
"""simple docstring"""

from __future__ import annotations

import unittest

from transformers import AutoTokenizer, MBartConfig, 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, TFMBartForConditionalGeneration, TFMBartModel

@require_tf
class __lowerCAmelCase	:
     lowercase		     =		MBartConfig
     lowercase		     =		{}
     lowercase		     =		"gelu"





     def __init__(			self			,  __UpperCAmelCase			,  __UpperCAmelCase=13			,  __UpperCAmelCase=7			,  __UpperCAmelCase=True			,  __UpperCAmelCase=False			,  __UpperCAmelCase=99			,  __UpperCAmelCase=32			,  __UpperCAmelCase=2			,  __UpperCAmelCase=4			,  __UpperCAmelCase=37			,  __UpperCAmelCase=0.1			,  __UpperCAmelCase=0.1			,  __UpperCAmelCase=20			,  __UpperCAmelCase=2			,  __UpperCAmelCase=1			,  __UpperCAmelCase=0			,  ):

      '''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_dropout_prob
      __UpperCamelCase       						= attention_probs_dropout_prob
      __UpperCamelCase       						= max_position_embeddings
      __UpperCamelCase       						= eos_token_id
      __UpperCamelCase       						= pad_token_id
      __UpperCamelCase       						= bos_token_id





     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      __UpperCamelCase       						= ids_tensor([self.batch_size, self.seq_length - 1]			,  self.vocab_size   )
      __UpperCamelCase       						= tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size   )			,  1   )
      __UpperCamelCase       						= tf.concat([input_ids, eos_tensor]			,  axis=1   )

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

      __UpperCamelCase       						= 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       						= prepare_mbart_inputs_dict(__UpperCAmelCase			,  __UpperCAmelCase			,  __UpperCAmelCase   )
      return config, inputs_dict







     def 		UpperCAmelCase      (			self			,  __UpperCAmelCase			,  __UpperCAmelCase   ):

      '''simple docstring'''




      __UpperCamelCase       						= TFMBartModel(config=__UpperCAmelCase   ).get_decoder()
      __UpperCamelCase       						= inputs_dict['input_ids']

      __UpperCamelCase       						= input_ids[:1, :]
      __UpperCamelCase       						= inputs_dict['attention_mask'][:1, :]
      __UpperCamelCase       						= inputs_dict['head_mask']
      __UpperCamelCase       						= 1

      # first forward pass
      __UpperCamelCase       						= model(__UpperCAmelCase			,  attention_mask=__UpperCAmelCase			,  head_mask=__UpperCAmelCase			,  use_cache=__UpperCAmelCase   )

      __UpperCamelCase						,			__UpperCamelCase       						= outputs.to_tuple()
      __UpperCamelCase       						= past_key_values[1]






def 							A		(       snake_case   :List[Any]       ,      snake_case   :str       ,      snake_case   :List[str]       ,      snake_case   :List[Any]=None       ,      snake_case   :List[Any]=None       ,      snake_case   :Optional[int]=None       ,      snake_case   :List[str]=None       ,      snake_case   :int=None       ,      )   ->			Dict:
 if attention_mask is None:
  __UpperCamelCase       						= tf.cast(tf.math.not_equal(snake_case       ,      config.pad_token_id	)       ,      tf.inta	)
 if decoder_attention_mask is None:
  __UpperCamelCase       						= 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       						= tf.ones((config.encoder_layers, config.encoder_attention_heads)	)
 if decoder_head_mask is None:
  __UpperCamelCase       						= tf.ones((config.decoder_layers, config.decoder_attention_heads)	)
 if cross_attn_head_mask is None:
  __UpperCamelCase       						= 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 __lowerCAmelCase	(   __SCREAMING_SNAKE_CASE    ,						__SCREAMING_SNAKE_CASE    ,						unittest.TestCase    ):
     lowercase		     =		(TFMBartForConditionalGeneration, TFMBartModel) if is_tf_available() else ()
     lowercase		     =		(TFMBartForConditionalGeneration,) if is_tf_available() else ()
     lowercase		     =		(
         {
             "conversational": TFMBartForConditionalGeneration,
             "feature-extraction": TFMBartModel,
             "summarization": TFMBartForConditionalGeneration,
             "text2text-generation": TFMBartForConditionalGeneration,
             "translation": TFMBartForConditionalGeneration,
         }
         if is_tf_available()
         else {}
     )
     lowercase		     =		True
     lowercase		     =		False
     lowercase		     =		False





     def 		UpperCAmelCase      (			self			,  __UpperCAmelCase			,  __UpperCAmelCase			,  __UpperCAmelCase			,  __UpperCAmelCase			,  __UpperCAmelCase   ):

      '''simple docstring'''




      if pipeline_test_casse_name != "FeatureExtractionPipelineTests":
       # Exception encountered when calling layer '...'
       return True

      return False





     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      __UpperCamelCase       						= TFMBartModelTester(self   )
      __UpperCamelCase       						= ConfigTester(self			,  config_class=__UpperCAmelCase   )





     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      self.config_tester.run_common_tests()







     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      __UpperCamelCase       						= self.model_tester.prepare_config_and_inputs_for_common()
      self.model_tester.check_decoder_model_past_large_inputs(*__UpperCAmelCase   )


@require_sentencepiece
@require_tokenizers
@require_tf
class __lowerCAmelCase	(   unittest.TestCase    ):
     lowercase		     =		[
         " UN Chief Says There Is No Military Solution in Syria",
     ]
     lowercase		     =		[
         "Şeful ONU declară că nu există o soluţie militară în Siria",
     ]
     lowercase		     =		"facebook/mbart-large-en-ro"





     @cached_property
     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      return AutoTokenizer.from_pretrained(self.model_name   )





     @cached_property
     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      __UpperCamelCase       						= TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name   )
      return model





     def 		UpperCAmelCase      (			self			,  **__UpperCAmelCase   ):

      '''simple docstring'''




      __UpperCamelCase       						= self.translate_src_text(**__UpperCAmelCase   )
      self.assertListEqual(self.expected_text			,  __UpperCAmelCase   )





     def 		UpperCAmelCase      (			self			,  **__UpperCAmelCase   ):

      '''simple docstring'''




      __UpperCamelCase       						= self.tokenizer(self.src_text			,  **__UpperCAmelCase			,  return_tensors='tf'   )
      __UpperCamelCase       						= self.model.generate(
          model_inputs.input_ids			,  attention_mask=model_inputs.attention_mask			,  num_beams=2   )
      __UpperCamelCase       						= self.tokenizer.batch_decode(__UpperCAmelCase			,  skip_special_tokens=__UpperCAmelCase   )
      return generated_words







     @slow
     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      self._assert_generated_batch_equal_expected()





"""simple docstring"""

import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast

from diffusers import DDIMScheduler, KandinskyInpaintPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()

class __lowerCAmelCase	(   __SCREAMING_SNAKE_CASE    ,						unittest.TestCase    ):
     lowercase		     =		KandinskyInpaintPipeline
     lowercase		     =		["prompt", "image_embeds", "negative_image_embeds", "image", "mask_image"]
     lowercase		     =		[
         "prompt",
         "negative_prompt",
         "image_embeds",
         "negative_image_embeds",
         "image",
         "mask_image",
     ]
     lowercase		     =		[
         "generator",
         "height",
         "width",
         "latents",
         "guidance_scale",
         "negative_prompt",
         "num_inference_steps",
         "return_dict",
         "guidance_scale",
         "num_images_per_prompt",
         "output_type",
         "return_dict",
     ]
     lowercase		     =		False





     @property
     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      return 32





     @property
     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      return 32





     @property
     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      return self.time_input_dim





     @property
     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      return self.time_input_dim * 4





     @property
     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      return 100





     @property
     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      __UpperCamelCase       						= XLMRobertaTokenizerFast.from_pretrained('YiYiXu/tiny-random-mclip-base'   )
      return tokenizer





     @property
     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      torch.manual_seed(0   )
      __UpperCamelCase       						= MCLIPConfig(
          numDims=self.cross_attention_dim			,  transformerDimensions=self.text_embedder_hidden_size			,  hidden_size=self.text_embedder_hidden_size			,  intermediate_size=37			,  num_attention_heads=4			,  num_hidden_layers=5			,  vocab_size=1005			,  )

      __UpperCamelCase       						= MultilingualCLIP(__UpperCAmelCase   )
      __UpperCamelCase       						= text_encoder.eval()

      return text_encoder





     @property
     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      torch.manual_seed(0   )

      __UpperCamelCase       						= {
          'in_channels': 9,
          # Out channels is double in channels because predicts mean and variance
          'out_channels': 8,
          'addition_embed_type': 'text_image',
          'down_block_types': ('ResnetDownsampleBlock2D', 'SimpleCrossAttnDownBlock2D'),
          'up_block_types': ('SimpleCrossAttnUpBlock2D', 'ResnetUpsampleBlock2D'),
          'mid_block_type': 'UNetMidBlock2DSimpleCrossAttn',
          'block_out_channels': (self.block_out_channels_a, self.block_out_channels_a * 2),
          'layers_per_block': 1,
          'encoder_hid_dim': self.text_embedder_hidden_size,
          'encoder_hid_dim_type': 'text_image_proj',
          'cross_attention_dim': self.cross_attention_dim,
          'attention_head_dim': 4,
          'resnet_time_scale_shift': 'scale_shift',
          'class_embed_type': None,
      }

      __UpperCamelCase       						= UNetaDConditionModel(**__UpperCAmelCase   )
      return model





     @property
     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      return {
          "block_out_channels": [32, 64],
          "down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
          "in_channels": 3,
          "latent_channels": 4,
          "layers_per_block": 1,
          "norm_num_groups": 8,
          "norm_type": "spatial",
          "num_vq_embeddings": 12,
          "out_channels": 3,
          "up_block_types": [
              "AttnUpDecoderBlock2D",
              "UpDecoderBlock2D",
          ],
          "vq_embed_dim": 4,
      }





     @property
     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      torch.manual_seed(0   )
      __UpperCamelCase       						= VQModel(**self.dummy_movq_kwargs   )
      return model





     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      __UpperCamelCase       						= self.dummy_text_encoder
      __UpperCamelCase       						= self.dummy_tokenizer
      __UpperCamelCase       						= self.dummy_unet
      __UpperCamelCase       						= self.dummy_movq

      __UpperCamelCase       						= DDIMScheduler(
          num_train_timesteps=1000			,  beta_schedule='linear'			,  beta_start=0.0_0_0_8_5			,  beta_end=0.0_1_2			,  clip_sample=__UpperCAmelCase			,  set_alpha_to_one=__UpperCAmelCase			,  steps_offset=1			,  prediction_type='epsilon'			,  thresholding=__UpperCAmelCase			,  )

      __UpperCamelCase       						= {
          'text_encoder': text_encoder,
          'tokenizer': tokenizer,
          'unet': unet,
          'scheduler': scheduler,
          'movq': movq,
      }

      return components





     def 		UpperCAmelCase      (			self			,  __UpperCAmelCase			,  __UpperCAmelCase=0   ):

      '''simple docstring'''




      __UpperCamelCase       						= floats_tensor((1, self.cross_attention_dim)			,  rng=random.Random(__UpperCAmelCase   )   ).to(__UpperCAmelCase   )
      __UpperCamelCase       						= floats_tensor((1, self.cross_attention_dim)			,  rng=random.Random(seed + 1   )   ).to(__UpperCAmelCase   )
      # create init_image
      __UpperCamelCase       						= floats_tensor((1, 3, 64, 64)			,  rng=random.Random(__UpperCAmelCase   )   ).to(__UpperCAmelCase   )
      __UpperCamelCase       						= image.cpu().permute(0			,  2			,  3			,  1   )[0]
      __UpperCamelCase       						= Image.fromarray(np.uinta(__UpperCAmelCase   )   ).convert('RGB'   ).resize((256, 256)   )
      # create mask
      __UpperCamelCase       						= np.ones((64, 64)			,  dtype=np.floataa   )
      __UpperCamelCase       						= 0

      if str(__UpperCAmelCase   ).startswith('mps'   ):
       __UpperCamelCase       						= torch.manual_seed(__UpperCAmelCase   )
      else:
       __UpperCamelCase       						= torch.Generator(device=__UpperCAmelCase   ).manual_seed(__UpperCAmelCase   )
      __UpperCamelCase       						= {
          'prompt': 'horse',
          'image': init_image,
          'mask_image': mask,
          'image_embeds': image_embeds,
          'negative_image_embeds': negative_image_embeds,
          'generator': generator,
          'height': 64,
          'width': 64,
          'num_inference_steps': 2,
          'guidance_scale': 4.0,
          'output_type': 'np',
      }
      return inputs





     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      __UpperCamelCase       						= 'cpu'

      __UpperCamelCase       						= self.get_dummy_components()

      __UpperCamelCase       						= self.pipeline_class(**__UpperCAmelCase   )
      __UpperCamelCase       						= pipe.to(__UpperCAmelCase   )

      pipe.set_progress_bar_config(disable=__UpperCAmelCase   )

      __UpperCamelCase       						= pipe(**self.get_dummy_inputs(__UpperCAmelCase   )   )
      __UpperCamelCase       						= output.images

      __UpperCamelCase       						= pipe(
          **self.get_dummy_inputs(__UpperCAmelCase   )			,  return_dict=__UpperCAmelCase			,  )[0]

      __UpperCamelCase       						= image[0, -3:, -3:, -1]
      __UpperCamelCase       						= image_from_tuple[0, -3:, -3:, -1]

      print(F'image.shape {image.shape}'   )

      assert image.shape == (1, 64, 64, 3)

      __UpperCamelCase       						= np.array(
          [0.8_3_2_6_9_1_9, 0.7_3_7_9_0_4_6_7, 0.2_0_9_1_8_5_8_1, 0.9_3_0_9_6_1_2, 0.5_5_1_1_7_9_1, 0.4_3_7_1_3_3_2_8, 0.5_5_1_3_3_2_1, 0.4_9_9_2_2_9_3_4, 0.5_9_4_9_7_7_8_6]   )

      assert (
          np.abs(image_slice.flatten() - expected_slice   ).max() < 1E-2
      ), F' expected_slice {expected_slice}, but got {image_slice.flatten()}'
      assert (
          np.abs(image_from_tuple_slice.flatten() - expected_slice   ).max() < 1E-2
      ), F' expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}'







     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      super().test_inference_batch_single_identical(expected_max_diff=3E-3   )


@slow
@require_torch_gpu
class __lowerCAmelCase	(   unittest.TestCase    ):





     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      super().tearDown()
      gc.collect()
      torch.cuda.empty_cache()







     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      __UpperCamelCase       						= load_numpy(
          'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
          '/kandinsky/kandinsky_inpaint_cat_with_hat_fp16.npy'   )

      __UpperCamelCase       						= load_image(
          'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main' '/kandinsky/cat.png'   )
      __UpperCamelCase       						= np.ones((768, 768)			,  dtype=np.floataa   )
      __UpperCamelCase       						= 0

      __UpperCamelCase       						= 'a hat'

      __UpperCamelCase       						= KandinskyPriorPipeline.from_pretrained(
          'kandinsky-community/kandinsky-2-1-prior'			,  torch_dtype=torch.floataa   )
      pipe_prior.to(__UpperCAmelCase   )

      __UpperCamelCase       						= KandinskyInpaintPipeline.from_pretrained(
          'kandinsky-community/kandinsky-2-1-inpaint'			,  torch_dtype=torch.floataa   )
      __UpperCamelCase       						= pipeline.to(__UpperCAmelCase   )
      pipeline.set_progress_bar_config(disable=__UpperCAmelCase   )

      __UpperCamelCase       						= torch.Generator(device='cpu'   ).manual_seed(0   )
      __UpperCamelCase						,			__UpperCamelCase       						= pipe_prior(
          __UpperCAmelCase			,  generator=__UpperCAmelCase			,  num_inference_steps=5			,  negative_prompt=''			,  ).to_tuple()

      __UpperCamelCase       						= pipeline(
          __UpperCAmelCase			,  image=__UpperCAmelCase			,  mask_image=__UpperCAmelCase			,  image_embeds=__UpperCAmelCase			,  negative_image_embeds=__UpperCAmelCase			,  generator=__UpperCAmelCase			,  num_inference_steps=100			,  height=768			,  width=768			,  output_type='np'			,  )

      __UpperCamelCase       						= output.images[0]

      assert image.shape == (768, 768, 3)

      assert_mean_pixel_difference(__UpperCAmelCase			,  __UpperCAmelCase   )



