






"""simple docstring"""



import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
    from PIL import Image

    from transformers import CLIPSegProcessor, ViTImageProcessor

@require_vision
class        a     (   unittest.TestCase	):







    def 			UpperCamelCase_      (			self       ):
      lowercase							    =       tempfile.mkdtemp()

      # fmt: off
      lowercase							    =       ['l', 'o', 'w', 'e', 'r', 's', 't', 'i', 'd', 'n', 'lo', 'l</w>', 'w</w>', 'r</w>', 't</w>', 'low</w>', 'er</w>', 'lowest</w>', 'newer</w>', 'wider', '<unk>', '<|startoftext|>', '<|endoftext|>']
      # fmt: on
      lowercase							    =       dict(zip(_lowerCamelCase							,		range(len(_lowerCamelCase       )       )       )       )
      lowercase							    =       ['#version: 0.2', 'l o', 'lo w</w>', 'e r</w>', '']
      lowercase							    =       {'unk_token': '<unk>'}

      lowercase							    =       os.path.join(self.tmpdirname							,		VOCAB_FILES_NAMES['vocab_file']       )
      lowercase							    =       os.path.join(self.tmpdirname							,		VOCAB_FILES_NAMES['merges_file']       )
      with open(self.vocab_file							,		'w'							,		encoding='utf-8'       ) as fp:
        fp.write(json.dumps(_lowerCamelCase       ) + '\n'       )
      with open(self.merges_file							,		'w'							,		encoding='utf-8'       ) as fp:
        fp.write('\n'.join(_lowerCamelCase       )       )

      lowercase							    =       {
          'do_resize': True,
          'size': 2_0,
          'do_center_crop': True,
          'crop_size': 1_8,
          'do_normalize': True,
          'image_mean': [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],
          'image_std': [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],
      }
      lowercase							    =       os.path.join(self.tmpdirname							,		_lowerCamelCase       )
      with open(self.image_processor_file							,		'w'							,		encoding='utf-8'       ) as fp:
        json.dump(_lowerCamelCase							,		_lowerCamelCase       )







    def 			UpperCamelCase_      (			self							,		**_lowerCamelCase       ):
      return CLIPTokenizer.from_pretrained(self.tmpdirname							,		**_lowerCamelCase       )







    def 			UpperCamelCase_      (			self							,		**_lowerCamelCase       ):
      return CLIPTokenizerFast.from_pretrained(self.tmpdirname							,		**_lowerCamelCase       )







    def 			UpperCamelCase_      (			self							,		**_lowerCamelCase       ):
      return ViTImageProcessor.from_pretrained(self.tmpdirname							,		**_lowerCamelCase       )







    def 			UpperCamelCase_      (			self       ):
      shutil.rmtree(self.tmpdirname       )







    def 			UpperCamelCase_      (			self       ):
      lowercase							    =       [np.random.randint(2_5_5							,		size=(3, 3_0, 4_0_0)							,		dtype=np.uinta       )]

      lowercase							    =       [Image.fromarray(np.moveaxis(_lowerCamelCase							,		0							,		-1       )       ) for x in image_inputs]

      return image_inputs







    def 			UpperCamelCase_      (			self       ):
      lowercase							    =       self.get_tokenizer()
      lowercase							    =       self.get_rust_tokenizer()
      lowercase							    =       self.get_image_processor()

      lowercase							    =       CLIPSegProcessor(tokenizer=_lowerCamelCase							,		image_processor=_lowerCamelCase       )
      processor_slow.save_pretrained(self.tmpdirname       )
      lowercase							    =       CLIPSegProcessor.from_pretrained(self.tmpdirname							,		use_fast=_lowerCamelCase       )

      lowercase							    =       CLIPSegProcessor(tokenizer=_lowerCamelCase							,		image_processor=_lowerCamelCase       )
      processor_fast.save_pretrained(self.tmpdirname       )
      lowercase							    =       CLIPSegProcessor.from_pretrained(self.tmpdirname       )

      self.assertEqual(processor_slow.tokenizer.get_vocab()							,		tokenizer_slow.get_vocab()       )
      self.assertEqual(processor_fast.tokenizer.get_vocab()							,		tokenizer_fast.get_vocab()       )
      self.assertEqual(tokenizer_slow.get_vocab()							,		tokenizer_fast.get_vocab()       )
      self.assertIsInstance(processor_slow.tokenizer							,		_lowerCamelCase       )
      self.assertIsInstance(processor_fast.tokenizer							,		_lowerCamelCase       )

      self.assertEqual(processor_slow.image_processor.to_json_string()							,		image_processor.to_json_string()       )
      self.assertEqual(processor_fast.image_processor.to_json_string()							,		image_processor.to_json_string()       )
      self.assertIsInstance(processor_slow.image_processor							,		_lowerCamelCase       )
      self.assertIsInstance(processor_fast.image_processor							,		_lowerCamelCase       )







    def 			UpperCamelCase_      (			self       ):
      lowercase							    =       CLIPSegProcessor(tokenizer=self.get_tokenizer()							,		image_processor=self.get_image_processor()       )
      processor.save_pretrained(self.tmpdirname       )

      lowercase							    =       self.get_tokenizer(bos_token='(BOS)'							,		eos_token='(EOS)'       )
      lowercase							    =       self.get_image_processor(do_normalize=_lowerCamelCase							,		padding_value=1.0       )

      lowercase							    =       CLIPSegProcessor.from_pretrained(
          self.tmpdirname							,		bos_token='(BOS)'							,		eos_token='(EOS)'							,		do_normalize=_lowerCamelCase							,		padding_value=1.0       )

      self.assertEqual(processor.tokenizer.get_vocab()							,		tokenizer_add_kwargs.get_vocab()       )
      self.assertIsInstance(processor.tokenizer							,		_lowerCamelCase       )

      self.assertEqual(processor.image_processor.to_json_string()							,		image_processor_add_kwargs.to_json_string()       )
      self.assertIsInstance(processor.image_processor							,		_lowerCamelCase       )







    def 			UpperCamelCase_      (			self       ):
      lowercase							    =       self.get_image_processor()
      lowercase							    =       self.get_tokenizer()

      lowercase							    =       CLIPSegProcessor(tokenizer=_lowerCamelCase							,		image_processor=_lowerCamelCase       )

      lowercase							    =       self.prepare_image_inputs()

      lowercase							    =       image_processor(_lowerCamelCase							,		return_tensors='np'       )
      lowercase							    =       processor(images=_lowerCamelCase							,		return_tensors='np'       )

      for key in input_feat_extract.keys():
        self.assertAlmostEqual(input_feat_extract[key].sum()							,		input_processor[key].sum()							,		delta=1e-2       )







    def 			UpperCamelCase_      (			self       ):
      lowercase							    =       self.get_image_processor()
      lowercase							    =       self.get_tokenizer()

      lowercase							    =       CLIPSegProcessor(tokenizer=_lowerCamelCase							,		image_processor=_lowerCamelCase       )

      lowercase							    =       'lower newer'

      lowercase							    =       processor(text=_lowerCamelCase       )

      lowercase							    =       tokenizer(_lowerCamelCase       )

      for key in encoded_tok.keys():
        self.assertListEqual(encoded_tok[key]							,		encoded_processor[key]       )







    def 			UpperCamelCase_      (			self       ):
      lowercase							    =       self.get_image_processor()
      lowercase							    =       self.get_tokenizer()

      lowercase							    =       CLIPSegProcessor(tokenizer=_lowerCamelCase							,		image_processor=_lowerCamelCase       )

      lowercase							    =       'lower newer'
      lowercase							    =       self.prepare_image_inputs()

      lowercase							    =       processor(text=_lowerCamelCase							,		images=_lowerCamelCase       )

      self.assertListEqual(list(inputs.keys()       )							,		['input_ids', 'attention_mask', 'pixel_values']       )

      # test if it raises when no input is passed
      with pytest.raises(_lowerCamelCase       ):
        processor()







    def 			UpperCamelCase_      (			self       ):
      lowercase							    =       self.get_image_processor()
      lowercase							    =       self.get_tokenizer()

      lowercase							    =       CLIPSegProcessor(tokenizer=_lowerCamelCase							,		image_processor=_lowerCamelCase       )

      lowercase							    =       self.prepare_image_inputs()
      lowercase							    =       self.prepare_image_inputs()

      lowercase							    =       processor(images=_lowerCamelCase							,		visual_prompt=_lowerCamelCase       )

      self.assertListEqual(list(inputs.keys()       )							,		['pixel_values', 'conditional_pixel_values']       )

      # test if it raises when no input is passed
      with pytest.raises(_lowerCamelCase       ):
        processor()







    def 			UpperCamelCase_      (			self       ):
      lowercase							    =       self.get_image_processor()
      lowercase							    =       self.get_tokenizer()

      lowercase							    =       CLIPSegProcessor(tokenizer=_lowerCamelCase							,		image_processor=_lowerCamelCase       )

      lowercase							    =       [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

      lowercase							    =       processor.batch_decode(_lowerCamelCase       )
      lowercase							    =       tokenizer.batch_decode(_lowerCamelCase       )

      self.assertListEqual(_lowerCamelCase							,		_lowerCamelCase       )










"""simple docstring"""



import gc
import unittest

from diffusers import FlaxDPMSolverMultistepScheduler, FlaxStableDiffusionPipeline
from diffusers.utils import is_flax_available, slow
from diffusers.utils.testing_utils import require_flax


if is_flax_available():
    import jax
    import jax.numpy as jnp
    from flax.jax_utils import replicate
    from flax.training.common_utils import shard

@slow
@require_flax
class        a     (   unittest.TestCase	):







    def 			UpperCamelCase_      (			self       ):
      # clean up the VRAM after each test
      super().tearDown()
      gc.collect()







    def 			UpperCamelCase_      (			self       ):
      lowercase    ,     lowercase							    =       FlaxStableDiffusionPipeline.from_pretrained(
          'stabilityai/stable-diffusion-2'							,		revision='bf16'							,		dtype=jnp.bfloataa							,		)

      lowercase							    =       'A painting of a squirrel eating a burger'
      lowercase							    =       jax.device_count()
      lowercase							    =       num_samples * [prompt]
      lowercase							    =       sd_pipe.prepare_inputs(_lowerCamelCase       )

      lowercase							    =       replicate(_lowerCamelCase       )
      lowercase							    =       shard(_lowerCamelCase       )

      lowercase							    =       jax.random.PRNGKey(0       )
      lowercase							    =       jax.random.split(_lowerCamelCase							,		jax.device_count()       )

      lowercase							    =       sd_pipe(_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase							,		num_inference_steps=2_5							,		jit=_lowerCamelCase       )[0]
      assert images.shape == (jax.device_count(), 1, 7_6_8, 7_6_8, 3)

      lowercase							    =       images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]       )
      lowercase							    =       images[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]

      lowercase							    =       jnp.asarray(jax.device_get(image_slice.flatten()       )       )
      lowercase							    =       jnp.array([0.4_2_3_8, 0.4_4_1_4, 0.4_3_9_5, 0.4_4_5_3, 0.4_6_2_9, 0.4_5_9_0, 0.4_5_3_1, 0.4_5_5_0_8, 0.4_5_1_2]       )
      print(F'output_slice: {output_slice}'       )
      assert jnp.abs(output_slice - expected_slice       ).max() < 1e-2







    def 			UpperCamelCase_      (			self       ):
      lowercase							    =       'stabilityai/stable-diffusion-2'
      lowercase    ,     lowercase							    =       FlaxDPMSolverMultistepScheduler.from_pretrained(_lowerCamelCase							,		subfolder='scheduler'       )
      lowercase    ,     lowercase							    =       FlaxStableDiffusionPipeline.from_pretrained(
          _lowerCamelCase							,		scheduler=_lowerCamelCase							,		revision='bf16'							,		dtype=jnp.bfloataa							,		)
      lowercase							    =       scheduler_params

      lowercase							    =       'A painting of a squirrel eating a burger'
      lowercase							    =       jax.device_count()
      lowercase							    =       num_samples * [prompt]
      lowercase							    =       sd_pipe.prepare_inputs(_lowerCamelCase       )

      lowercase							    =       replicate(_lowerCamelCase       )
      lowercase							    =       shard(_lowerCamelCase       )

      lowercase							    =       jax.random.PRNGKey(0       )
      lowercase							    =       jax.random.split(_lowerCamelCase							,		jax.device_count()       )

      lowercase							    =       sd_pipe(_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase							,		num_inference_steps=2_5							,		jit=_lowerCamelCase       )[0]
      assert images.shape == (jax.device_count(), 1, 7_6_8, 7_6_8, 3)

      lowercase							    =       images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]       )
      lowercase							    =       images[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]

      lowercase							    =       jnp.asarray(jax.device_get(image_slice.flatten()       )       )
      lowercase							    =       jnp.array([0.4_3_3_6, 0.4_2_9_6_9, 0.4_4_5_3, 0.4_1_9_9, 0.4_2_9_7, 0.4_5_3_1, 0.4_4_3_4, 0.4_4_3_4, 0.4_2_9_7]       )
      print(F'output_slice: {output_slice}'       )
      assert jnp.abs(output_slice - expected_slice       ).max() < 1e-2


