

import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModelWithProjection, CLIPTokenizer

from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffusers.utils import load_numpy, slow
from diffusers.utils.testing_utils import require_torch_gpu, torch_device

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference






class      lowerCamelCase_      (	_lowercase						,      unittest.TestCase	):
	_lowercase		:   Any     					=				ShapEPipeline
	_lowercase		:   int     					=				['''prompt''']
	_lowercase		:   List[str]     					=				['''prompt''']
	_lowercase		:   Union[str, Any]     					=				[
	    '''num_images_per_prompt''',
	    '''num_inference_steps''',
	    '''generator''',
	    '''latents''',
	    '''guidance_scale''',
	    '''frame_size''',
	    '''output_type''',
	    '''return_dict''',
	]
	_lowercase		:   Optional[Any]     					=				False




	@property
	def        lowerCAmelCase_       (     self						:     List[str]							):
		return 32




	@property
	def        lowerCAmelCase_       (     self						:     Optional[int]							):
		return 32




	@property
	def        lowerCAmelCase_       (     self						:     Dict							):
		return self.time_input_dim * 4




	@property
	def        lowerCAmelCase_       (     self						:     str							):
		return 8




	@property
	def        lowerCAmelCase_       (     self						:     Optional[int]							):
		__A :			Tuple		    =			CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""							)
		return tokenizer




	@property
	def        lowerCAmelCase_       (     self						:     str							):
		torch.manual_seed(0							)
		__A :			Tuple		    =			CLIPTextConfig(
		    bos_token_id=0					,      eos_token_id=2					,      hidden_size=self.text_embedder_hidden_size					,      projection_dim=self.text_embedder_hidden_size					,      intermediate_size=37					,      layer_norm_eps=1e-0_5					,      num_attention_heads=4					,      num_hidden_layers=5					,      pad_token_id=1					,      vocab_size=1000					,      )
		return CLIPTextModelWithProjection(__A							)




	@property
	def        lowerCAmelCase_       (     self						:     List[Any]							):
		torch.manual_seed(0							)

		__A :			Optional[int]		    =			{
		    """num_attention_heads""": 2,
		    """attention_head_dim""": 16,
		    """embedding_dim""": self.time_input_dim,
		    """num_embeddings""": 32,
		    """embedding_proj_dim""": self.text_embedder_hidden_size,
		    """time_embed_dim""": self.time_embed_dim,
		    """num_layers""": 1,
		    """clip_embed_dim""": self.time_input_dim * 2,
		    """additional_embeddings""": 0,
		    """time_embed_act_fn""": """gelu""",
		    """norm_in_type""": """layer""",
		    """encoder_hid_proj_type""": None,
		    """added_emb_type""": None,
		}

		__A :			List[str]		    =			PriorTransformer(**__A							)
		return model




	@property
	def        lowerCAmelCase_       (     self						:     Dict							):
		torch.manual_seed(0							)

		__A :			Any		    =			{
		    """param_shapes""": (
		        (self.renderer_dim, 93),
		        (self.renderer_dim, 8),
		        (self.renderer_dim, 8),
		        (self.renderer_dim, 8),
		    ),
		    """d_latent""": self.time_input_dim,
		    """d_hidden""": self.renderer_dim,
		    """n_output""": 12,
		    """background""": (
		        0.1,
		        0.1,
		        0.1,
		    ),
		}
		__A :			Tuple		    =			ShapERenderer(**__A							)
		return model




	def        lowerCAmelCase_       (     self						:     Optional[int]							):
		__A :			Union[str, Any]		    =			self.dummy_prior
		__A :			List[Any]		    =			self.dummy_text_encoder
		__A :			Union[str, Any]		    =			self.dummy_tokenizer
		__A :			Tuple		    =			self.dummy_renderer

		__A :			Any		    =			HeunDiscreteScheduler(
		    beta_schedule="""exp"""					,      num_train_timesteps=1024					,      prediction_type="""sample"""					,      use_karras_sigmas=__A					,      clip_sample=__A					,      clip_sample_range=1.0					,      )
		__A :			Tuple		    =			{
		    """prior""": prior,
		    """text_encoder""": text_encoder,
		    """tokenizer""": tokenizer,
		    """renderer""": renderer,
		    """scheduler""": scheduler,
		}

		return components




	def        lowerCAmelCase_       (     self						:     List[Any]					,      __A						:     str					,      __A						:     Any=0							):
		if str(__A							).startswith("""mps"""							):
			__A :			int		    =			torch.manual_seed(__A							)
		else:
			__A :			List[Any]		    =			torch.Generator(device=__A							).manual_seed(__A							)
		__A :			Optional[int]		    =			{
		    """prompt""": """horse""",
		    """generator""": generator,
		    """num_inference_steps""": 1,
		    """frame_size""": 32,
		    """output_type""": """np""",
		}
		return inputs




	def        lowerCAmelCase_       (     self						:     Optional[int]							):
		__A :			str		    =			"""cpu"""

		__A :			Tuple		    =			self.get_dummy_components()

		__A :			Optional[Any]		    =			self.pipeline_class(**__A							)
		__A :			List[str]		    =			pipe.to(__A							)

		pipe.set_progress_bar_config(disable=__A							)

		__A :			Tuple		    =			pipe(**self.get_dummy_inputs(__A							)							)
		__A :			Tuple		    =			output.images[0]
		__A :			Optional[Any]		    =			image[0, -3:, -3:, -1]

		assert image.shape == (20, 32, 32, 3)

		__A :			List[Any]		    =			np.array(
		    [
		        0.0_0_0_3_9_2_1_6,
		        0.0_0_0_3_9_2_1_6,
		        0.0_0_0_3_9_2_1_6,
		        0.0_0_0_3_9_2_1_6,
		        0.0_0_0_3_9_2_1_6,
		        0.0_0_0_3_9_2_1_6,
		        0.0_0_0_3_9_2_1_6,
		        0.0_0_0_3_9_2_1_6,
		        0.0_0_0_3_9_2_1_6,
		    ]							)

		assert np.abs(image_slice.flatten() - expected_slice							).max() < 1e-2




	def        lowerCAmelCase_       (     self						:     Dict							):
		# NOTE: Larger batch sizes cause this test to timeout, only test on smaller batches
		self._test_inference_batch_consistent(batch_sizes=[1, 2]							)




	def        lowerCAmelCase_       (     self						:     Dict							):
		__A :			List[str]		    =			torch_device == """cpu"""
		__A :			str		    =			True

		self._test_inference_batch_single_identical(
		    batch_size=2					,      test_max_difference=__A					,      relax_max_difference=__A					,      )




	def        lowerCAmelCase_       (     self						:     Dict							):
		__A :			List[Any]		    =			self.get_dummy_components()
		__A :			List[str]		    =			self.pipeline_class(**__A							)
		__A :			Any		    =			pipe.to(__A							)
		pipe.set_progress_bar_config(disable=__A							)

		__A :			List[Any]		    =			1
		__A :			int		    =			2

		__A :			int		    =			self.get_dummy_inputs(__A							)

		for key in inputs.keys():
			if key in self.batch_params:
				__A :			Optional[Any]		    =			batch_size * [inputs[key]]

		__A :			Tuple		    =			pipe(**__A					,      num_images_per_prompt=__A							)[0]

		assert images.shape[0] == batch_size * num_images_per_prompt






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




	def        lowerCAmelCase_       (     self						:     Optional[Any]							):
		# clean up the VRAM after each test
		super().tearDown()
		gc.collect()
		torch.cuda.empty_cache()




	def        lowerCAmelCase_       (     self						:     int							):
		__A :			Any		    =			load_numpy(
		    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
		    """/shap_e/test_shap_e_np_out.npy"""							)
		__A :			Union[str, Any]		    =			ShapEPipeline.from_pretrained("""openai/shap-e"""							)
		__A :			Any		    =			pipe.to(__A							)
		pipe.set_progress_bar_config(disable=__A							)

		__A :			int		    =			torch.Generator(device=__A							).manual_seed(0							)

		__A :			str		    =			pipe(
		    """a shark"""					,      generator=__A					,      guidance_scale=1_5.0					,      num_inference_steps=64					,      frame_size=64					,      output_type="""np"""					,      ).images[0]

		assert images.shape == (20, 64, 64, 3)

		assert_mean_pixel_difference(__A					,      __A							)










'''simple docstring'''





import json
import os
import unittest

from transformers import DebertaTokenizer, DebertaTokenizerFast
from transformers.models.deberta.tokenization_deberta import VOCAB_FILES_NAMES
from transformers.testing_utils import slow

from ...test_tokenization_common import TokenizerTesterMixin



class  snake_case__						(    SCREAMING_SNAKE_CASE_  ,     unittest.TestCase				):
 A__				= DebertaTokenizer
 A__				= True
 A__				= DebertaTokenizerFast
 def A_   (   self						:							Optional[int]  )      ->   str:







       '''simple docstring'''
       super().setUp()

       # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
       __snake_case :   Union[str, Any]													=    [
           'l',
           'o',
           'w',
           'e',
           'r',
           's',
           't',
           'i',
           'd',
           'n',
           '\u0120',
           '\u0120l',
           '\u0120n',
           '\u0120lo',
           '\u0120low',
           'er',
           '\u0120lowest',
           '\u0120newer',
           '\u0120wider',
           '[UNK]',
       ]
       __snake_case :   List[str]													=    dict(zip(__a      ,     range(len(__a  )  )  )  )
       __snake_case :   int													=    ['#version: 0.2', '\u0120 l', '\u0120l o', '\u0120lo w', 'e r', '']
       __snake_case :   Any													=    {'unk_token': '[UNK]'}

       __snake_case :   Union[str, Any]													=    os.path.join(self.tmpdirname      ,     VOCAB_FILES_NAMES['vocab_file']  )
       __snake_case :   Dict													=    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(__a  ) + '\n'  )
       with open(self.merges_file      ,     'w'      ,     encoding='utf-8'  ) as fp:
             fp.write('\n'.join(__a  )  )
 def A_   (   self						:							Optional[int]      ,     **__a						:							List[Any]  )      ->   Optional[Any]:







       '''simple docstring'''
       kwargs.update(self.special_tokens_map  )
       return self.tokenizer_class.from_pretrained(self.tmpdirname      ,     **__a  )
 def A_   (   self						:							Optional[int]      ,     __a						:							Dict  )      ->   List[str]:







       '''simple docstring'''
       __snake_case :   Union[str, Any]													=    'lower newer'
       __snake_case :   List[Any]													=    'lower newer'
       return input_text, output_text
 def A_   (   self						:							Dict  )      ->   Union[str, Any]:







       '''simple docstring'''
       __snake_case :   str													=    self.get_tokenizer()
       __snake_case :   List[str]													=    'lower newer'
       __snake_case :   Tuple													=    ['l', 'o', 'w', 'er', '\u0120', 'n', 'e', 'w', 'er']
       __snake_case :   Optional[int]													=    tokenizer.tokenize(__a  )
       self.assertListEqual(__a      ,     __a  )

       __snake_case :   str													=    tokens + [tokenizer.unk_token]
       __snake_case :   Tuple													=    [0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
       self.assertListEqual(tokenizer.convert_tokens_to_ids(__a  )      ,     __a  )
 def A_   (   self						:							List[Any]  )      ->   Dict:







       '''simple docstring'''
       __snake_case :   List[str]													=    self.get_tokenizer()
       __snake_case :   List[str]													=    tokenizer('Hello'      ,     'World'  )
       __snake_case :   int													=    [0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1]
       self.assertListEqual(tokd['token_type_ids']      ,     __a  )
 @slow
 def A_   (   self						:							Dict  )      ->   str:







       '''simple docstring'''
       __snake_case :   str													=    self.tokenizer_class.from_pretrained('microsoft/deberta-base'  )

       __snake_case :   str													=    tokenizer.encode('sequence builders'      ,     add_special_tokens=__a  )
       __snake_case :   Any													=    tokenizer.encode('multi-sequence build'      ,     add_special_tokens=__a  )

       __snake_case :   List[Any]													=    tokenizer.encode(
           'sequence builders'      ,     add_special_tokens=__a      ,     add_prefix_space=__a  )
       __snake_case :   Union[str, Any]													=    tokenizer.encode(
           'sequence builders'      ,     'multi-sequence build'      ,     add_special_tokens=__a      ,     add_prefix_space=__a  )

       __snake_case :   int													=    tokenizer.build_inputs_with_special_tokens(__a  )
       __snake_case :   Tuple													=    tokenizer.build_inputs_with_special_tokens(__a      ,     __a  )

       assert encoded_sentence == encoded_text_from_decode
       assert encoded_pair == encoded_pair_from_decode





 @slow
 def A_   (   self						:							Optional[Any]  )      ->   Optional[int]:







       '''simple docstring'''
       __snake_case :   int													=    [self.tokenizer_class]
       if self.test_rust_tokenizer:
             tokenizer_classes.append(self.rust_tokenizer_class  )

       for tokenizer_class in tokenizer_classes:
             __snake_case :   List[Any]													=    tokenizer_class.from_pretrained('microsoft/deberta-base'  )

             __snake_case :   int													=    [
                 'ALBERT: A Lite BERT for Self-supervised Learning of Language Representations',
                 'ALBERT incorporates two parameter reduction techniques',
                 'The first one is a factorized embedding parameterization. By decomposing the large vocabulary'
                 ' embedding matrix into two small matrices, we separate the size of the hidden layers from the size of'
                 ' vocabulary embedding.',
             ]

             __snake_case :   Union[str, Any]													=    tokenizer(__a      ,     padding=__a  )
             __snake_case :   List[str]													=    [tokenizer.decode(__a      ,     skip_special_tokens=__a  ) for seq in encoding['input_ids']]

             # fmt: off
             __snake_case :   Optional[Any]													=    {
                 'input_ids': [
                     [1, 2118, 11126, 565, 35, 83, 25191, 163, 18854, 13, 12156, 12, 16101, 25376, 13807, 9, 22205, 27893, 1635, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                     [1, 2118, 11126, 565, 24536, 80, 43797, 4878, 7373, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                     [1, 133, 78, 65, 16, 10, 3724, 1538, 33183, 11303, 43797, 1938, 4, 870, 24165, 29105, 5, 739, 32644, 33183, 11303, 36173, 88, 80, 650, 7821, 45940, 6, 52, 2559, 5, 1836, 9, 5, 7397, 13171, 31, 5, 1836, 9, 32644, 33183, 11303, 4, 2]
                 ],
                 'token_type_ids': [
                     [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                     [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                     [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
                 ],
                 'attention_mask': [
                     [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                     [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                     [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
                 ]
             }
             # fmt: on

             __snake_case :   int													=    [
                 'ALBERT: A Lite BERT for Self-supervised Learning of Language Representations',
                 'ALBERT incorporates two parameter reduction techniques',
                 'The first one is a factorized embedding parameterization. By decomposing the large vocabulary'
                 ' embedding matrix into two small matrices, we separate the size of the hidden layers from the size of'
                 ' vocabulary embedding.',
             ]

             self.assertDictEqual(encoding.data      ,     __a  )

             for expected, decoded in zip(__a      ,     __a  ):
                   self.assertEqual(__a      ,     __a  )



