




'''simple docstring'''






from typing import List, Optional, Union

import torch
from transformers import (
    XLMRobertaTokenizer,
)

from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDIMScheduler, DDPMScheduler
from ...utils import (
    is_accelerate_available,
    is_accelerate_version,
    logging,
    randn_tensor,
    replace_example_docstring,
)
from .text_encoder import MultilingualCLIP


A_			      =		logging.get_logger(__name__)  # pylint: disable=invalid-name

A_			      =		"\n    Examples:\n        ```py\n        >>> from diffusers import KandinskyPipeline, KandinskyPriorPipeline\n        >>> import torch\n\n        >>> pipe_prior = KandinskyPriorPipeline.from_pretrained(\"kandinsky-community/Kandinsky-2-1-prior\")\n        >>> pipe_prior.to(\"cuda\")\n\n        >>> prompt = \"red cat, 4k photo\"\n        >>> out = pipe_prior(prompt)\n        >>> image_emb = out.image_embeds\n        >>> negative_image_emb = out.negative_image_embeds\n\n        >>> pipe = KandinskyPipeline.from_pretrained(\"kandinsky-community/kandinsky-2-1\")\n        >>> pipe.to(\"cuda\")\n\n        >>> image = pipe(\n        ...     prompt,\n        ...     image_embeds=image_emb,\n        ...     negative_image_embeds=negative_image_emb,\n        ...     height=768,\n        ...     width=768,\n        ...     num_inference_steps=100,\n        ... ).images\n\n        >>> image[0].save(\"cat.png\")\n        ```\n"






def 		UpperCamelCase__      (		__SCREAMING_SNAKE_CASE				,      __SCREAMING_SNAKE_CASE				,      __SCREAMING_SNAKE_CASE=8		)    ->   Optional[int]:
      snake_case__       :   List[str]	      =    h // scale_factor**2
      if h % scale_factor**2 != 0:
            new_h += 1
      snake_case__       :   Dict	      =    w // scale_factor**2
      if w % scale_factor**2 != 0:
            new_w += 1
      return new_h * scale_factor, new_w * scale_factor






class        lowercase_      (						lowerCAmelCase_							):





     def __init__(     self     :					Optional[int]     ,							__lowerCamelCase     :					MultilingualCLIP     ,							__lowerCamelCase     :					XLMRobertaTokenizer     ,							__lowerCamelCase     :					UNetaDConditionModel     ,							__lowerCamelCase     :					Union[DDIMScheduler, DDPMScheduler]     ,							__lowerCamelCase     :					VQModel     ,							):
           super().__init__()

           self.register_modules(
               text_encoder=__lowerCamelCase     ,							tokenizer=__lowerCamelCase     ,							unet=__lowerCamelCase     ,							scheduler=__lowerCamelCase     ,							movq=__lowerCamelCase     ,							)
           snake_case__       :   Tuple	      =    2 ** (len(self.movq.config.block_out_channels			) - 1)





     def 			_lowerCAmelCase      (     self     :					List[Any]     ,							__lowerCamelCase     :					Any     ,							__lowerCamelCase     :					Union[str, Any]     ,							__lowerCamelCase     :					Any     ,							__lowerCamelCase     :					Union[str, Any]     ,							__lowerCamelCase     :					List[str]     ,							__lowerCamelCase     :					Optional[Any]			):
           if latents is None:
                 snake_case__       :   Optional[Any]	      =    randn_tensor(__lowerCamelCase     ,							generator=__lowerCamelCase     ,							device=__lowerCamelCase     ,							dtype=__lowerCamelCase			)
           else:
                 if latents.shape != shape:
                       raise ValueError(F"Unexpected latents shape, got {latents.shape}, expected {shape}"			)
                 snake_case__       :   Tuple	      =    latents.to(__lowerCamelCase			)

           snake_case__       :   List[Any]	      =    latents * scheduler.init_noise_sigma
           return latents





     def 			_lowerCAmelCase      (     self     :					str     ,							__lowerCamelCase     :					Optional[int]     ,							__lowerCamelCase     :					List[str]     ,							__lowerCamelCase     :					str     ,							__lowerCamelCase     :					Dict     ,							__lowerCamelCase     :					Optional[Any]=None     ,							):
           snake_case__       :   Optional[Any]	      =    len(__lowerCamelCase			) if isinstance(__lowerCamelCase     ,							__lowerCamelCase			) else 1
           # get prompt text embeddings
           snake_case__       :   str	      =    self.tokenizer(
               __lowerCamelCase     ,							padding='max_length'     ,							truncation=__lowerCamelCase     ,							max_length=77     ,							return_attention_mask=__lowerCamelCase     ,							add_special_tokens=__lowerCamelCase     ,							return_tensors='pt'     ,							)

           snake_case__       :   int	      =    text_inputs.input_ids
           snake_case__       :   List[str]	      =    self.tokenizer(__lowerCamelCase     ,							padding='longest'     ,							return_tensors='pt'			).input_ids

           if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(__lowerCamelCase     ,							__lowerCamelCase			):
                 snake_case__       :   Union[str, Any]	      =    self.tokenizer.batch_decode(untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]			)
                 logger.warning(
                     'The following part of your input was truncated because CLIP can only handle sequences up to'
                     F" {self.tokenizer.model_max_length} tokens: {removed_text}"			)

           snake_case__       :   Dict	      =    text_input_ids.to(__lowerCamelCase			)
           snake_case__       :   List[str]	      =    text_inputs.attention_mask.to(__lowerCamelCase			)

           snake_case__      ,							snake_case__       :   Any	      =    self.text_encoder(
               input_ids=__lowerCamelCase     ,							attention_mask=__lowerCamelCase			)

           snake_case__       :   Any	      =    prompt_embeds.repeat_interleave(__lowerCamelCase     ,							dim=0			)
           snake_case__       :   Any	      =    text_encoder_hidden_states.repeat_interleave(__lowerCamelCase     ,							dim=0			)
           snake_case__       :   int	      =    text_mask.repeat_interleave(__lowerCamelCase     ,							dim=0			)

           if do_classifier_free_guidance:
                 snake_case__       :   List[str]
                 if negative_prompt is None:
                       snake_case__       :   str	      =    [''] * batch_size
                 elif type(__lowerCamelCase			) is not type(__lowerCamelCase			):
                       raise TypeError(
                           F"`negative_prompt` should be the same type to `prompt`, but got {type(__lowerCamelCase			)} !="
                           F" {type(__lowerCamelCase			)}."			)
                 elif isinstance(__lowerCamelCase     ,							__lowerCamelCase			):
                       snake_case__       :   List[str]	      =    [negative_prompt]
                 elif batch_size != len(__lowerCamelCase			):
                       raise ValueError(
                           F"`negative_prompt`: {negative_prompt} has batch size {len(__lowerCamelCase			)}, but `prompt`:"
                           F" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
                           ' the batch size of `prompt`.'			)
                 else:
                       snake_case__       :   List[str]	      =    negative_prompt

                 snake_case__       :   int	      =    self.tokenizer(
                     __lowerCamelCase     ,							padding='max_length'     ,							max_length=77     ,							truncation=__lowerCamelCase     ,							return_attention_mask=__lowerCamelCase     ,							add_special_tokens=__lowerCamelCase     ,							return_tensors='pt'     ,							)
                 snake_case__       :   Optional[int]	      =    uncond_input.input_ids.to(__lowerCamelCase			)
                 snake_case__       :   str	      =    uncond_input.attention_mask.to(__lowerCamelCase			)

                 snake_case__      ,							snake_case__       :   Tuple	      =    self.text_encoder(
                     input_ids=__lowerCamelCase     ,							attention_mask=__lowerCamelCase			)

                 # duplicate unconditional embeddings for each generation per prompt, using mps friendly method

                 snake_case__       :   int	      =    negative_prompt_embeds.shape[1]
                 snake_case__       :   Tuple	      =    negative_prompt_embeds.repeat(1     ,							__lowerCamelCase			)
                 snake_case__       :   List[str]	      =    negative_prompt_embeds.view(batch_size * num_images_per_prompt     ,							__lowerCamelCase			)

                 snake_case__       :   Dict	      =    uncond_text_encoder_hidden_states.shape[1]
                 snake_case__       :   Dict	      =    uncond_text_encoder_hidden_states.repeat(1     ,							__lowerCamelCase     ,							1			)
                 snake_case__       :   List[Any]	      =    uncond_text_encoder_hidden_states.view(
                     batch_size * num_images_per_prompt     ,							__lowerCamelCase     ,							-1			)
                 snake_case__       :   Dict	      =    uncond_text_mask.repeat_interleave(__lowerCamelCase     ,							dim=0			)

                 # done duplicates

                 # For classifier free guidance, we need to do two forward passes.
                 # Here we concatenate the unconditional and text embeddings into a single batch
                 # to avoid doing two forward passes
                 snake_case__       :   Dict	      =    torch.cat([negative_prompt_embeds, prompt_embeds]			)
                 snake_case__       :   List[str]	      =    torch.cat([uncond_text_encoder_hidden_states, text_encoder_hidden_states]			)

                 snake_case__       :   Optional[Any]	      =    torch.cat([uncond_text_mask, text_mask]			)

           return prompt_embeds, text_encoder_hidden_states, text_mask





     def 			_lowerCAmelCase      (     self     :					int     ,							__lowerCamelCase     :					Union[str, Any]=0			):
           if is_accelerate_available():
                 from accelerate import cpu_offload
           else:
                 raise ImportError('Please install accelerate via `pip install accelerate`'			)

           snake_case__       :   List[str]	      =    torch.device(F"cuda:{gpu_id}"			)

           snake_case__       :   Tuple	      =    [
               self.unet,
               self.text_encoder,
               self.movq,
           ]
           for cpu_offloaded_model in models:
                 if cpu_offloaded_model is not None:
                       cpu_offload(__lowerCamelCase     ,							__lowerCamelCase			)





     def 			_lowerCAmelCase      (     self     :					List[Any]     ,							__lowerCamelCase     :					List[str]=0			):
           if is_accelerate_available() and is_accelerate_version('>='     ,							'0.17.0.dev0'			):
                 from accelerate import cpu_offload_with_hook
           else:
                 raise ImportError('`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.'			)

           snake_case__       :   Optional[int]	      =    torch.device(F"cuda:{gpu_id}"			)

           if self.device.type != "cpu":
                 self.to('cpu'     ,							silence_dtype_warnings=__lowerCamelCase			)
                 torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)

           snake_case__       :   Union[str, Any]	      =    None
           for cpu_offloaded_model in [self.text_encoder, self.unet, self.movq]:
                 snake_case__      ,							snake_case__       :   Tuple	      =    cpu_offload_with_hook(__lowerCamelCase     ,							__lowerCamelCase     ,							prev_module_hook=__lowerCamelCase			)

           if self.safety_checker is not None:
                 snake_case__      ,							snake_case__       :   Dict	      =    cpu_offload_with_hook(self.safety_checker     ,							__lowerCamelCase     ,							prev_module_hook=__lowerCamelCase			)

           # We'll offload the last model manually.
           snake_case__       :   Any	      =    hook





     @property
     # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
     def 			_lowerCAmelCase      (     self     :					Optional[Any]			):
           if not hasattr(self.unet     ,							'_hf_hook'			):
                 return self.device
           for module in self.unet.modules():
                 if (
                     hasattr(__lowerCamelCase     ,							'_hf_hook'			)
                     and hasattr(module._hf_hook     ,							'execution_device'			)
                     and module._hf_hook.execution_device is not None
                 ):
                       return torch.device(module._hf_hook.execution_device			)
           return self.device





     @torch.no_grad()
     @replace_example_docstring(__lowerCamelCase			)
     def __call__(     self     :					Union[str, Any]     ,							__lowerCamelCase     :					Union[str, List[str]]     ,							__lowerCamelCase     :					Union[torch.FloatTensor, List[torch.FloatTensor]]     ,							__lowerCamelCase     :					Union[torch.FloatTensor, List[torch.FloatTensor]]     ,							__lowerCamelCase     :					Optional[Union[str, List[str]]] = None     ,							__lowerCamelCase     :					int = 512     ,							__lowerCamelCase     :					int = 512     ,							__lowerCamelCase     :					int = 100     ,							__lowerCamelCase     :					float = 4.0     ,							__lowerCamelCase     :					int = 1     ,							__lowerCamelCase     :					Optional[Union[torch.Generator, List[torch.Generator]]] = None     ,							__lowerCamelCase     :					Optional[torch.FloatTensor] = None     ,							__lowerCamelCase     :					Optional[str] = "pil"     ,							__lowerCamelCase     :					bool = True     ,							):

           if isinstance(__lowerCamelCase     ,							__lowerCamelCase			):
                 snake_case__       :   str	      =    1
           elif isinstance(__lowerCamelCase     ,							__lowerCamelCase			):
                 snake_case__       :   List[str]	      =    len(__lowerCamelCase			)
           else:
                 raise ValueError(F"`prompt` has to be of type `str` or `list` but is {type(__lowerCamelCase			)}"			)

           snake_case__       :   Optional[int]	      =    self._execution_device

           snake_case__       :   Optional[int]	      =    batch_size * num_images_per_prompt
           snake_case__       :   Dict	      =    guidance_scale > 1.0

           snake_case__      ,							snake_case__      ,							snake_case__       :   Any	      =    self._encode_prompt(
               __lowerCamelCase     ,							__lowerCamelCase     ,							__lowerCamelCase     ,							__lowerCamelCase     ,							__lowerCamelCase			)

           if isinstance(__lowerCamelCase     ,							__lowerCamelCase			):
                 snake_case__       :   Optional[Any]	      =    torch.cat(__lowerCamelCase     ,							dim=0			)
           if isinstance(__lowerCamelCase     ,							__lowerCamelCase			):
                 snake_case__       :   str	      =    torch.cat(__lowerCamelCase     ,							dim=0			)

           if do_classifier_free_guidance:
                 snake_case__       :   Union[str, Any]	      =    image_embeds.repeat_interleave(__lowerCamelCase     ,							dim=0			)
                 snake_case__       :   Tuple	      =    negative_image_embeds.repeat_interleave(__lowerCamelCase     ,							dim=0			)

           snake_case__       :   Union[str, Any]	      =    torch.cat([negative_image_embeds, image_embeds]     ,							dim=0			).to(
               dtype=prompt_embeds.dtype     ,							device=__lowerCamelCase			)

           self.scheduler.set_timesteps(__lowerCamelCase     ,							device=__lowerCamelCase			)
           snake_case__       :   Optional[int]	      =    self.scheduler.timesteps

           snake_case__       :   Tuple	      =    self.unet.config.in_channels

           snake_case__      ,							snake_case__       :   int	      =    get_new_h_w(__lowerCamelCase     ,							__lowerCamelCase     ,							self.movq_scale_factor			)

           # create initial latent
           snake_case__       :   List[str]	      =    self.prepare_latents(
               (batch_size, num_channels_latents, height, width)     ,							text_encoder_hidden_states.dtype     ,							__lowerCamelCase     ,							__lowerCamelCase     ,							__lowerCamelCase     ,							self.scheduler     ,							)

           for i, t in enumerate(self.progress_bar(__lowerCamelCase			)			):
                 # expand the latents if we are doing classifier free guidance
                 snake_case__       :   List[Any]	      =    torch.cat([latents] * 2			) if do_classifier_free_guidance else latents

                 snake_case__       :   str	      =    {'text_embeds': prompt_embeds, 'image_embeds': image_embeds}
                 snake_case__       :   Union[str, Any]	      =    self.unet(
                     sample=__lowerCamelCase     ,							timestep=__lowerCamelCase     ,							encoder_hidden_states=__lowerCamelCase     ,							added_cond_kwargs=__lowerCamelCase     ,							return_dict=__lowerCamelCase     ,							)[0]

                 if do_classifier_free_guidance:
                       snake_case__      ,							snake_case__       :   Tuple	      =    noise_pred.split(latents.shape[1]     ,							dim=1			)
                       snake_case__      ,							snake_case__       :   int	      =    noise_pred.chunk(2			)
                       snake_case__      ,							snake_case__       :   Tuple	      =    variance_pred.chunk(2			)
                       snake_case__       :   List[str]	      =    noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
                       snake_case__       :   Optional[int]	      =    torch.cat([noise_pred, variance_pred_text]     ,							dim=1			)

                 if not (
                     hasattr(self.scheduler.config     ,							'variance_type'			)
                     and self.scheduler.config.variance_type in ["learned", "learned_range"]
                 ):
                       snake_case__      ,							snake_case__       :   Any	      =    noise_pred.split(latents.shape[1]     ,							dim=1			)

                 # compute the previous noisy sample x_t -> x_t-1
                 snake_case__       :   str	      =    self.scheduler.step(
                     __lowerCamelCase     ,							__lowerCamelCase     ,							__lowerCamelCase     ,							generator=__lowerCamelCase     ,							).prev_sample
           # post-processing
           snake_case__       :   Any	      =    self.movq.decode(__lowerCamelCase     ,							force_not_quantize=__lowerCamelCase			)['sample']

           if output_type not in ["pt", "np", "pil"]:
                 raise ValueError(F"Only the output types `pt`, `pil` and `np` are supported not output_type={output_type}"			)

           if output_type in ["np", "pil"]:
                 snake_case__       :   Any	      =    image * 0.5 + 0.5
                 snake_case__       :   Dict	      =    image.clamp(0     ,							1			)
                 snake_case__       :   int	      =    image.cpu().permute(0     ,							2     ,							3     ,							1			).float().numpy()

           if output_type == "pil":
                 snake_case__       :   Dict	      =    self.numpy_to_pil(__lowerCamelCase			)

           if not return_dict:
                 return (image,)

           return ImagePipelineOutput(images=__lowerCamelCase			)






'''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):\n        super().__init__()\n        self.transform = BertPredictionHeadTransform(config)\n\n        # The output weights are the same as the input embeddings, but there is\n        # an output-only bias for each token.\n        self.decoder = nn.Linear(config.hidden_size, config.vocab_size, bias=False)\n\n        self.bias = nn.Parameter(torch.zeros(config.vocab_size))\n\n        # Need a link between the two variables so that the bias is correctly resized with `resize_token_embeddings`\n        self.decoder.bias = self.bias\n\n    def forward(self, hidden_states):\n        hidden_states = self.transform(hidden_states)\n        hidden_states = self.decoder(hidden_states)\n        return hidden_states\n"






class        lowercase_      (						unittest.TestCase							):





     def 			_lowerCAmelCase      (     self     :					Union[str, Any]			):
           snake_case__       :   str	      =    tempfile.mkdtemp()
           os.makedirs(os.path.join(self.transformer_dir     ,							'models/bert/'			)			)
           snake_case__       :   Optional[Any]	      =    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 			_lowerCAmelCase      (     self     :					int			):
           snake_case__       :   Union[str, Any]	      =    'src/transformers'
           shutil.rmtree(self.transformer_dir			)





     def 			_lowerCAmelCase      (     self     :					Tuple     ,							__lowerCamelCase     :					Tuple     ,							__lowerCamelCase     :					Dict     ,							__lowerCamelCase     :					Tuple     ,							__lowerCamelCase     :					List[Any]=None			):
           snake_case__       :   Optional[int]	      =    comment + F"\nclass {class_name}(nn.Module):\n" + class_code
           if overwrite_result is not None:
                 snake_case__       :   List[Any]	      =    comment + F"\nclass {class_name}(nn.Module):\n" + overwrite_result
           snake_case__       :   Optional[int]	      =    black.Mode(target_versions={black.TargetVersion.PYaa}     ,							line_length=119			)
           snake_case__       :   Tuple	      =    black.format_str(__lowerCamelCase     ,							mode=__lowerCamelCase			)
           snake_case__       :   Union[str, Any]	      =    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 			_lowerCAmelCase      (     self     :					Tuple			):
           snake_case__       :   List[str]	      =    check_copies.find_code_in_transformers('models.bert.modeling_bert.BertLMPredictionHead'			)
           self.assertEqual(__lowerCamelCase     ,							__lowerCamelCase			)





     def 			_lowerCAmelCase      (     self     :					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
           snake_case__       :   Union[str, 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'     ,							__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 			_lowerCAmelCase      (     self     :					Union[str, Any]			):
           snake_case__       :   List[str]	      =    check_copies.LOCALIZED_READMES['README_zh-hans.md']

           snake_case__       :   List[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.'
           )
           snake_case__       :   Union[str, Any]	      =    (
               '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'
           )
           snake_case__       :   Tuple	      =    (
               '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'
           )

           snake_case__      ,							snake_case__       :   Optional[Any]	      =    check_copies.convert_to_localized_md(
               __lowerCamelCase     ,							__lowerCamelCase     ,							localized_readme['format_model_list']			)

           self.assertFalse(__lowerCamelCase			)
           self.assertEqual(__lowerCamelCase     ,							__lowerCamelCase			)

           snake_case__      ,							snake_case__       :   Any	      =    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			)

           snake_case__       :   Optional[Any]	      =    (
               '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.'
           )
           snake_case__       :   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'
           )
           snake_case__       :   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'
           )

           snake_case__      ,							snake_case__       :   Any	      =    check_copies.convert_to_localized_md(
               __lowerCamelCase     ,							__lowerCamelCase     ,							localized_readme['format_model_list']			)

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