




"""simple docstring"""





import gc
import unittest

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

from diffusers import AutoencoderKL, DDIMScheduler, LDMTextToImagePipeline, UNetaDConditionModel
from diffusers.utils.testing_utils import (
    enable_full_determinism,
    load_numpy,
    nightly,
    require_torch_gpu,
    slow,
    torch_device,
)

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()



class      a__				(	__magic_name__							,    unittest.TestCase   ):
			lowercase_    =	LDMTextToImagePipeline
			lowercase_    =	TEXT_TO_IMAGE_PARAMS - {
			    "negative_prompt",
			    "negative_prompt_embeds",
			    "cross_attention_kwargs",
			    "prompt_embeds",
			}
			lowercase_    =	PipelineTesterMixin.required_optional_params - {
			    "num_images_per_prompt",
			    "callback",
			    "callback_steps",
			}
			lowercase_    =	TEXT_TO_IMAGE_BATCH_PARAMS
			lowercase_    =	False


			def       a_     (      self      :    Union[str, Any]):


				"""simple docstring"""




				torch.manual_seed(0)
				__UpperCAmelCase	: str		=					UNetaDConditionModel(
				    block_out_channels=(32, 64)      ,  layers_per_block=2      ,  sample_size=32      ,  in_channels=4      ,  out_channels=4      ,  down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")      ,  up_block_types=("CrossAttnUpBlock2D", "UpBlock2D")      ,  cross_attention_dim=32      ,  )
				__UpperCAmelCase	: Dict		=					DDIMScheduler(
				    beta_start=0.00085      ,  beta_end=0.012      ,  beta_schedule="scaled_linear"      ,  clip_sample=UpperCamelCase_      ,  set_alpha_to_one=UpperCamelCase_      ,  )
				torch.manual_seed(0)
				__UpperCAmelCase	: Optional[int]		=					AutoencoderKL(
				    block_out_channels=(32, 64)      ,  in_channels=3      ,  out_channels=3      ,  down_block_types=("DownEncoderBlock2D", "DownEncoderBlock2D")      ,  up_block_types=("UpDecoderBlock2D", "UpDecoderBlock2D")      ,  latent_channels=4      ,  )
				torch.manual_seed(0)
				__UpperCAmelCase	: List[str]		=					CLIPTextConfig(
				    bos_token_id=0      ,  eos_token_id=2      ,  hidden_size=32      ,  intermediate_size=37      ,  layer_norm_eps=1e-05      ,  num_attention_heads=4      ,  num_hidden_layers=5      ,  pad_token_id=1      ,  vocab_size=1000      ,  )
				__UpperCAmelCase	: List[str]		=					CLIPTextModel(UpperCamelCase_)
				__UpperCAmelCase	: Union[str, Any]		=					CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")

				__UpperCAmelCase	: Optional[int]		=					{
				    "unet": unet,
				    "scheduler": scheduler,
				    "vqvae": vae,
				    "bert": text_encoder,
				    "tokenizer": tokenizer,
				}
				return components


			def       a_     (      self      :    Tuple      ,  UpperCamelCase_      :    Optional[Any]      ,  UpperCamelCase_      :    Tuple=0):


				"""simple docstring"""




				if str(UpperCamelCase_).startswith("mps"):
					__UpperCAmelCase	: str		=					torch.manual_seed(UpperCamelCase_)
				else:
					__UpperCAmelCase	: Tuple		=					torch.Generator(device=UpperCamelCase_).manual_seed(UpperCamelCase_)
				__UpperCAmelCase	: Tuple		=					{
				    "prompt": "A painting of a squirrel eating a burger",
				    "generator": generator,
				    "num_inference_steps": 2,
				    "guidance_scale": 6.0,
				    "output_type": "numpy",
				}
				return inputs


			def       a_     (      self      :    Tuple):


				"""simple docstring"""




				__UpperCAmelCase	: Any		=					"cpu"  # ensure determinism for the device-dependent torch.Generator

				__UpperCAmelCase	: Union[str, Any]		=					self.get_dummy_components()
				__UpperCAmelCase	: Union[str, Any]		=					LDMTextToImagePipeline(**UpperCamelCase_)
				pipe.to(UpperCamelCase_)
				pipe.set_progress_bar_config(disable=UpperCamelCase_)

				__UpperCAmelCase	: int		=					self.get_dummy_inputs(UpperCamelCase_)
				__UpperCAmelCase	: Dict		=					pipe(**UpperCamelCase_).images
				__UpperCAmelCase	: List[Any]		=					image[0, -3:, -3:, -1]

				assert image.shape == (1, 16, 16, 3)
				__UpperCAmelCase	: List[str]		=					np.array([0.6101, 0.6156, 0.5622, 0.4895, 0.6661, 0.3804, 0.5748, 0.6136, 0.5014])

				assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-3






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


			def       a_     (      self      :    Optional[int]):


				"""simple docstring"""




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


			def       a_     (      self      :    List[str]      ,  UpperCamelCase_      :    str      ,  UpperCamelCase_      :    int=torch.floataa      ,  UpperCamelCase_      :    Dict=0):


				"""simple docstring"""




				__UpperCAmelCase	: Optional[Any]		=					torch.manual_seed(UpperCamelCase_)
				__UpperCAmelCase	: Optional[Any]		=					np.random.RandomState(UpperCamelCase_).standard_normal((1, 4, 32, 32))
				__UpperCAmelCase	: str		=					torch.from_numpy(UpperCamelCase_).to(device=UpperCamelCase_      ,  dtype=UpperCamelCase_)
				__UpperCAmelCase	: Tuple		=					{
				    "prompt": "A painting of a squirrel eating a burger",
				    "latents": latents,
				    "generator": generator,
				    "num_inference_steps": 3,
				    "guidance_scale": 6.0,
				    "output_type": "numpy",
				}
				return inputs


			def       a_     (      self      :    Optional[int]):


				"""simple docstring"""




				__UpperCAmelCase	: str		=					LDMTextToImagePipeline.from_pretrained("CompVis/ldm-text2im-large-256").to(UpperCamelCase_)
				pipe.set_progress_bar_config(disable=UpperCamelCase_)

				__UpperCAmelCase	: Optional[int]		=					self.get_inputs(UpperCamelCase_)
				__UpperCAmelCase	: Union[str, Any]		=					pipe(**UpperCamelCase_).images
				__UpperCAmelCase	: Dict		=					image[0, -3:, -3:, -1].flatten()

				assert image.shape == (1, 256, 256, 3)
				__UpperCAmelCase	: List[Any]		=					np.array([0.51825, 0.52850, 0.52543, 0.54258, 0.52304, 0.52569, 0.54363, 0.55276, 0.56878])
				__UpperCAmelCase	: List[Any]		=					np.abs(expected_slice - image_slice).max()
				assert max_diff < 1e-3






@nightly
@require_torch_gpu
class      a__				(	unittest.TestCase   ):


			def       a_     (      self      :    Optional[Any]):


				"""simple docstring"""




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


			def       a_     (      self      :    Tuple      ,  UpperCamelCase_      :    Tuple      ,  UpperCamelCase_      :    int=torch.floataa      ,  UpperCamelCase_      :    Optional[int]=0):


				"""simple docstring"""




				__UpperCAmelCase	: str		=					torch.manual_seed(UpperCamelCase_)
				__UpperCAmelCase	: Tuple		=					np.random.RandomState(UpperCamelCase_).standard_normal((1, 4, 32, 32))
				__UpperCAmelCase	: Optional[Any]		=					torch.from_numpy(UpperCamelCase_).to(device=UpperCamelCase_      ,  dtype=UpperCamelCase_)
				__UpperCAmelCase	: Dict		=					{
				    "prompt": "A painting of a squirrel eating a burger",
				    "latents": latents,
				    "generator": generator,
				    "num_inference_steps": 50,
				    "guidance_scale": 6.0,
				    "output_type": "numpy",
				}
				return inputs


			def       a_     (      self      :    Dict):


				"""simple docstring"""




				__UpperCAmelCase	: str		=					LDMTextToImagePipeline.from_pretrained("CompVis/ldm-text2im-large-256").to(UpperCamelCase_)
				pipe.set_progress_bar_config(disable=UpperCamelCase_)

				__UpperCAmelCase	: Union[str, Any]		=					self.get_inputs(UpperCamelCase_)
				__UpperCAmelCase	: List[str]		=					pipe(**UpperCamelCase_).images[0]

				__UpperCAmelCase	: int		=					load_numpy(
				    "https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/ldm_text2img/ldm_large_256_ddim.npy")
				__UpperCAmelCase	: int		=					np.abs(expected_image - image).max()
				assert max_diff < 1e-3








"""simple docstring"""





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

import black


A   =       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.
A   =       """    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      a__				(	unittest.TestCase   ):


			def       a_     (      self      :    Optional[Any]):


						"""simple docstring"""




						__UpperCAmelCase	: List[Any]		=					tempfile.mkdtemp()
						os.makedirs(os.path.join(self.transformer_dir      ,  "models/bert/"))
						__UpperCAmelCase	: Tuple		=					self.transformer_dir
						shutil.copy(
						    os.path.join(UpperCamelCase_      ,  "src/transformers/models/bert/modeling_bert.py")      ,  os.path.join(self.transformer_dir      ,  "models/bert/modeling_bert.py")      ,  )


			def       a_     (      self      :    int):


						"""simple docstring"""




						__UpperCAmelCase	: Optional[Any]		=					"src/transformers"
						shutil.rmtree(self.transformer_dir)


			def       a_     (      self      :    str      ,  UpperCamelCase_      :    str      ,  UpperCamelCase_      :    int      ,  UpperCamelCase_      :    Optional[int]      ,  UpperCamelCase_      :    List[str]=None):


						"""simple docstring"""




						__UpperCAmelCase	: str		=					comment + F"\nclass {class_name}(nn.Module):\n" + class_code
						if overwrite_result is not None:
									__UpperCAmelCase	: str		=					comment + F"\nclass {class_name}(nn.Module):\n" + overwrite_result
						__UpperCAmelCase	: Optional[int]		=					black.Mode(target_versions={black.TargetVersion.PYaa}      ,  line_length=119)
						__UpperCAmelCase	: Dict		=					black.format_str(UpperCamelCase_      ,  mode=UpperCamelCase_)
						__UpperCAmelCase	: Dict		=					os.path.join(self.transformer_dir      ,  "new_code.py")
						with open(UpperCamelCase_      ,  "w"      ,  newline="\n") as f:
									f.write(UpperCamelCase_)
						if overwrite_result is None:
									self.assertTrue(len(check_copies.is_copy_consistent(UpperCamelCase_)) == 0)
						else:
									check_copies.is_copy_consistent(f.name      ,  overwrite=UpperCamelCase_)
									with open(UpperCamelCase_      ,  "r") as f:
												self.assertTrue(f.read()      ,  UpperCamelCase_)


			def       a_     (      self      :    Tuple):


						"""simple docstring"""




						__UpperCAmelCase	: Any		=					check_copies.find_code_in_transformers("models.bert.modeling_bert.BertLMPredictionHead")
						self.assertEqual(UpperCamelCase_      ,  UpperCamelCase_)


			def       a_     (      self      :    Optional[int]):


						"""simple docstring"""




						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"      ,  UpperCamelCase_      ,  )

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

						# Copy consistency with a really long name
						__UpperCAmelCase	: Optional[Any]		=					"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"      ,  UpperCamelCase_      ,  UpperCamelCase_)      ,  )

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


			def       a_     (      self      :    Dict):


						"""simple docstring"""




						__UpperCAmelCase	: List[str]		=					check_copies.LOCALIZED_READMES["README_zh-hans.md"]

						__UpperCAmelCase	: str		=					(
						    "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."
						)
						__UpperCAmelCase	: List[str]		=					(
						    "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"
						)
						__UpperCAmelCase	: str		=					(
						    "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"
						)

						__UpperCAmelCase	,     __UpperCAmelCase	: Any		=					check_copies.convert_to_localized_md(
						    UpperCamelCase_      ,  UpperCamelCase_      ,  localized_readme["format_model_list"])

						self.assertFalse(UpperCamelCase_)
						self.assertEqual(UpperCamelCase_      ,  UpperCamelCase_)

						__UpperCAmelCase	,     __UpperCAmelCase	: int		=					check_copies.convert_to_localized_md(
						    UpperCamelCase_      ,  UpperCamelCase_      ,  localized_readme["format_model_list"])

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

						__UpperCAmelCase	: str		=					(
						    "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."
						)
						__UpperCAmelCase	: Optional[int]		=					(
						    "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"
						)
						__UpperCAmelCase	: int		=					(
						    "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"
						)

						__UpperCAmelCase	,     __UpperCAmelCase	: List[str]		=					check_copies.convert_to_localized_md(
						    UpperCamelCase_      ,  UpperCamelCase_      ,  localized_readme["format_model_list"])

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


