
import unittest

from transformers import is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, require_torch, slow


if is_flax_available():
		import optax
		from flax.training.common_utils import onehot

		from transformers import AutoTokenizer, FlaxMTaForConditionalGeneration
		from transformers.models.ta.modeling_flax_ta import shift_tokens_right




@require_torch
@require_sentencepiece
@require_tokenizers
@require_flax
class 				lowerCamelCase__							(       unittest.TestCase):




	'''simple docstring'''




	@slow
	def      _lowerCamelCase       (      self	:Dict		)	->				str:
								__UpperCamelCase			:     List[str]         =      FlaxMTaForConditionalGeneration.from_pretrained("google/mt5-small"		)
								__UpperCamelCase			:     Optional[int]         =      AutoTokenizer.from_pretrained("google/mt5-small"		)

								__UpperCamelCase			:     List[Any]         =      tokenizer("Hello there"  ,     return_tensors="np"		).input_ids
								__UpperCamelCase			:     str         =      tokenizer("Hi I am"  ,     return_tensors="np"		).input_ids

								__UpperCamelCase			:     Any         =      shift_tokens_right(a  ,     model.config.pad_token_id  ,     model.config.decoder_start_token_id		)

								__UpperCamelCase			:     List[Any]         =      model(a  ,     decoder_input_ids=a		).logits
								__UpperCamelCase			:     List[str]         =      optax.softmax_cross_entropy(a  ,     onehot(a  ,     logits.shape[-1]		)		).mean()

								__UpperCamelCase			:     Optional[Any]         =      -(labels.shape[-1] * loss.item())

								__UpperCamelCase			:     Optional[int]         =      -84.9127
								self.assertTrue(abs(mtf_score - EXPECTED_SCORE		) < 1E-4		)

import argparse

from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection

from diffusers import UnCLIPImageVariationPipeline, UnCLIPPipeline


if __name__ == "__main__":
		lowercase						:      List[str]									= argparse.ArgumentParser()

		parser.add_argument('--dump_path', default=None, type=str, required=True, help='Path to the output model.')

		parser.add_argument(
		    '--txt2img_unclip',
		    default='kakaobrain/karlo-v1-alpha',
		    type=str,
		    required=False,
		    help='The pretrained txt2img unclip.',
		)

		lowercase						:      int									= parser.parse_args()

		lowercase						:      str									= UnCLIPPipeline.from_pretrained(args.txtaimg_unclip)

		lowercase						:      int									= CLIPImageProcessor()
		lowercase						:      Optional[int]									= CLIPVisionModelWithProjection.from_pretrained('openai/clip-vit-large-patch14')

		lowercase						:      int									= UnCLIPImageVariationPipeline(
		    decoder=txtaimg.decoder,
		    text_encoder=txtaimg.text_encoder,
		    tokenizer=txtaimg.tokenizer,
		    text_proj=txtaimg.text_proj,
		    feature_extractor=feature_extractor,
		    image_encoder=image_encoder,
		    super_res_first=txtaimg.super_res_first,
		    super_res_last=txtaimg.super_res_last,
		    decoder_scheduler=txtaimg.decoder_scheduler,
		    super_res_scheduler=txtaimg.super_res_scheduler,
		)

		imgaimg.save_pretrained(args.dump_path)