     if attention_mask is None:
          _UpperCamelCase							       =			np.where(input_ids != config.pad_token_id,   1,   0 )
     if decoder_attention_mask is None:
          _UpperCamelCase							       =			np.where(decoder_input_ids != config.pad_token_id,   1,   0 )
     if head_mask is None:
          _UpperCamelCase							       =			np.ones((config.encoder_layers, config.encoder_attention_heads) )
     if decoder_head_mask is None:
          _UpperCamelCase							       =			np.ones((config.decoder_layers, config.decoder_attention_heads) )
     if cross_attn_head_mask is None:
          _UpperCamelCase							       =			np.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": attention_mask,
     }
class 							__lowerCAmelCase	:






       """simple docstring"""





       def __init__(				self  :						int			,					lowerCAmelCase__  :						Optional[Any]			,					lowerCAmelCase__  :						List[str]=13			,					lowerCAmelCase__  :						str=7			,					lowerCAmelCase__  :						Optional[Any]=True			,					lowerCAmelCase__  :						Tuple=False			,					lowerCAmelCase__  :						Tuple=99			,					lowerCAmelCase__  :						Dict=16			,					lowerCAmelCase__  :						List[str]=2			,					lowerCAmelCase__  :						int=4			,					lowerCAmelCase__  :						Tuple=4			,					lowerCAmelCase__  :						Dict="gelu"			,					lowerCAmelCase__  :						Optional[Any]=0.1			,					lowerCAmelCase__  :						Union[str, Any]=0.1			,					lowerCAmelCase__  :						Dict=32			,					lowerCAmelCase__  :						str=2			,					lowerCAmelCase__  :						Tuple=1			,					lowerCAmelCase__  :						Tuple=0			,					lowerCAmelCase__  :						List[Any]=0.02			,					)       ->	List[str]:

            '''simple docstring'''




            _UpperCamelCase							       =			parent
            _UpperCamelCase							       =			batch_size
            _UpperCamelCase							       =			seq_length
            _UpperCamelCase							       =			is_training
            _UpperCamelCase							       =			use_labels
            _UpperCamelCase							       =			vocab_size
            _UpperCamelCase							       =			hidden_size
            _UpperCamelCase							       =			num_hidden_layers
            _UpperCamelCase							       =			num_attention_heads
            _UpperCamelCase							       =			intermediate_size
            _UpperCamelCase							       =			hidden_act
            _UpperCamelCase							       =			hidden_dropout_prob
            _UpperCamelCase							       =			attention_probs_dropout_prob
            _UpperCamelCase							       =			max_position_embeddings
            _UpperCamelCase							       =			eos_token_id
            _UpperCamelCase							       =			pad_token_id
            _UpperCamelCase							       =			bos_token_id
            _UpperCamelCase							       =			initializer_range





       def snake_case__		(				self  :						List[str]	)       ->	List[str]:

            '''simple docstring'''




            _UpperCamelCase							       =			np.clip(ids_tensor([self.batch_size, self.seq_length - 1]			,					self.vocab_size	)			,					3			,					self.vocab_size	)
            _UpperCamelCase							       =			np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)			,					dtype=np.intaa	))			,					-1	)

            _UpperCamelCase							       =			shift_tokens_right(lowerCAmelCase__			,					1			,					2	)

            _UpperCamelCase							       =			BlenderbotConfig(
                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_id=self.eos_token_id			,					bos_token_id=self.bos_token_id			,					pad_token_id=self.pad_token_id			,					initializer_range=self.initializer_range			,					use_cache=lowerCAmelCase__			,					)
            _UpperCamelCase							       =			prepare_blenderbot_inputs_dict(lowerCAmelCase__			,					lowerCAmelCase__			,					lowerCAmelCase__	)
            return config, inputs_dict





       def snake_case__		(				self  :						int	)       ->	Union[str, Any]:

            '''simple docstring'''




            _UpperCamelCase       ,							_UpperCamelCase							       =			self.prepare_config_and_inputs()
            return config, inputs_dict





       def snake_case__		(				self  :						str			,					lowerCAmelCase__  :						List[str]			,					lowerCAmelCase__  :						Tuple			,					lowerCAmelCase__  :						Tuple	)       ->	Tuple:

            '''simple docstring'''




            _UpperCamelCase							       =			20
            _UpperCamelCase							       =			model_class_name(lowerCAmelCase__	)

            _UpperCamelCase							       =			model.encode(inputs_dict['''input_ids''']	)

            _UpperCamelCase       ,							_UpperCamelCase							       =			(
                inputs_dict['''decoder_input_ids'''],
                inputs_dict['''decoder_attention_mask'''],
            )

            _UpperCamelCase							       =			model.init_cache(decoder_input_ids.shape[0]			,					lowerCAmelCase__			,					lowerCAmelCase__	)
            _UpperCamelCase							       =			jnp.ones((decoder_input_ids.shape[0], max_decoder_length)			,					dtype='''i4'''	)

            _UpperCamelCase							       =			jnp.broadcast_to(
                jnp.arange(decoder_input_ids.shape[-1] - 1	)[None, :]			,					(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)			,					)
            _UpperCamelCase							       =			model.decode(
                decoder_input_ids[:, :-1]			,					lowerCAmelCase__			,					decoder_attention_mask=lowerCAmelCase__			,					past_key_values=lowerCAmelCase__			,					decoder_position_ids=lowerCAmelCase__			,					)

            _UpperCamelCase							       =			jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]			,					dtype='''i4'''	)
            _UpperCamelCase							       =			model.decode(
                decoder_input_ids[:, -1:]			,					lowerCAmelCase__			,					decoder_attention_mask=lowerCAmelCase__			,					past_key_values=outputs_cache.past_key_values			,					decoder_position_ids=lowerCAmelCase__			,					)

            _UpperCamelCase							       =			model.decode(lowerCAmelCase__			,					lowerCAmelCase__	)

            _UpperCamelCase							       =			np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])	)	)
            self.parent.assertTrue(diff < 1e-3			,					msg=f"""Max diff is {diff}"""	)







       def snake_case__		(				self  :						int			,					lowerCAmelCase__  :						Optional[Any]			,					lowerCAmelCase__  :						Optional[int]			,					lowerCAmelCase__  :						Optional[int]	)       ->	Optional[Any]:

            '''simple docstring'''




            _UpperCamelCase							       =			20
            _UpperCamelCase							       =			model_class_name(lowerCAmelCase__	)

            _UpperCamelCase							       =			model.encode(inputs_dict['''input_ids''']	)

            _UpperCamelCase       ,							_UpperCamelCase							       =			(
                inputs_dict['''decoder_input_ids'''],
                inputs_dict['''decoder_attention_mask'''],
            )

            _UpperCamelCase							       =			jnp.concatenate(
                [
                    decoder_attention_mask,
                    jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])	),
                ]			,					axis=-1			,					)

            _UpperCamelCase							       =			model.init_cache(decoder_input_ids.shape[0]			,					lowerCAmelCase__			,					lowerCAmelCase__	)
            _UpperCamelCase							       =			jnp.broadcast_to(
                jnp.arange(decoder_input_ids.shape[-1] - 1	)[None, :]			,					(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)			,					)

            _UpperCamelCase							       =			model.decode(
                decoder_input_ids[:, :-1]			,					lowerCAmelCase__			,					decoder_attention_mask=lowerCAmelCase__			,					past_key_values=lowerCAmelCase__			,					decoder_position_ids=lowerCAmelCase__			,					)
            _UpperCamelCase							       =			jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]			,					dtype='''i4'''	)
            _UpperCamelCase							       =			model.decode(
                decoder_input_ids[:, -1:]			,					lowerCAmelCase__			,					past_key_values=outputs_cache.past_key_values			,					decoder_attention_mask=lowerCAmelCase__			,					decoder_position_ids=lowerCAmelCase__			,					)

            _UpperCamelCase							       =			model.decode(lowerCAmelCase__			,					lowerCAmelCase__			,					decoder_attention_mask=lowerCAmelCase__	)

            _UpperCamelCase							       =			np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])	)	)
            self.parent.assertTrue(diff < 1e-3			,					msg=f"""Max diff is {diff}"""	)

@require_flax
class 							__lowerCAmelCase	(       unittest.TestCase					):






       """simple docstring"""
       _snake_case      :	List[Any]									=       9_9





       def snake_case__		(				self  :						Dict	)       ->	List[Any]:

            '''simple docstring'''




            _UpperCamelCase							       =			np.array(
                [
                    [71, 82, 18, 33, 46, 91, 2],
                    [68, 34, 26, 58, 30, 82, 2],
                    [5, 97, 17, 39, 94, 40, 2],
                    [76, 83, 94, 25, 70, 78, 2],
                    [87, 59, 41, 35, 48, 66, 2],
                    [55, 13, 16, 58, 5, 2, 1],  # note padding
                    [64, 27, 31, 51, 12, 75, 2],
                    [52, 64, 86, 17, 83, 39, 2],
                    [48, 61, 9, 24, 71, 82, 2],
                    [26, 1, 60, 48, 22, 13, 2],
                    [21, 5, 62, 28, 14, 76, 2],
                    [45, 98, 37, 86, 59, 48, 2],
                    [70, 70, 50, 9, 28, 0, 2],
                ]			,					dtype=np.intaa			,					)

            _UpperCamelCase							       =			input_ids.shape[0]
            _UpperCamelCase							       =			BlenderbotConfig(
                vocab_size=self.vocab_size			,					d_model=24			,					encoder_layers=2			,					decoder_layers=2			,					encoder_attention_heads=2			,					decoder_attention_heads=2			,					encoder_ffn_dim=32			,					decoder_ffn_dim=32			,					max_position_embeddings=48			,					eos_token_id=2			,					pad_token_id=1			,					bos_token_id=0			,					)
            return config, input_ids, batch_size





       def snake_case__		(				self  :						Dict	)       ->	List[str]:

            '''simple docstring'''




            _UpperCamelCase       ,							_UpperCamelCase       ,							_UpperCamelCase							       =			self._get_config_and_data()
            _UpperCamelCase							       =			FlaxBlenderbotForConditionalGeneration(lowerCAmelCase__	)
            _UpperCamelCase							       =			lm_model(input_ids=lowerCAmelCase__	)
            _UpperCamelCase							       =			(batch_size, input_ids.shape[1], config.vocab_size)
            self.assertEqual(outputs['''logits'''].shape			,					lowerCAmelCase__	)





       def snake_case__		(				self  :						Any	)       ->	List[str]:

            '''simple docstring'''




            _UpperCamelCase							       =			BlenderbotConfig(
                vocab_size=self.vocab_size			,					d_model=14			,					encoder_layers=2			,					decoder_layers=2			,					encoder_attention_heads=2			,					decoder_attention_heads=2			,					encoder_ffn_dim=8			,					decoder_ffn_dim=8			,					max_position_embeddings=48			,					)
            _UpperCamelCase							       =			FlaxBlenderbotForConditionalGeneration(lowerCAmelCase__	)
            _UpperCamelCase							       =			np.array([[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]]			,					dtype=np.intaa	)
            _UpperCamelCase							       =			np.array([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]]			,					dtype=np.intaa	)
            _UpperCamelCase							       =			lm_model(input_ids=lowerCAmelCase__			,					decoder_input_ids=lowerCAmelCase__	)
            _UpperCamelCase							       =			(*summary.shape, config.vocab_size)
            self.assertEqual(outputs['''logits'''].shape			,					lowerCAmelCase__	)







       def snake_case__		(				self  :						List[str]	)       ->	Dict:

            '''simple docstring'''




            _UpperCamelCase							       =			np.array([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]]			,					dtype=np.intaa	)
            _UpperCamelCase							       =			shift_tokens_right(lowerCAmelCase__			,					1			,					2	)
            _UpperCamelCase							       =			np.equal(lowerCAmelCase__			,					1	).astype(np.floataa	).sum()
            _UpperCamelCase							       =			np.equal(lowerCAmelCase__			,					1	).astype(np.floataa	).sum()
            self.assertEqual(shifted.shape			,					input_ids.shape	)
            self.assertEqual(lowerCAmelCase__			,					n_pad_before - 1	)
            self.assertTrue(np.equal(shifted[:, 0]			,					2	).all()	)

@require_flax
class 							__lowerCAmelCase	(       __magic_name__		,		unittest.TestCase		,		__magic_name__					):






       """simple docstring"""
       _snake_case      :	Dict									=       True
       _snake_case      :	str									=       (
           (
               FlaxBlenderbotModel,
               FlaxBlenderbotForConditionalGeneration,
           )
           if is_flax_available()
           else ()
       )
       _snake_case      :	Tuple									=       (FlaxBlenderbotForConditionalGeneration,) if is_flax_available() else ()





       def snake_case__		(				self  :						Union[str, Any]	)       ->	List[Any]:

            '''simple docstring'''




            _UpperCamelCase							       =			FlaxBlenderbotModelTester(self	)





       def snake_case__		(				self  :						List[str]	)       ->	Union[str, Any]:

            '''simple docstring'''




            _UpperCamelCase       ,							_UpperCamelCase							       =			self.model_tester.prepare_config_and_inputs()
            for model_class in self.all_model_classes:
                 self.model_tester.check_use_cache_forward(lowerCAmelCase__			,					lowerCAmelCase__			,					lowerCAmelCase__	)





       def snake_case__		(				self  :						Union[str, Any]	)       ->	Tuple:

            '''simple docstring'''




            _UpperCamelCase       ,							_UpperCamelCase							       =			self.model_tester.prepare_config_and_inputs()
            for model_class in self.all_model_classes:
                 self.model_tester.check_use_cache_forward_with_attn_mask(lowerCAmelCase__			,					lowerCAmelCase__			,					lowerCAmelCase__	)





       def snake_case__		(				self  :						Union[str, Any]	)       ->	Optional[Any]:

            '''simple docstring'''




            _UpperCamelCase       ,							_UpperCamelCase							       =			self.model_tester.prepare_config_and_inputs_for_common()

            for model_class in self.all_model_classes:
                 with self.subTest(model_class.__name__	):
                      _UpperCamelCase							       =			self._prepare_for_class(lowerCAmelCase__			,					lowerCAmelCase__	)
                      _UpperCamelCase							       =			model_class(lowerCAmelCase__	)

                      @jax.jit
                      def encode_jitted(lowerCAmelCase__  :						Union[str, Any]			,					lowerCAmelCase__  :						Dict=None			,					**lowerCAmelCase__  :						Optional[Any]	):
                           return model.encode(input_ids=lowerCAmelCase__			,					attention_mask=lowerCAmelCase__	)

                      with self.subTest('''JIT Enabled'''	):
                           _UpperCamelCase							       =			encode_jitted(**lowerCAmelCase__	).to_tuple()

                      with self.subTest('''JIT Disabled'''	):
                           with jax.disable_jit():
                                _UpperCamelCase							       =			encode_jitted(**lowerCAmelCase__	).to_tuple()

                      self.assertEqual(len(lowerCAmelCase__	)			,					len(lowerCAmelCase__	)	)
                      for jitted_output, output in zip(lowerCAmelCase__			,					lowerCAmelCase__	):
                           self.assertEqual(jitted_output.shape			,					output.shape	)





       def snake_case__		(				self  :						Dict	)       ->	str:

            '''simple docstring'''




            _UpperCamelCase       ,							_UpperCamelCase							       =			self.model_tester.prepare_config_and_inputs_for_common()

            for model_class in self.all_model_classes:
                 with self.subTest(model_class.__name__	):
                      _UpperCamelCase							       =			model_class(lowerCAmelCase__	)
                      _UpperCamelCase							       =			model.encode(inputs_dict['''input_ids''']			,					inputs_dict['''attention_mask''']	)

                      _UpperCamelCase							       =			{
                          '''decoder_input_ids''': inputs_dict['''decoder_input_ids'''],
                          '''decoder_attention_mask''': inputs_dict['''decoder_attention_mask'''],
                          '''encoder_outputs''': encoder_outputs,
                      }

                      @jax.jit
                      def decode_jitted(lowerCAmelCase__  :						List[Any]			,					lowerCAmelCase__  :						List[Any]			,					lowerCAmelCase__  :						List[Any]	):
                           return model.decode(
                               decoder_input_ids=lowerCAmelCase__			,					decoder_attention_mask=lowerCAmelCase__			,					encoder_outputs=lowerCAmelCase__			,					)

                      with self.subTest('''JIT Enabled'''	):
                           _UpperCamelCase							       =			decode_jitted(**lowerCAmelCase__	).to_tuple()

                      with self.subTest('''JIT Disabled'''	):
                           with jax.disable_jit():
                                _UpperCamelCase							       =			decode_jitted(**lowerCAmelCase__	).to_tuple()

                      self.assertEqual(len(lowerCAmelCase__	)			,					len(lowerCAmelCase__	)	)
                      for jitted_output, output in zip(lowerCAmelCase__			,					lowerCAmelCase__	):
                           self.assertEqual(jitted_output.shape			,					output.shape	)





       @slow
       def snake_case__		(				self  :						Dict	)       ->	Any:

            '''simple docstring'''




            for model_class_name in self.all_model_classes:
                 _UpperCamelCase							       =			model_class_name.from_pretrained('''facebook/blenderbot-400M-distill'''	)
                 # FlaxBlenderbotForSequenceClassification expects eos token in input_ids
                 _UpperCamelCase							       =			np.ones((1, 1)	) * model.config.eos_token_id
                 _UpperCamelCase							       =			model(lowerCAmelCase__	)
                 self.assertIsNotNone(lowerCAmelCase__	)







       @unittest.skipUnless(jax_device != '''cpu'''			,					'''3B test too slow on CPU.'''	)
       @slow
       def snake_case__		(				self  :						int	)       ->	Tuple:

            '''simple docstring'''




            _UpperCamelCase							       =			{'''num_beams''': 1, '''early_stopping''': True, '''min_length''': 15, '''max_length''': 25}
            _UpperCamelCase							       =			{'''skip_special_tokens''': True, '''clean_up_tokenization_spaces''': True}

            _UpperCamelCase							       =			FlaxBlenderbotForConditionalGeneration.from_pretrained('''facebook/blenderbot-3B'''			,					from_pt=lowerCAmelCase__	)
            _UpperCamelCase							       =			BlenderbotTokenizer.from_pretrained('''facebook/blenderbot-3B'''	)

            _UpperCamelCase							       =			['''Sam''']
            _UpperCamelCase							       =			tokenizer(lowerCAmelCase__			,					return_tensors='''jax'''	)

            _UpperCamelCase							       =			model.generate(**lowerCAmelCase__			,					**lowerCAmelCase__	)
            _UpperCamelCase							       =			'''Sam is a great name. It means "sun" in Gaelic.'''

            _UpperCamelCase							       =			tokenizer.batch_decode(lowerCAmelCase__			,					**lowerCAmelCase__	)
            assert generated_txt[0].strip() == tgt_text





"""simple docstring"""

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   _UpperCAmelCase				:




      def __init__(				self ,							lowercase_ = "cpu" ,							lowercase_ = "openai/clip-vit-large-patch14"				)    ->       None:
          UpperCAmelCase          =       device
          UpperCAmelCase          =       CLIPTokenizerFast.from_pretrained(lowercase_				)
          UpperCAmelCase          =       [0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3]
          UpperCAmelCase          =       [0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1]
          UpperCAmelCase          =       torchvision.transforms.Normalize(self.image_mean ,							self.image_std				)
          UpperCAmelCase          =       torchvision.transforms.Resize(2_2_4				)
          UpperCAmelCase          =       torchvision.transforms.CenterCrop(2_2_4				)




      def      a_   (				self ,							lowercase_				)    ->       Any:
          UpperCAmelCase          =       self.resize(lowercase_				)
          UpperCAmelCase          =       self.center_crop(lowercase_				)
          UpperCAmelCase          =       self.normalize(lowercase_				)
          return images




      def __call__(				self ,							lowercase_=None ,							lowercase_=None ,							**lowercase_				)    ->       Union[str, Any]:
          UpperCAmelCase          =       self.tokenizer(text=lowercase_ ,							**lowercase_				)
          UpperCAmelCase          =       self.preprocess_img(lowercase_				)
          UpperCAmelCase          =       {key: value.to(self.device				) for (key, value) in encoding.items()}
          return encoding

class   _UpperCAmelCase				(		nn.Module				):




      def __init__(				self ,							lowercase_=1_0 ,							lowercase_=0.0_1 ,							lowercase_=None ,							lowercase_=None ,							lowercase_=None ,							lowercase_=None ,							lowercase_=None ,							lowercase_=None ,							lowercase_=False ,							lowercase_=True ,							lowercase_="image" ,							lowercase_=True ,							lowercase_=False ,							lowercase_=False ,							lowercase_=False ,							)    ->       None:
          super().__init__()
          UpperCAmelCase          =       None
          UpperCAmelCase          =       device if device else get_device()
          if vqgan:
              UpperCAmelCase          =       vqgan
          else:
              UpperCAmelCase          =       load_vqgan(self.device ,							conf_path=lowercase_ ,							ckpt_path=lowercase_				)
          self.vqgan.eval()
          if clip:
              UpperCAmelCase          =       clip
          else:
              UpperCAmelCase          =       CLIPModel.from_pretrained('openai/clip-vit-base-patch32'				)
          self.clip.to(self.device				)
          UpperCAmelCase          =       ProcessorGradientFlow(device=self.device				)

          UpperCAmelCase          =       iterations
          UpperCAmelCase          =       lr
          UpperCAmelCase          =       log
          UpperCAmelCase          =       make_grid
          UpperCAmelCase          =       return_val
          UpperCAmelCase          =       quantize
          UpperCAmelCase          =       self.vqgan.decoder.z_shape




      def      a_   (				self ,							lowercase_=None ,							lowercase_=None ,							lowercase_=5 ,							lowercase_=True				)    ->       Dict:
          UpperCAmelCase          =       []
          if output_path is None:
              UpperCAmelCase          =       './animation.gif'
          if input_path is None:
              UpperCAmelCase          =       self.save_path
          UpperCAmelCase          =       sorted(glob(input_path + '/*'				)				)
          if not len(lowercase_				):
              raise ValueError(
                  'No images found in save path, aborting (did you pass save_intermediate=True to the generate'
                  ' function?)'				)
          if len(lowercase_				) == 1:
              print('Only one image found in save path, (did you pass save_intermediate=True to the generate function?)'				)
          UpperCAmelCase          =       total_duration / len(lowercase_				)
          UpperCAmelCase          =       [frame_duration] * len(lowercase_				)
          if extend_frames:
              UpperCAmelCase          =       1.5
              UpperCAmelCase          =       3
          for file_name in paths:
              if file_name.endswith('.png'				):
                  images.append(imageio.imread(lowercase_				)				)
          imageio.mimsave(lowercase_ ,							lowercase_ ,							duration=lowercase_				)
          print(F"gif saved to {output_path}"				)




      def      a_   (				self ,							lowercase_=None ,							lowercase_=None				)    ->       List[Any]:
          if not (path or img):
              raise ValueError('Input either path or tensor'				)
          if img is not None:
              raise NotImplementedError
          UpperCAmelCase          =       preprocess(Image.open(lowercase_				) ,							target_image_size=2_5_6				).to(self.device				)
          UpperCAmelCase          =       preprocess_vqgan(lowercase_				)
          UpperCAmelCase      ,				*UpperCAmelCase          =       self.vqgan.encode(lowercase_				)
          return z




      def      a_   (				self ,							lowercase_				)    ->       Optional[int]:
          UpperCAmelCase          =       self.latent.detach().requires_grad_()
          UpperCAmelCase          =       base_latent + transform_vector
          if self.quantize:
              UpperCAmelCase      ,				*UpperCAmelCase          =       self.vqgan.quantize(lowercase_				)
          else:
              UpperCAmelCase          =       trans_latent
          return self.vqgan.decode(lowercase_				)




      def      a_   (				self ,							lowercase_ ,							lowercase_ ,							lowercase_=None				)    ->       str:
          UpperCAmelCase          =       self.clip_preprocessor(text=lowercase_ ,							images=lowercase_ ,							return_tensors='pt' ,							padding=lowercase_				)
          UpperCAmelCase          =       self.clip(**lowercase_				)
          UpperCAmelCase          =       clip_outputs.logits_per_image
          if weights is not None:
              UpperCAmelCase          =       similarity_logits * weights
          return similarity_logits.sum()




      def      a_   (				self ,							lowercase_ ,							lowercase_ ,							lowercase_				)    ->       List[Any]:
          UpperCAmelCase          =       self._get_clip_similarity(pos_prompts['prompts'] ,							lowercase_ ,							weights=(1 / pos_prompts['weights'])				)
          if neg_prompts:
              UpperCAmelCase          =       self._get_clip_similarity(neg_prompts['prompts'] ,							lowercase_ ,							weights=neg_prompts['weights']				)
          else:
              UpperCAmelCase          =       torch.tensor([1] ,							device=self.device				)
          UpperCAmelCase          =       -torch.log(lowercase_				) + torch.log(lowercase_				)
          return loss




      def      a_   (				self ,							lowercase_ ,							lowercase_ ,							lowercase_				)    ->       Tuple:
          UpperCAmelCase          =       torch.randn_like(self.latent ,							requires_grad=lowercase_ ,							device=self.device				)
          UpperCAmelCase          =       torch.optim.Adam([vector] ,							lr=self.lr				)

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




      def      a_   (				self ,							lowercase_ ,							lowercase_ ,							lowercase_				)    ->       Dict:
          wandb.init(reinit=lowercase_ ,							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          =       Image.open(lowercase_				)
              UpperCAmelCase          =       image.resize((2_5_6, 2_5_6)				)
              wandb.log('Original Image' ,							wandb.Image(lowercase_				)				)




      def      a_   (				self ,							lowercase_				)    ->       Tuple:
          if not prompts:
              return []
          UpperCAmelCase          =       []
          UpperCAmelCase          =       []
          if isinstance(lowercase_ ,							lowercase_				):
              UpperCAmelCase          =       [prompt.strip() for prompt in prompts.split('|'				)]
          for prompt in prompts:
              if isinstance(lowercase_ ,							(tuple, list)				):
                  UpperCAmelCase          =       prompt[0]
                  UpperCAmelCase          =       float(prompt[1]				)
              elif ":" in prompt:
                  UpperCAmelCase      ,				UpperCAmelCase          =       prompt.split(':'				)
                  UpperCAmelCase          =       float(lowercase_				)
              else:
                  UpperCAmelCase          =       prompt
                  UpperCAmelCase          =       1.0
              processed_prompts.append(lowercase_				)
              weights.append(lowercase_				)
          return {
              "prompts": processed_prompts,
              "weights": torch.tensor(lowercase_ ,							device=self.device				),
          }




      def      a_   (				self ,							lowercase_ ,							lowercase_=None ,							lowercase_=None ,							lowercase_=True ,							lowercase_=False ,							lowercase_=True ,							lowercase_=True ,							lowercase_=None ,							)    ->       List[str]:
          if image_path:
              UpperCAmelCase          =       self._get_latent(lowercase_				)
          else:
              UpperCAmelCase          =       torch.randn(self.latent_dim ,							device=self.device				)
          if self.log:
              self._init_logging(lowercase_ ,							lowercase_ ,							lowercase_				)

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

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

          UpperCAmelCase          =       loop_post_process(lowercase_				)
          for iter, transformed_img in enumerate(self._optimize_CLIP(lowercase_ ,							lowercase_ ,							lowercase_				)				):
              if show_intermediate:
                  show_pil(lowercase_				)
              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(lowercase_				)}				)
          if show_final:
              show_pil(lowercase_				)
          if save_final:
              transformed_img.save(os.path.join(self.save_path ,							F"iter_{iter:03d}_final.png"				)				)



