

from __future__ import annotations

import unittest

from transformers import is_tf_available, is_torch_available
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, SMALL_MODEL_IDENTIFIER, is_pt_tf_cross_test, slow


if is_tf_available():
					from transformers import (
					    AutoConfig,
					    BertConfig,
					    GPTaConfig,
					    TaConfig,
					    TFAutoModel,
					    TFAutoModelForCausalLM,
					    TFAutoModelForMaskedLM,
					    TFAutoModelForPreTraining,
					    TFAutoModelForQuestionAnswering,
					    TFAutoModelForSeqaSeqLM,
					    TFAutoModelForSequenceClassification,
					    TFAutoModelWithLMHead,
					    TFBertForMaskedLM,
					    TFBertForPreTraining,
					    TFBertForQuestionAnswering,
					    TFBertForSequenceClassification,
					    TFBertModel,
					    TFGPTaLMHeadModel,
					    TFRobertaForMaskedLM,
					    TFTaForConditionalGeneration,
					)
					from transformers.models.bert.modeling_tf_bert import TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST
					from transformers.models.gpta.modeling_tf_gpta import TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST
					from transformers.models.ta.modeling_tf_ta import TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST

if is_torch_available():
					from transformers import (
					    AutoModel,
					    AutoModelForCausalLM,
					    AutoModelForMaskedLM,
					    AutoModelForPreTraining,
					    AutoModelForQuestionAnswering,
					    AutoModelForSeqaSeqLM,
					    AutoModelForSequenceClassification,
					    AutoModelWithLMHead,
					    BertForMaskedLM,
					    BertForPreTraining,
					    BertForQuestionAnswering,
					    BertForSequenceClassification,
					    BertModel,
					    GPTaLMHeadModel,
					    RobertaForMaskedLM,
					    TaForConditionalGeneration,
					)

@is_pt_tf_cross_test
class 			a					(		unittest.TestCase			):


						@slow
						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										for model_name in ["bert-base-uncased"]:
														_UpperCAmelCase    :						Optional[Any]							=      AutoConfig.from_pretrained(A_		)
														self.assertIsNotNone(A_		)
														self.assertIsInstance(A_  ,   A_		)

														_UpperCAmelCase    :						Any							=      TFAutoModel.from_pretrained(A_  ,   from_pt=A_		)
														self.assertIsNotNone(A_		)
														self.assertIsInstance(A_  ,   A_		)

														_UpperCAmelCase    :						List[Any]							=      AutoModel.from_pretrained(A_  ,   from_tf=A_		)
														self.assertIsNotNone(A_		)
														self.assertIsInstance(A_  ,   A_		)


						@slow
						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										for model_name in ["bert-base-uncased"]:
														_UpperCAmelCase    :						List[str]							=      AutoConfig.from_pretrained(A_		)
														self.assertIsNotNone(A_		)
														self.assertIsInstance(A_  ,   A_		)

														_UpperCAmelCase    :						List[Any]							=      TFAutoModelForPreTraining.from_pretrained(A_  ,   from_pt=A_		)
														self.assertIsNotNone(A_		)
														self.assertIsInstance(A_  ,   A_		)

														_UpperCAmelCase    :						List[str]							=      AutoModelForPreTraining.from_pretrained(A_  ,   from_tf=A_		)
														self.assertIsNotNone(A_		)
														self.assertIsInstance(A_  ,   A_		)


						@slow
						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										for model_name in TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
														_UpperCAmelCase    :						List[Any]							=      AutoConfig.from_pretrained(A_		)
														self.assertIsNotNone(A_		)
														self.assertIsInstance(A_  ,   A_		)

														_UpperCAmelCase    :						Optional[Any]							=      TFAutoModelForCausalLM.from_pretrained(A_  ,   from_pt=A_		)
														_UpperCAmelCase    :						Union[str, Any]							=      TFAutoModelForCausalLM.from_pretrained(
														    A_  ,   output_loading_info=A_  ,   from_pt=A_		)
														self.assertIsNotNone(A_		)
														self.assertIsInstance(A_  ,   A_		)

														_UpperCAmelCase    :						Optional[int]							=      AutoModelForCausalLM.from_pretrained(A_  ,   from_tf=A_		)
														_UpperCAmelCase    :						str							=      AutoModelForCausalLM.from_pretrained(
														    A_  ,   output_loading_info=A_  ,   from_tf=A_		)
														self.assertIsNotNone(A_		)
														self.assertIsInstance(A_  ,   A_		)


						@slow
						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
														_UpperCAmelCase    :						List[Any]							=      AutoConfig.from_pretrained(A_		)
														self.assertIsNotNone(A_		)
														self.assertIsInstance(A_  ,   A_		)

														_UpperCAmelCase    :						Optional[int]							=      TFAutoModelWithLMHead.from_pretrained(A_  ,   from_pt=A_		)
														self.assertIsNotNone(A_		)
														self.assertIsInstance(A_  ,   A_		)

														_UpperCAmelCase    :						str							=      AutoModelWithLMHead.from_pretrained(A_  ,   from_tf=A_		)
														self.assertIsNotNone(A_		)
														self.assertIsInstance(A_  ,   A_		)


						@slow
						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
														_UpperCAmelCase    :						List[Any]							=      AutoConfig.from_pretrained(A_		)
														self.assertIsNotNone(A_		)
														self.assertIsInstance(A_  ,   A_		)

														_UpperCAmelCase    :						int							=      TFAutoModelForMaskedLM.from_pretrained(A_  ,   from_pt=A_		)
														_UpperCAmelCase    :						Optional[Any]							=      TFAutoModelForMaskedLM.from_pretrained(
														    A_  ,   output_loading_info=A_  ,   from_pt=A_		)
														self.assertIsNotNone(A_		)
														self.assertIsInstance(A_  ,   A_		)

														_UpperCAmelCase    :						List[str]							=      AutoModelForMaskedLM.from_pretrained(A_  ,   from_tf=A_		)
														_UpperCAmelCase    :						Optional[Any]							=      AutoModelForMaskedLM.from_pretrained(
														    A_  ,   output_loading_info=A_  ,   from_tf=A_		)
														self.assertIsNotNone(A_		)
														self.assertIsInstance(A_  ,   A_		)


						@slow
						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										for model_name in TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
														_UpperCAmelCase    :						Tuple							=      AutoConfig.from_pretrained(A_		)
														self.assertIsNotNone(A_		)
														self.assertIsInstance(A_  ,   A_		)

														_UpperCAmelCase    :						str							=      TFAutoModelForSeqaSeqLM.from_pretrained(A_  ,   from_pt=A_		)
														_UpperCAmelCase    :						Optional[Any]							=      TFAutoModelForSeqaSeqLM.from_pretrained(
														    A_  ,   output_loading_info=A_  ,   from_pt=A_		)
														self.assertIsNotNone(A_		)
														self.assertIsInstance(A_  ,   A_		)

														_UpperCAmelCase    :						str							=      AutoModelForSeqaSeqLM.from_pretrained(A_  ,   from_tf=A_		)
														_UpperCAmelCase    :						Any							=      AutoModelForSeqaSeqLM.from_pretrained(
														    A_  ,   output_loading_info=A_  ,   from_tf=A_		)
														self.assertIsNotNone(A_		)
														self.assertIsInstance(A_  ,   A_		)


						@slow
						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										for model_name in ["bert-base-uncased"]:
														_UpperCAmelCase    :						Optional[int]							=      AutoConfig.from_pretrained(A_		)
														self.assertIsNotNone(A_		)
														self.assertIsInstance(A_  ,   A_		)

														_UpperCAmelCase    :						List[Any]							=      TFAutoModelForSequenceClassification.from_pretrained(A_  ,   from_pt=A_		)
														self.assertIsNotNone(A_		)
														self.assertIsInstance(A_  ,   A_		)

														_UpperCAmelCase    :						Tuple							=      AutoModelForSequenceClassification.from_pretrained(A_  ,   from_tf=A_		)
														self.assertIsNotNone(A_		)
														self.assertIsInstance(A_  ,   A_		)


						@slow
						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										for model_name in ["bert-base-uncased"]:
														_UpperCAmelCase    :						int							=      AutoConfig.from_pretrained(A_		)
														self.assertIsNotNone(A_		)
														self.assertIsInstance(A_  ,   A_		)

														_UpperCAmelCase    :						Any							=      TFAutoModelForQuestionAnswering.from_pretrained(A_  ,   from_pt=A_		)
														self.assertIsNotNone(A_		)
														self.assertIsInstance(A_  ,   A_		)

														_UpperCAmelCase    :						Dict							=      AutoModelForQuestionAnswering.from_pretrained(A_  ,   from_tf=A_		)
														self.assertIsNotNone(A_		)
														self.assertIsInstance(A_  ,   A_		)


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										_UpperCAmelCase    :						Any							=      TFAutoModelWithLMHead.from_pretrained(A_  ,   from_pt=A_		)
										self.assertIsInstance(A_  ,   A_		)
										self.assertEqual(model.num_parameters()  ,   14410		)
										self.assertEqual(model.num_parameters(only_trainable=A_		)  ,   14410		)

										_UpperCAmelCase    :						Optional[Any]							=      AutoModelWithLMHead.from_pretrained(A_  ,   from_tf=A_		)
										self.assertIsInstance(A_  ,   A_		)
										self.assertEqual(model.num_parameters()  ,   14410		)
										self.assertEqual(model.num_parameters(only_trainable=A_		)  ,   14410		)





						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										_UpperCAmelCase    :						Optional[Any]							=      TFAutoModelWithLMHead.from_pretrained(A_  ,   from_pt=A_		)
										self.assertIsInstance(A_  ,   A_		)
										self.assertEqual(model.num_parameters()  ,   14410		)
										self.assertEqual(model.num_parameters(only_trainable=A_		)  ,   14410		)

										_UpperCAmelCase    :						List[str]							=      AutoModelWithLMHead.from_pretrained(A_  ,   from_tf=A_		)
										self.assertIsInstance(A_  ,   A_		)
										self.assertEqual(model.num_parameters()  ,   14410		)
										self.assertEqual(model.num_parameters(only_trainable=A_		)  ,   14410		)





import argparse

import torch

from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_from_original_stable_diffusion_ckpt


if __name__ == "__main__":
					SCREAMING_SNAKE_CASE_            = argparse.ArgumentParser()

					parser.add_argument(
					    '--checkpoint_path', default=None, type=str, required=True, help='Path to the checkpoint to convert.'
					)
					# !wget https://raw.githubusercontent.com/CompVis/stable-diffusion/main/configs/stable-diffusion/v1-inference.yaml
					parser.add_argument(
					    '--original_config_file',
					    default=None,
					    type=str,
					    help='The YAML config file corresponding to the original architecture.',
					)
					parser.add_argument(
					    '--num_in_channels',
					    default=None,
					    type=int,
					    help='The number of input channels. If `None` number of input channels will be automatically inferred.',
					)
					parser.add_argument(
					    '--scheduler_type',
					    default='pndm',
					    type=str,
					    help='Type of scheduler to use. Should be one of [\'pndm\', \'lms\', \'ddim\', \'euler\', \'euler-ancestral\', \'dpm\']',
					)
					parser.add_argument(
					    '--pipeline_type',
					    default=None,
					    type=str,
					    help=(
					        'The pipeline type. One of \'FrozenOpenCLIPEmbedder\', \'FrozenCLIPEmbedder\', \'PaintByExample\''
					        '. If `None` pipeline will be automatically inferred.'
					    ),
					)
					parser.add_argument(
					    '--image_size',
					    default=None,
					    type=int,
					    help=(
					        'The image size that the model was trained on. Use 512 for Stable Diffusion v1.X and Stable Siffusion v2'
					        ' Base. Use 768 for Stable Diffusion v2.'
					    ),
					)
					parser.add_argument(
					    '--prediction_type',
					    default=None,
					    type=str,
					    help=(
					        'The prediction type that the model was trained on. Use \'epsilon\' for Stable Diffusion v1.X and Stable'
					        ' Diffusion v2 Base. Use \'v_prediction\' for Stable Diffusion v2.'
					    ),
					)
					parser.add_argument(
					    '--extract_ema',
					    action='store_true',
					    help=(
					        'Only relevant for checkpoints that have both EMA and non-EMA weights. Whether to extract the EMA weights'
					        ' or not. Defaults to `False`. Add `--extract_ema` to extract the EMA weights. EMA weights usually yield'
					        ' higher quality images for inference. Non-EMA weights are usually better to continue fine-tuning.'
					    ),
					)
					parser.add_argument(
					    '--upcast_attention',
					    action='store_true',
					    help=(
					        'Whether the attention computation should always be upcasted. This is necessary when running stable'
					        ' diffusion 2.1.'
					    ),
					)
					parser.add_argument(
					    '--from_safetensors',
					    action='store_true',
					    help='If `--checkpoint_path` is in `safetensors` format, load checkpoint with safetensors instead of PyTorch.',
					)
					parser.add_argument(
					    '--to_safetensors',
					    action='store_true',
					    help='Whether to store pipeline in safetensors format or not.',
					)
					parser.add_argument('--dump_path', default=None, type=str, required=True, help='Path to the output model.')
					parser.add_argument('--device', type=str, help='Device to use (e.g. cpu, cuda:0, cuda:1, etc.)')
					parser.add_argument(
					    '--stable_unclip',
					    type=str,
					    default=None,
					    required=False,
					    help='Set if this is a stable unCLIP model. One of \'txt2img\' or \'img2img\'.',
					)
					parser.add_argument(
					    '--stable_unclip_prior',
					    type=str,
					    default=None,
					    required=False,
					    help='Set if this is a stable unCLIP txt2img model. Selects which prior to use. If `--stable_unclip` is set to `txt2img`, the karlo prior (https://huggingface.co/kakaobrain/karlo-v1-alpha/tree/main/prior) is selected by default.',
					)
					parser.add_argument(
					    '--clip_stats_path',
					    type=str,
					    help='Path to the clip stats file. Only required if the stable unclip model\'s config specifies `model.params.noise_aug_config.params.clip_stats_path`.',
					    required=False,
					)
					parser.add_argument(
					    '--controlnet', action='store_true', default=None, help='Set flag if this is a controlnet checkpoint.'
					)
					parser.add_argument('--half', action='store_true', help='Save weights in half precision.')
					parser.add_argument(
					    '--vae_path',
					    type=str,
					    default=None,
					    required=False,
					    help='Set to a path, hub id to an already converted vae to not convert it again.',
					)
					SCREAMING_SNAKE_CASE_            = parser.parse_args()

					SCREAMING_SNAKE_CASE_            = download_from_original_stable_diffusion_ckpt(
					    checkpoint_path=args.checkpoint_path,
					    original_config_file=args.original_config_file,
					    image_size=args.image_size,
					    prediction_type=args.prediction_type,
					    model_type=args.pipeline_type,
					    extract_ema=args.extract_ema,
					    scheduler_type=args.scheduler_type,
					    num_in_channels=args.num_in_channels,
					    upcast_attention=args.upcast_attention,
					    from_safetensors=args.from_safetensors,
					    device=args.device,
					    stable_unclip=args.stable_unclip,
					    stable_unclip_prior=args.stable_unclip_prior,
					    clip_stats_path=args.clip_stats_path,
					    controlnet=args.controlnet,
					    vae_path=args.vae_path,
					)

					if args.half:
										pipe.to(torch_dtype=torch.floataa)

					if args.controlnet:
										# only save the controlnet model
										pipe.controlnet.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)
					else:
										pipe.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)


