


"""simple docstring"""




from __future__ import annotations

import unittest
import warnings

from transformers import AutoTokenizer, MarianConfig, MarianTokenizer, TranslationPipeline, is_tf_available
from transformers.testing_utils import require_sentencepiece, 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, TFMarianModel, TFMarianMTModel





@require_tf
class __snake_case		:







   """simple docstring"""




   lowerCAmelCase_				:   Union[str, Any]     	=			MarianConfig
   lowerCAmelCase_				:   Dict     	=			{}
   lowerCAmelCase_				:   int     	=			'gelu'




   def __init__(		self							:Union[str, Any]				,  UpperCamelCase__							:Tuple				,  UpperCamelCase__							:str=13				,  UpperCamelCase__							:List[Any]=7				,  UpperCamelCase__							:Tuple=True				,  UpperCamelCase__							:Dict=False				,  UpperCamelCase__							:int=99				,  UpperCamelCase__							:str=32				,  UpperCamelCase__							:int=2				,  UpperCamelCase__							:Union[str, Any]=4				,  UpperCamelCase__							:Tuple=37				,  UpperCamelCase__							:Dict=0.1				,  UpperCamelCase__							:List[Any]=0.1				,  UpperCamelCase__							:List[Any]=20				,  UpperCamelCase__							:int=2				,  UpperCamelCase__							:Tuple=1				,  UpperCamelCase__							:Any=0				,  ):
          _a          =				parent
          _a          =				batch_size
          _a          =				seq_length
          _a          =				is_training
          _a          =				use_labels
          _a          =				vocab_size
          _a          =				hidden_size
          _a          =				num_hidden_layers
          _a          =				num_attention_heads
          _a          =				intermediate_size

          _a          =				hidden_dropout_prob
          _a          =				attention_probs_dropout_prob
          _a          =				max_position_embeddings
          _a          =				eos_token_id
          _a          =				pad_token_id
          _a          =				bos_token_id




   def SCREAMING_SNAKE_CASE_      (		self							:Optional[int]       ):
          _a          =				ids_tensor([self.batch_size, self.seq_length - 1]				,  self.vocab_size       )
          _a          =				tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size       )				,  1       )
          _a          =				tf.concat([input_ids, eos_tensor]				,  axis=1       )

          _a          =				ids_tensor([self.batch_size, self.seq_length]				,  self.vocab_size       )

          _a          =				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				,  )
          _a          =				prepare_marian_inputs_dict(UpperCamelCase__				,  UpperCamelCase__				,  UpperCamelCase__       )
          return config, inputs_dict




   def SCREAMING_SNAKE_CASE_      (		self							:str				,  UpperCamelCase__							:int				,  UpperCamelCase__							:Union[str, Any]       ):
          _a          =				TFMarianModel(config=UpperCamelCase__       ).get_decoder()
          _a          =				inputs_dict["input_ids"]

          _a          =				input_ids[:1, :]
          _a          =				inputs_dict["attention_mask"][:1, :]
          _a          =				inputs_dict["head_mask"]
          _a          =				1

          # first forward pass
          _a          =				model(UpperCamelCase__				,  attention_mask=UpperCamelCase__				,  head_mask=UpperCamelCase__				,  use_cache=UpperCamelCase__       )

          _a   , _a          =				outputs.to_tuple()

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

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

          _a          =				model(UpperCamelCase__				,  attention_mask=UpperCamelCase__       )[0]
          _a          =				model(UpperCamelCase__				,  attention_mask=UpperCamelCase__				,  past_key_values=UpperCamelCase__       )[0]

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

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

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






def 						__a      (  a    ,		a    ,		a    ,		a=None    ,		a=None    ,		a=None    ,		a=None    ,		a=None    ,		):





       """simple docstring"""







       if attention_mask is None:
              _a          =				tf.cast(tf.math.not_equal(a    ,		config.pad_token_id					)    ,		tf.inta					)
       if decoder_attention_mask is None:
              _a          =				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:
              _a          =				tf.ones((config.encoder_layers, config.encoder_attention_heads)					)
       if decoder_head_mask is None:
              _a          =				tf.ones((config.decoder_layers, config.decoder_attention_heads)					)
       if cross_attn_head_mask is None:
              _a          =				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 __snake_case		(					_SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE				,       unittest.TestCase      ):







   """simple docstring"""




   lowerCAmelCase_				:   Any     	=			(TFMarianMTModel, TFMarianModel) if is_tf_available() else ()
   lowerCAmelCase_				:   Tuple     	=			(TFMarianMTModel,) if is_tf_available() else ()
   lowerCAmelCase_				:   Tuple     	=			(
       {
           'conversational': TFMarianMTModel,
           'feature-extraction': TFMarianModel,
           'summarization': TFMarianMTModel,
           'text2text-generation': TFMarianMTModel,
           'translation': TFMarianMTModel,
       }
       if is_tf_available()
       else {}
   )
   lowerCAmelCase_				:   int     	=			True
   lowerCAmelCase_				:   List[str]     	=			False
   lowerCAmelCase_				:   Union[str, Any]     	=			False




   def SCREAMING_SNAKE_CASE_      (		self							:int       ):
          _a          =				TFMarianModelTester(self       )
          _a          =				ConfigTester(self				,  config_class=UpperCamelCase__       )




   def SCREAMING_SNAKE_CASE_      (		self							:Optional[int]       ):
          self.config_tester.run_common_tests()




   def SCREAMING_SNAKE_CASE_      (		self							:Tuple       ):
          _a          =				self.model_tester.prepare_config_and_inputs_for_common()
          self.model_tester.check_decoder_model_past_large_inputs(*UpperCamelCase__       )






@require_tf
class __snake_case		(					unittest.TestCase      ):







   """simple docstring"""




   lowerCAmelCase_				:   List[Any]     	=			1_000  # show more chars for failing integration tests




   @classmethod
   def SCREAMING_SNAKE_CASE_      (		cls							:str       ):
          _a          =				f'Helsinki-NLP/opus-mt-{cls.src}-{cls.tgt}'
          return cls




   @cached_property
   def SCREAMING_SNAKE_CASE_      (		self							:Union[str, Any]       ):
          return AutoTokenizer.from_pretrained(self.model_name       )




   @property
   def SCREAMING_SNAKE_CASE_      (		self							:List[str]       ):
          return self.tokenizer.eos_token_id




   @cached_property
   def SCREAMING_SNAKE_CASE_      (		self							:Any       ):
          warnings.simplefilter("error"       )
          _a          =				TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name       )
          assert isinstance(UpperCamelCase__				,  UpperCamelCase__       )
          _a          =				model.config
          self.assertListEqual(c.bad_words_ids				,  [[c.pad_token_id]]       )
          self.assertEqual(c.max_length				,  512       )
          self.assertEqual(c.decoder_start_token_id				,  c.pad_token_id       )
          return model




   def SCREAMING_SNAKE_CASE_      (		self							:Optional[Any]				,  **UpperCamelCase__							:List[str]       ):
          _a          =				self.translate_src_text(**UpperCamelCase__       )
          self.assertListEqual(self.expected_text				,  UpperCamelCase__       )




   def SCREAMING_SNAKE_CASE_      (		self							:Any				,  **UpperCamelCase__							:List[str]       ):
          _a          =				self.tokenizer(self.src_text				,  **UpperCamelCase__				,  padding=UpperCamelCase__				,  return_tensors="tf"       )
          _a          =				self.model.generate(
              model_inputs.input_ids				,  attention_mask=model_inputs.attention_mask				,  num_beams=2				,  max_length=128       )
          _a          =				self.tokenizer.batch_decode(generated_ids.numpy()				,  skip_special_tokens=UpperCamelCase__       )
          return generated_words






@require_sentencepiece
@require_tokenizers
@require_tf
class __snake_case		(					_SCREAMING_SNAKE_CASE      ):







   """simple docstring"""




   lowerCAmelCase_				:   str     	=			'mt'
   lowerCAmelCase_				:   List[str]     	=			'en'
   lowerCAmelCase_				:   List[str]     	=			['Billi messu b\'mod ġentili, Ġesù fejjaq raġel li kien milqut bil - marda kerha tal - ġdiem.']
   lowerCAmelCase_				:   Any     	=			['Touching gently, Jesus healed a man who was affected by the sad disease of leprosy.']




   @unittest.skip("Skipping until #12647 is resolved."       )
   @slow
   def SCREAMING_SNAKE_CASE_      (		self							:List[str]       ):
          self._assert_generated_batch_equal_expected()






@require_sentencepiece
@require_tokenizers
@require_tf
class __snake_case		(					_SCREAMING_SNAKE_CASE      ):







   """simple docstring"""




   lowerCAmelCase_				:   str     	=			'en'
   lowerCAmelCase_				:   Optional[Any]     	=			'zh'
   lowerCAmelCase_				:   List[str]     	=			['My name is Wolfgang and I live in Berlin']
   lowerCAmelCase_				:   str     	=			['我叫沃尔夫冈 我住在柏林']




   @unittest.skip("Skipping until #12647 is resolved."       )
   @slow
   def SCREAMING_SNAKE_CASE_      (		self							:List[str]       ):
          self._assert_generated_batch_equal_expected()






@require_sentencepiece
@require_tokenizers
@require_tf
class __snake_case		(					_SCREAMING_SNAKE_CASE      ):







   """simple docstring"""




   lowerCAmelCase_				:   Optional[Any]     	=			'en'
   lowerCAmelCase_				:   str     	=			'ROMANCE'
   lowerCAmelCase_				:   List[Any]     	=			[
       '>>fr<< Don\'t spend so much time watching TV.',
       '>>pt<< Your message has been sent.',
       '>>es<< He\'s two years older than me.',
   ]
   lowerCAmelCase_				:   Tuple     	=			[
       'Ne passez pas autant de temps à regarder la télé.',
       'A sua mensagem foi enviada.',
       'Es dos años más viejo que yo.',
   ]




   @unittest.skip("Skipping until #12647 is resolved."       )
   @slow
   def SCREAMING_SNAKE_CASE_      (		self							:Optional[Any]       ):
          self._assert_generated_batch_equal_expected()




   @unittest.skip("Skipping until #12647 is resolved."       )
   @slow
   def SCREAMING_SNAKE_CASE_      (		self							:Tuple       ):
          _a          =				TranslationPipeline(self.model				,  self.tokenizer				,  framework="tf"       )
          _a          =				pipeline(self.src_text       )
          self.assertEqual(self.expected_text				,  [x["translation_text"] for x in output]       )




"""simple docstring"""




from typing import Union

import torch
from PIL import Image
from torchvision import transforms as tfms
from tqdm.auto import tqdm
from transformers import CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    LMSDiscreteScheduler,
    PNDMScheduler,
    UNetaDConditionModel,
)





class __snake_case		(					_SCREAMING_SNAKE_CASE      ):







   """simple docstring"""




   def __init__(		self							:List[Any]				,  UpperCamelCase__							:AutoencoderKL				,  UpperCamelCase__							:CLIPTextModel				,  UpperCamelCase__							:CLIPTokenizer				,  UpperCamelCase__							:UNetaDConditionModel				,  UpperCamelCase__							:Union[PNDMScheduler, LMSDiscreteScheduler, DDIMScheduler]				,  ):
          super().__init__()

          self.register_modules(vae=UpperCamelCase__				,  text_encoder=UpperCamelCase__				,  tokenizer=UpperCamelCase__				,  unet=UpperCamelCase__				,  scheduler=UpperCamelCase__       )




   def SCREAMING_SNAKE_CASE_      (		self							:Tuple				,  UpperCamelCase__							:str       ):
          with torch.no_grad():
                 _a          =				self.vae.encode(tfms.ToTensor()(UpperCamelCase__       ).unsqueeze(0       ).to(self.device       ) * 2 - 1       )
                 _a          =				0.18215 * latent.latent_dist.sample()
          return latent




   def SCREAMING_SNAKE_CASE_      (		self							:List[Any]				,  UpperCamelCase__							:Any       ):
          _a          =				(1 / 0.18215) * latent
          with torch.no_grad():
                 _a          =				self.vae.decode(UpperCamelCase__       ).sample
          _a          =				(img / 2 + 0.5).clamp(0				,  1       )
          _a          =				img.detach().cpu().permute(0				,  2				,  3				,  1       ).numpy()
          _a          =				(img * 255).round().astype("uint8"       )
          return Image.fromarray(img[0]       )




   def SCREAMING_SNAKE_CASE_      (		self							:List[str]				,  UpperCamelCase__							:List[Any]       ):
          _a          =				self.tokenizer(
              UpperCamelCase__				,  padding="max_length"				,  max_length=self.tokenizer.model_max_length				,  truncation=UpperCamelCase__				,  return_tensors="pt"				,  )

          _a          =				self.text_encoder(text_input.input_ids.to(self.device       )       )[0]

          _a          =				self.tokenizer(
              ""				,  padding="max_length"				,  max_length=self.tokenizer.model_max_length				,  truncation=UpperCamelCase__				,  return_tensors="pt"				,  )

          _a          =				self.text_encoder(uncond_input.input_ids.to(self.device       )       )[0]

          return torch.cat([uncond_embedding, text_embedding]       )




   def __call__(		self							:Tuple				,  UpperCamelCase__							:Image.Image				,  UpperCamelCase__							:str				,  UpperCamelCase__							:float = 0.3				,  UpperCamelCase__							:float = 0.6				,  UpperCamelCase__							:float = 0.5				,  UpperCamelCase__							:int = 42				,  UpperCamelCase__							:int = 50				,  UpperCamelCase__							:float = 7.5				,  ):
          _a          =				steps - int(kmin * steps       )
          _a          =				steps - int(kmax * steps       )

          _a          =				self.prep_text(UpperCamelCase__       )

          self.scheduler.set_timesteps(UpperCamelCase__       )

          _a   , _a          =				img.size
          _a          =				self.encode(UpperCamelCase__       )

          torch.manual_seed(UpperCamelCase__       )
          _a          =				torch.randn(
              (1, self.unet.config.in_channels, height // 8, width // 8)				,  ).to(self.device       )

          _a          =				self.scheduler.add_noise(
              UpperCamelCase__				,  UpperCamelCase__				,  timesteps=self.scheduler.timesteps[tmax]				,  )

          _a          =				torch.cat([latents] * 2       )

          _a          =				self.scheduler.scale_model_input(UpperCamelCase__				,  self.scheduler.timesteps[tmax]       )

          with torch.no_grad():
                 _a          =				self.unet(
                     UpperCamelCase__				,  self.scheduler.timesteps[tmax]				,  encoder_hidden_states=UpperCamelCase__				,  ).sample

          _a   , _a          =				pred.chunk(2       )
          _a          =				pred_uncond + guidance_scale * (pred_text - pred_uncond)

          _a          =				self.scheduler.step(UpperCamelCase__				,  self.scheduler.timesteps[tmax]				,  UpperCamelCase__       ).prev_sample

          for i, t in enumerate(tqdm(self.scheduler.timesteps       )       ):
                 if i > tmax:
                        if i < tmin:  # layout generation phase
                               _a          =				self.scheduler.add_noise(
                                   UpperCamelCase__				,  UpperCamelCase__				,  timesteps=UpperCamelCase__				,  )

                               _a          =				(mix_factor * latents) + (
                                   1 - mix_factor
                               ) * orig_latents  # interpolating between layout noise and conditionally generated noise to preserve layout sematics
                               _a          =				torch.cat([input] * 2       )

                        else:  # content generation phase
                               _a          =				torch.cat([latents] * 2       )

                        _a          =				self.scheduler.scale_model_input(UpperCamelCase__				,  UpperCamelCase__       )

                        with torch.no_grad():
                               _a          =				self.unet(
                                   UpperCamelCase__				,  UpperCamelCase__				,  encoder_hidden_states=UpperCamelCase__				,  ).sample

                        _a   , _a          =				pred.chunk(2       )
                        _a          =				pred_uncond + guidance_scale * (pred_text - pred_uncond)

                        _a          =				self.scheduler.step(UpperCamelCase__				,  UpperCamelCase__				,  UpperCamelCase__       ).prev_sample

          return self.decode(UpperCamelCase__       )
