




import os
import re
import shutil
import sys
import tempfile
import unittest

import black


__SCREAMING_SNAKE_CASE 						=os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, """utils"""))

import check_copies  # noqa: E402


# This is the reference code that will be used in the tests.
# If BertLMPredictionHead is changed in modeling_bert.py, this code needs to be manually updated.
__SCREAMING_SNAKE_CASE 						="""    def __init__(self, config):
        super().__init__()
        self.transform = BertPredictionHeadTransform(config)

        # The output weights are the same as the input embeddings, but there is
        # an output-only bias for each token.
        self.decoder = nn.Linear(config.hidden_size, config.vocab_size, bias=False)

        self.bias = nn.Parameter(torch.zeros(config.vocab_size))

        # Need a link between the two variables so that the bias is correctly resized with `resize_token_embeddings`
        self.decoder.bias = self.bias

    def forward(self, hidden_states):
        hidden_states = self.transform(hidden_states)
        hidden_states = self.decoder(hidden_states)
        return hidden_states
"""






class  __magic_name__						(  unittest.TestCase):
				'''simple docstring'''







				def     _A							(							self:   int		):
											SCREAMING_SNAKE_CASE_   	=    tempfile.mkdtemp()
											os.makedirs(os.path.join(self.transformer_dir		,					'''models/bert/'''		)		)
											SCREAMING_SNAKE_CASE_   	=    self.transformer_dir
											shutil.copy(
											    os.path.join(_lowerCamelCase		,					'''src/transformers/models/bert/modeling_bert.py'''		)		,					os.path.join(self.transformer_dir		,					'''models/bert/modeling_bert.py'''		)		,					)


				def     _A							(							self:   Tuple		):
											SCREAMING_SNAKE_CASE_   	=    '''src/transformers'''
											shutil.rmtree(self.transformer_dir		)


				def     _A							(							self:   Dict		,					_lowerCamelCase:   Optional[int]		,					_lowerCamelCase:   Optional[int]		,					_lowerCamelCase:   str		,					_lowerCamelCase:   str=None		):
											SCREAMING_SNAKE_CASE_   	=    comment + f"\nclass {class_name}(nn.Module):\n" + class_code
											if overwrite_result is not None:
																		SCREAMING_SNAKE_CASE_   	=    comment + f"\nclass {class_name}(nn.Module):\n" + overwrite_result
											SCREAMING_SNAKE_CASE_   	=    black.Mode(target_versions={black.TargetVersion.PYaa}		,					line_length=1_19		)
											SCREAMING_SNAKE_CASE_   	=    black.format_str(_lowerCamelCase		,					mode=_lowerCamelCase		)
											SCREAMING_SNAKE_CASE_   	=    os.path.join(self.transformer_dir		,					'''new_code.py'''		)
											with open(_lowerCamelCase		,					'''w'''		,					newline='''\n'''		) as f:
																		f.write(_lowerCamelCase		)
											if overwrite_result is None:
																		self.assertTrue(len(check_copies.is_copy_consistent(_lowerCamelCase		)		) == 0		)
											else:
																		check_copies.is_copy_consistent(f.name		,					overwrite=_lowerCamelCase		)
																		with open(_lowerCamelCase		,					'''r'''		) as f:
																									self.assertTrue(f.read()		,					_lowerCamelCase		)


				def     _A							(							self:   int		):
											SCREAMING_SNAKE_CASE_   	=    check_copies.find_code_in_transformers('''models.bert.modeling_bert.BertLMPredictionHead'''		)
											self.assertEqual(_lowerCamelCase		,					_lowerCamelCase		)


				def     _A							(							self:   Union[str, Any]		):
											# Base copy consistency
											self.check_copy_consistency(
											    '''# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead'''		,					'''BertLMPredictionHead'''		,					REFERENCE_CODE + '''\n'''		,					)

											# With no empty line at the end
											self.check_copy_consistency(
											    '''# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead'''		,					'''BertLMPredictionHead'''		,					_lowerCamelCase		,					)

											# Copy consistency with rename
											self.check_copy_consistency(
											    '''# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead with Bert->TestModel'''		,					'''TestModelLMPredictionHead'''		,					re.sub('''Bert'''		,					'''TestModel'''		,					_lowerCamelCase		)		,					)

											# Copy consistency with a really long name
											SCREAMING_SNAKE_CASE_   	=    '''TestModelWithAReallyLongNameBecauseSomePeopleLikeThatForSomeReason'''
											self.check_copy_consistency(
											    f"# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead with Bert->{long_class_name}"		,					f"{long_class_name}LMPredictionHead"		,					re.sub('''Bert'''		,					_lowerCamelCase		,					_lowerCamelCase		)		,					)

											# Copy consistency with overwrite
											self.check_copy_consistency(
											    '''# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead with Bert->TestModel'''		,					'''TestModelLMPredictionHead'''		,					_lowerCamelCase		,					overwrite_result=re.sub('''Bert'''		,					'''TestModel'''		,					_lowerCamelCase		)		,					)





				def     _A							(							self:   Any		):
											SCREAMING_SNAKE_CASE_   	=    check_copies.LOCALIZED_READMES['''README_zh-hans.md''']

											SCREAMING_SNAKE_CASE_   	=    (
											    '''1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (from Google Research and the'''
											    ''' Toyota Technological Institute at Chicago) released with the paper [ALBERT: A Lite BERT for'''
											    ''' Self-supervised Learning of Language Representations](https://arxiv.org/abs/1909.11942), by Zhenzhong'''
											    ''' Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut.\n1.'''
											    ''' **[DistilBERT](https://huggingface.co/transformers/model_doc/distilbert.html)** (from HuggingFace),'''
											    ''' released together with the paper [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and'''
											    ''' lighter](https://arxiv.org/abs/1910.01108) by Victor Sanh, Lysandre Debut and Thomas Wolf. The same'''
											    ''' method has been applied to compress GPT2 into'''
											    ''' [DistilGPT2](https://github.com/huggingface/transformers/tree/main/examples/distillation), RoBERTa into'''
											    ''' [DistilRoBERTa](https://github.com/huggingface/transformers/tree/main/examples/distillation),'''
											    ''' Multilingual BERT into'''
											    ''' [DistilmBERT](https://github.com/huggingface/transformers/tree/main/examples/distillation) and a German'''
											    ''' version of DistilBERT.\n1. **[ELECTRA](https://huggingface.co/transformers/model_doc/electra.html)**'''
											    ''' (from Google Research/Stanford University) released with the paper [ELECTRA: Pre-training text encoders'''
											    ''' as discriminators rather than generators](https://arxiv.org/abs/2003.10555) by Kevin Clark, Minh-Thang'''
											    ''' Luong, Quoc V. Le, Christopher D. Manning.'''
											)
											SCREAMING_SNAKE_CASE_   	=    (
											    '''1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (来自 Google Research and the'''
											    ''' Toyota Technological Institute at Chicago) 伴随论文 [ALBERT: A Lite BERT for Self-supervised Learning of'''
											    ''' Language Representations](https://arxiv.org/abs/1909.11942), 由 Zhenzhong Lan, Mingda Chen, Sebastian'''
											    ''' Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut 发布。\n'''
											)
											SCREAMING_SNAKE_CASE_   	=    (
											    '''1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (来自 Google Research and the'''
											    ''' Toyota Technological Institute at Chicago) 伴随论文 [ALBERT: A Lite BERT for Self-supervised Learning of'''
											    ''' Language Representations](https://arxiv.org/abs/1909.11942), 由 Zhenzhong Lan, Mingda Chen, Sebastian'''
											    ''' Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut 发布。\n1.'''
											    ''' **[DistilBERT](https://huggingface.co/transformers/model_doc/distilbert.html)** (来自 HuggingFace) 伴随论文'''
											    ''' [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and'''
											    ''' lighter](https://arxiv.org/abs/1910.01108) 由 Victor Sanh, Lysandre Debut and Thomas Wolf 发布。 The same'''
											    ''' method has been applied to compress GPT2 into'''
											    ''' [DistilGPT2](https://github.com/huggingface/transformers/tree/main/examples/distillation), RoBERTa into'''
											    ''' [DistilRoBERTa](https://github.com/huggingface/transformers/tree/main/examples/distillation),'''
											    ''' Multilingual BERT into'''
											    ''' [DistilmBERT](https://github.com/huggingface/transformers/tree/main/examples/distillation) and a German'''
											    ''' version of DistilBERT.\n1. **[ELECTRA](https://huggingface.co/transformers/model_doc/electra.html)** (来自'''
											    ''' Google Research/Stanford University) 伴随论文 [ELECTRA: Pre-training text encoders as discriminators rather'''
											    ''' than generators](https://arxiv.org/abs/2003.10555) 由 Kevin Clark, Minh-Thang Luong, Quoc V. Le,'''
											    ''' Christopher D. Manning 发布。\n'''
											)

											SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_   	=    check_copies.convert_to_localized_md(
											    _lowerCamelCase		,					_lowerCamelCase		,					localized_readme['''format_model_list''']		)

											self.assertFalse(_lowerCamelCase		)
											self.assertEqual(_lowerCamelCase		,					_lowerCamelCase		)

											SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_   	=    check_copies.convert_to_localized_md(
											    _lowerCamelCase		,					_lowerCamelCase		,					localized_readme['''format_model_list''']		)

											# Check whether the number of models is equal to README.md after conversion.
											self.assertTrue(_lowerCamelCase		)

											SCREAMING_SNAKE_CASE_   	=    (
											    '''1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (from Google Research and the'''
											    ''' Toyota Technological Institute at Chicago) released with the paper [ALBERT: A Lite BERT for'''
											    ''' Self-supervised Learning of Language Representations](https://arxiv.org/abs/1909.11942), by Zhenzhong'''
											    ''' Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut.'''
											)
											SCREAMING_SNAKE_CASE_   	=    (
											    '''1. **[ALBERT](https://huggingface.co/transformers/main/model_doc/albert.html)** (来自 Google Research and'''
											    ''' the Toyota Technological Institute at Chicago) 伴随论文 [ALBERT: A Lite BERT for Self-supervised Learning of'''
											    ''' Language Representations](https://arxiv.org/abs/1909.11942), 由 Zhenzhong Lan, Mingda Chen, Sebastian'''
											    ''' Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut 发布。\n'''
											)
											SCREAMING_SNAKE_CASE_   	=    (
											    '''1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (来自 Google Research and the'''
											    ''' Toyota Technological Institute at Chicago) 伴随论文 [ALBERT: A Lite BERT for Self-supervised Learning of'''
											    ''' Language Representations](https://arxiv.org/abs/1909.11942), 由 Zhenzhong Lan, Mingda Chen, Sebastian'''
											    ''' Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut 发布。\n'''
											)

											SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_   	=    check_copies.convert_to_localized_md(
											    _lowerCamelCase		,					_lowerCamelCase		,					localized_readme['''format_model_list''']		)

											# Check if the model link is synchronized.
											self.assertEqual(_lowerCamelCase		,					_lowerCamelCase		)








import gc
import random
import unittest

import numpy as np
import torch
from transformers import CLIPImageProcessor, CLIPVisionConfig, CLIPVisionModel

from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEImgaImgPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffusers.utils import floats_tensor, load_image, 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  __magic_name__						(  __UpperCAmelCase    ,  unittest.TestCase):
				'''simple docstring'''







				SCREAMING_SNAKE_CASE__					:							List[str]	      =					ShapEImgaImgPipeline
				SCREAMING_SNAKE_CASE__					:							Dict	      =					["image"]
				SCREAMING_SNAKE_CASE__					:							List[Any]	      =					["image"]
				SCREAMING_SNAKE_CASE__					:							List[Any]	      =					[
				    "num_images_per_prompt",
				    "num_inference_steps",
				    "generator",
				    "latents",
				    "guidance_scale",
				    "frame_size",
				    "output_type",
				    "return_dict",
				]
				SCREAMING_SNAKE_CASE__					:							Optional[int]	      =					False


				@property
				def     _A							(							self:   Optional[Any]		):
											return 32


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


				@property
				def     _A							(							self:   List[Any]		):
											return self.time_input_dim * 4


				@property
				def     _A							(							self:   Any		):
											return 8


				@property
				def     _A							(							self:   int		):
											torch.manual_seed(0		)
											SCREAMING_SNAKE_CASE_   	=    CLIPVisionConfig(
											    hidden_size=self.text_embedder_hidden_size		,					image_size=64		,					projection_dim=self.text_embedder_hidden_size		,					intermediate_size=37		,					num_attention_heads=4		,					num_channels=3		,					num_hidden_layers=5		,					patch_size=1		,					)

											SCREAMING_SNAKE_CASE_   	=    CLIPVisionModel(_lowerCamelCase		)
											return model


				@property
				def     _A							(							self:   List[Any]		):
											SCREAMING_SNAKE_CASE_   	=    CLIPImageProcessor(
											    crop_size=2_24		,					do_center_crop=_lowerCamelCase		,					do_normalize=_lowerCamelCase		,					do_resize=_lowerCamelCase		,					image_mean=[0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73]		,					image_std=[0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11]		,					resample=3		,					size=2_24		,					)

											return image_processor


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

											SCREAMING_SNAKE_CASE_   	=    {
											    '''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''',
											    '''embedding_proj_norm_type''': '''layer''',
											    '''encoder_hid_proj_type''': None,
											    '''added_emb_type''': None,
											}

											SCREAMING_SNAKE_CASE_   	=    PriorTransformer(**_lowerCamelCase		)
											return model


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

											SCREAMING_SNAKE_CASE_   	=    {
											    '''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,
											    ),
											}
											SCREAMING_SNAKE_CASE_   	=    ShapERenderer(**_lowerCamelCase		)
											return model


				def     _A							(							self:   Optional[int]		):
											SCREAMING_SNAKE_CASE_   	=    self.dummy_prior
											SCREAMING_SNAKE_CASE_   	=    self.dummy_image_encoder
											SCREAMING_SNAKE_CASE_   	=    self.dummy_image_processor
											SCREAMING_SNAKE_CASE_   	=    self.dummy_renderer

											SCREAMING_SNAKE_CASE_   	=    HeunDiscreteScheduler(
											    beta_schedule='''exp'''		,					num_train_timesteps=10_24		,					prediction_type='''sample'''		,					use_karras_sigmas=_lowerCamelCase		,					clip_sample=_lowerCamelCase		,					clip_sample_range=1.0		,					)
											SCREAMING_SNAKE_CASE_   	=    {
											    '''prior''': prior,
											    '''image_encoder''': image_encoder,
											    '''image_processor''': image_processor,
											    '''renderer''': renderer,
											    '''scheduler''': scheduler,
											}

											return components


				def     _A							(							self:   Optional[Any]		,					_lowerCamelCase:   List[Any]		,					_lowerCamelCase:   Optional[Any]=0		):
											SCREAMING_SNAKE_CASE_   	=    floats_tensor((1, 3, 64, 64)		,					rng=random.Random(_lowerCamelCase		)		).to(_lowerCamelCase		)

											if str(_lowerCamelCase		).startswith('''mps'''		):
																		SCREAMING_SNAKE_CASE_   	=    torch.manual_seed(_lowerCamelCase		)
											else:
																		SCREAMING_SNAKE_CASE_   	=    torch.Generator(device=_lowerCamelCase		).manual_seed(_lowerCamelCase		)
											SCREAMING_SNAKE_CASE_   	=    {
											    '''image''': input_image,
											    '''generator''': generator,
											    '''num_inference_steps''': 1,
											    '''frame_size''': 32,
											    '''output_type''': '''np''',
											}
											return inputs


				def     _A							(							self:   Any		):
											SCREAMING_SNAKE_CASE_   	=    '''cpu'''

											SCREAMING_SNAKE_CASE_   	=    self.get_dummy_components()

											SCREAMING_SNAKE_CASE_   	=    self.pipeline_class(**_lowerCamelCase		)
											SCREAMING_SNAKE_CASE_   	=    pipe.to(_lowerCamelCase		)

											pipe.set_progress_bar_config(disable=_lowerCamelCase		)

											SCREAMING_SNAKE_CASE_   	=    pipe(**self.get_dummy_inputs(_lowerCamelCase		)		)
											SCREAMING_SNAKE_CASE_   	=    output.images[0]
											SCREAMING_SNAKE_CASE_   	=    image[0, -3:, -3:, -1]

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

											SCREAMING_SNAKE_CASE_   	=    np.array(
											    [
											        0.00_03_92_16,
											        0.00_03_92_16,
											        0.00_03_92_16,
											        0.00_03_92_16,
											        0.00_03_92_16,
											        0.00_03_92_16,
											        0.00_03_92_16,
											        0.00_03_92_16,
											        0.00_03_92_16,
											    ]		)

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


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


				def     _A							(							self:   Tuple		):
											SCREAMING_SNAKE_CASE_   	=    torch_device == '''cpu'''
											SCREAMING_SNAKE_CASE_   	=    True
											self._test_inference_batch_single_identical(
											    batch_size=2		,					test_max_difference=_lowerCamelCase		,					relax_max_difference=_lowerCamelCase		,					)





				def     _A							(							self:   Tuple		):
											SCREAMING_SNAKE_CASE_   	=    self.get_dummy_components()
											SCREAMING_SNAKE_CASE_   	=    self.pipeline_class(**_lowerCamelCase		)
											SCREAMING_SNAKE_CASE_   	=    pipe.to(_lowerCamelCase		)
											pipe.set_progress_bar_config(disable=_lowerCamelCase		)

											SCREAMING_SNAKE_CASE_   	=    1
											SCREAMING_SNAKE_CASE_   	=    2

											SCREAMING_SNAKE_CASE_   	=    self.get_dummy_inputs(_lowerCamelCase		)

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

											SCREAMING_SNAKE_CASE_   	=    pipe(**_lowerCamelCase		,					num_images_per_prompt=_lowerCamelCase		)[0]

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






@slow
@require_torch_gpu
class  __magic_name__						(  unittest.TestCase):
				'''simple docstring'''







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





				def     _A							(							self:   Optional[int]		):
											SCREAMING_SNAKE_CASE_   	=    load_image(
											    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main''' '''/shap_e/corgi.png'''		)
											SCREAMING_SNAKE_CASE_   	=    load_numpy(
											    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
											    '''/shap_e/test_shap_e_img2img_out.npy'''		)
											SCREAMING_SNAKE_CASE_   	=    ShapEImgaImgPipeline.from_pretrained('''openai/shap-e-img2img'''		)
											SCREAMING_SNAKE_CASE_   	=    pipe.to(_lowerCamelCase		)
											pipe.set_progress_bar_config(disable=_lowerCamelCase		)

											SCREAMING_SNAKE_CASE_   	=    torch.Generator(device=_lowerCamelCase		).manual_seed(0		)

											SCREAMING_SNAKE_CASE_   	=    pipe(
											    _lowerCamelCase		,					generator=_lowerCamelCase		,					guidance_scale=3.0		,					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		)


