




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    (		_lowerCamelCase				,unittest.TestCase						):






			'''simple docstring'''





			UpperCamelCase__								=ShapEPipeline
			UpperCamelCase__								=['''prompt''']
			UpperCamelCase__								=['''prompt''']
			UpperCamelCase__								=[
			    '''num_images_per_prompt''',
			    '''num_inference_steps''',
			    '''generator''',
			    '''latents''',
			    '''guidance_scale''',
			    '''frame_size''',
			    '''output_type''',
			    '''return_dict''',
			]
			UpperCamelCase__								=False

			@property
			def 							UpperCAmelCase__       ( self			:   Dict  )						->   Dict:
								return 32

			@property
			def 							UpperCAmelCase__       ( self			:   Tuple  )						->   Optional[Any]:
								return 32

			@property
			def 							UpperCAmelCase__       ( self			:   Optional[Any]  )						->   Optional[int]:
								return self.time_input_dim * 4

			@property
			def 							UpperCAmelCase__       ( self			:   str  )						->   Any:
								return 8

			@property
			def 							UpperCAmelCase__       ( self			:   List[str]  )						->   Tuple:
								__magic_name__							:	List[Any]    							=							CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''  )
								return tokenizer

			@property
			def 							UpperCAmelCase__       ( self			:   int  )						->   List[str]:
								torch.manual_seed(0  )
								__magic_name__							:	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-05       ,						num_attention_heads=4       ,						num_hidden_layers=5       ,						pad_token_id=1       ,						vocab_size=1000       ,						)
								return CLIPTextModelWithProjection(lowerCamelCase_  )

			@property
			def 							UpperCAmelCase__       ( self			:   Union[str, Any]  )						->   int:
								torch.manual_seed(0  )

								__magic_name__							:	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,
								}

								__magic_name__							:	Any    							=							PriorTransformer(**lowerCamelCase_  )
								return model

			@property
			def 							UpperCAmelCase__       ( self			:   str  )						->   Dict:
								torch.manual_seed(0  )

								__magic_name__							:	List[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,
								    ),
								}
								__magic_name__							:	str    							=							ShapERenderer(**lowerCamelCase_  )
								return model

			def 							UpperCAmelCase__       ( self			:   str  )						->   Optional[Any]:
								__magic_name__							:	Union[str, Any]    							=							self.dummy_prior
								__magic_name__							:	List[Any]    							=							self.dummy_text_encoder
								__magic_name__							:	Tuple    							=							self.dummy_tokenizer
								__magic_name__							:	Any    							=							self.dummy_renderer

								__magic_name__							:	str    							=							HeunDiscreteScheduler(
								    beta_schedule='''exp'''       ,						num_train_timesteps=1024       ,						prediction_type='''sample'''       ,						use_karras_sigmas=lowerCamelCase_       ,						clip_sample=lowerCamelCase_       ,						clip_sample_range=1.0       ,						)
								__magic_name__							:	List[Any]    							=							{
								    '''prior''': prior,
								    '''text_encoder''': text_encoder,
								    '''tokenizer''': tokenizer,
								    '''renderer''': renderer,
								    '''scheduler''': scheduler,
								}

								return components

			def 							UpperCAmelCase__       ( self			:   str       ,						lowerCamelCase_			:   Tuple       ,						lowerCamelCase_			:   str=0  )						->   Optional[Any]:
								if str(lowerCamelCase_  ).startswith('''mps'''  ):
													__magic_name__							:	int    							=							torch.manual_seed(lowerCamelCase_  )
								else:
													__magic_name__							:	Tuple    							=							torch.Generator(device=lowerCamelCase_  ).manual_seed(lowerCamelCase_  )
								__magic_name__							:	List[str]    							=							{
								    '''prompt''': '''horse''',
								    '''generator''': generator,
								    '''num_inference_steps''': 1,
								    '''frame_size''': 32,
								    '''output_type''': '''np''',
								}
								return inputs

			def 							UpperCAmelCase__       ( self			:   List[Any]  )						->   Union[str, Any]:
								__magic_name__							:	str    							=							'''cpu'''

								__magic_name__							:	Union[str, Any]    							=							self.get_dummy_components()

								__magic_name__							:	Tuple    							=							self.pipeline_class(**lowerCamelCase_  )
								__magic_name__							:	Union[str, Any]    							=							pipe.to(lowerCamelCase_  )

								pipe.set_progress_bar_config(disable=lowerCamelCase_  )

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

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

								__magic_name__							:	Tuple    							=							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 							UpperCAmelCase__       ( self			:   List[Any]  )						->   Optional[int]:
								# NOTE: Larger batch sizes cause this test to timeout, only test on smaller batches
								self._test_inference_batch_consistent(batch_sizes=[1, 2]  )

			def 							UpperCAmelCase__       ( self			:   int  )						->   int:
								__magic_name__							:	Dict    							=							torch_device == '''cpu'''
								__magic_name__							:	Union[str, Any]    							=							True

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


			def 							UpperCAmelCase__       ( self			:   Union[str, Any]  )						->   Union[str, Any]:
								__magic_name__							:	List[Any]    							=							self.get_dummy_components()
								__magic_name__							:	Union[str, Any]    							=							self.pipeline_class(**lowerCamelCase_  )
								__magic_name__							:	int    							=							pipe.to(lowerCamelCase_  )
								pipe.set_progress_bar_config(disable=lowerCamelCase_  )

								__magic_name__							:	List[str]    							=							1
								__magic_name__							:	str    							=							2

								__magic_name__							:	Optional[int]    							=							self.get_dummy_inputs(lowerCamelCase_  )

								for key in inputs.keys():
													if key in self.batch_params:
																		__magic_name__							:	str    							=							batch_size * [inputs[key]]

								__magic_name__							:	Optional[Any]    							=							pipe(**lowerCamelCase_       ,						num_images_per_prompt=lowerCamelCase_  )[0]

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






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






			'''simple docstring'''





			def 							UpperCAmelCase__       ( self			:   Tuple  )						->   Union[str, Any]:
								# clean up the VRAM after each test
								super().tearDown()
								gc.collect()
								torch.cuda.empty_cache()


			def 							UpperCAmelCase__       ( self			:   List[str]  )						->   List[str]:
								__magic_name__							:	Any    							=							load_numpy(
								    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
								    '''/shap_e/test_shap_e_np_out.npy'''  )
								__magic_name__							:	Dict    							=							ShapEPipeline.from_pretrained('''openai/shap-e'''  )
								__magic_name__							:	Tuple    							=							pipe.to(lowerCamelCase_  )
								pipe.set_progress_bar_config(disable=lowerCamelCase_  )

								__magic_name__							:	List[str]    							=							torch.Generator(device=lowerCamelCase_  ).manual_seed(0  )

								__magic_name__							:	Union[str, Any]    							=							pipe(
								    '''a shark'''       ,						generator=lowerCamelCase_       ,						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(lowerCamelCase_       ,						lowerCamelCase_  )









import json
import os
import unittest

from transformers.models.blenderbot_small.tokenization_blenderbot_small import (
    VOCAB_FILES_NAMES,
    BlenderbotSmallTokenizer,
)

from ...test_tokenization_common import TokenizerTesterMixin


class    lowerCamelCase    (		_lowerCamelCase				,unittest.TestCase						):






			'''simple docstring'''





			UpperCamelCase__								=BlenderbotSmallTokenizer
			UpperCamelCase__								=False

			def 							UpperCAmelCase__       ( self			:   Union[str, Any]  )						->   str:
								super().setUp()

								__magic_name__							:	Union[str, Any]    							=							['''__start__''', '''adapt''', '''act''', '''ap@@''', '''te''', '''__end__''', '''__unk__''']
								__magic_name__							:	Tuple    							=							dict(zip(lowerCamelCase_       ,						range(len(lowerCamelCase_  )  )  )  )

								__magic_name__							:	Tuple    							=							['''#version: 0.2''', '''a p''', '''t e</w>''', '''ap t</w>''', '''a d''', '''ad apt</w>''', '''a c''', '''ac t</w>''', '''''']
								__magic_name__							:	List[Any]    							=							{'''unk_token''': '''__unk__''', '''bos_token''': '''__start__''', '''eos_token''': '''__end__'''}

								__magic_name__							:	str    							=							os.path.join(self.tmpdirname       ,						VOCAB_FILES_NAMES['''vocab_file''']  )
								__magic_name__							:	List[str]    							=							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_  )  )

			def 							UpperCAmelCase__       ( self			:   List[str]       ,						**lowerCamelCase_			:   Optional[Any]  )						->   List[Any]:
								kwargs.update(self.special_tokens_map  )
								return BlenderbotSmallTokenizer.from_pretrained(self.tmpdirname       ,						**lowerCamelCase_  )

			def 							UpperCAmelCase__       ( self			:   Dict       ,						lowerCamelCase_			:   Union[str, Any]  )						->   Optional[int]:
								__magic_name__							:	Union[str, Any]    							=							'''adapt act apte'''
								__magic_name__							:	Dict    							=							'''adapt act apte'''
								return input_text, output_text

			def 							UpperCAmelCase__       ( self			:   Union[str, Any]  )						->   Any:
								__magic_name__							:	int    							=							BlenderbotSmallTokenizer(self.vocab_file       ,						self.merges_file       ,						**self.special_tokens_map  )
								__magic_name__							:	str    							=							'''adapt act apte'''
								__magic_name__							:	Any    							=							['''adapt''', '''act''', '''ap@@''', '''te''']
								__magic_name__							:	List[str]    							=							tokenizer.tokenize(lowerCamelCase_  )
								self.assertListEqual(lowerCamelCase_       ,						lowerCamelCase_  )

								__magic_name__							:	Union[str, Any]    							=							[tokenizer.bos_token] + tokens + [tokenizer.eos_token]

								__magic_name__							:	List[Any]    							=							[0, 1, 2, 3, 4, 5]
								self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCamelCase_  )       ,						lowerCamelCase_  )

			def 							UpperCAmelCase__       ( self			:   int  )						->   int:
								__magic_name__							:	Any    							=							BlenderbotSmallTokenizer.from_pretrained('''facebook/blenderbot-90M'''  )
								assert tok('''sam'''  ).input_ids == [1384]
								__magic_name__							:	Dict    							=							'''I am a small frog.'''
								__magic_name__							:	Tuple    							=							tok([src_text]       ,						padding=lowerCamelCase_       ,						truncation=lowerCamelCase_  )['''input_ids''']
								__magic_name__							:	Tuple    							=							tok.batch_decode(lowerCamelCase_       ,						skip_special_tokens=lowerCamelCase_       ,						clean_up_tokenization_spaces=lowerCamelCase_  )[0]
								assert src_text != decoded  # I wish it did!
								assert decoded == "i am a small frog ."


			def 							UpperCAmelCase__       ( self			:   Tuple  )						->   Dict:
								__magic_name__							:	Tuple    							=							BlenderbotSmallTokenizer.from_pretrained('''facebook/blenderbot-90M'''  )
								__magic_name__							:	Any    							=							'''I am a small frog .'''
								__magic_name__							:	List[str]    							=							'''.'''
								__magic_name__							:	Tuple    							=							tok(lowerCamelCase_  )['''input_ids''']
								__magic_name__							:	Optional[Any]    							=							tok(lowerCamelCase_  )['''input_ids''']

								assert encoded[-1] == encoded_dot[0]



