import sentencepiece as spm

# pegasus:
# 1. no bos
# 2. eos_id is 1
# 3. unk_id is 2
# build a sample spm file accordingly
spm.SentencePieceTrainer.train(
    "--input=botchan.txt --model_prefix=test_sentencepiece_no_bos --bos_id=-1 --unk_id=2  --eos_id=1  --vocab_size=1000"
)

# 4. now update the fixture
# mv test_sentencepiece_no_bos.model ../../tests/fixtures/










"""simple docstring"""




import argparse

import torch
from clip import load

from transformers import CLIPConfig, CLIPModel





def 			a_							(				_lowercase			,		_lowercase  ):
						_UpperCamelCase  ,				_UpperCamelCase  ,				_UpperCamelCase     :   Any      =       pt_attn_layer.in_proj_weight.chunk(3			,		dim=0  )
						_UpperCamelCase  ,				_UpperCamelCase  ,				_UpperCamelCase     :   Dict      =       pt_attn_layer.in_proj_bias.chunk(3			,		dim=0  )

						_UpperCamelCase     :   int      =       pt_attn_layer.out_proj.weight
						_UpperCamelCase     :   List[Any]      =       pt_attn_layer.out_proj.bias

						_UpperCamelCase     :   Any      =       q_proj
						_UpperCamelCase     :   str      =       q_proj_bias

						_UpperCamelCase     :   str      =       k_proj
						_UpperCamelCase     :   List[str]      =       k_proj_bias

						_UpperCamelCase     :   Dict      =       v_proj
						_UpperCamelCase     :   Dict      =       v_proj_bias

						_UpperCamelCase     :   Dict      =       out_proj_weights
						_UpperCamelCase     :   Optional[Any]      =       out_proj_bias





def 			a_							(				_lowercase			,		_lowercase  ):
						copy_linear(hf_mlp.fca			,		pt_mlp.c_fc  )
						copy_linear(hf_mlp.fca			,		pt_mlp.c_proj  )





def 			a_							(				_lowercase			,		_lowercase  ):
						_UpperCamelCase     :   str      =       pt_linear.weight
						_UpperCamelCase     :   List[str]      =       pt_linear.bias





def 			a_							(				_lowercase			,		_lowercase  ):
						# copy layer norms
						copy_linear(hf_layer.layer_norma			,		pt_layer.ln_a  )
						copy_linear(hf_layer.layer_norma			,		pt_layer.ln_a  )

						# copy MLP
						copy_mlp(hf_layer.mlp			,		pt_layer.mlp  )

						# copy attn
						copy_attn_layer(hf_layer.self_attn			,		pt_layer.attn  )





def 			a_							(				_lowercase			,		_lowercase  ):
						for hf_layer, pt_layer in zip(_lowercase			,		_lowercase  ):
												copy_layer(_lowercase			,		_lowercase  )





def 			a_							(				_lowercase			,		_lowercase  ):
						# copy  embeds
						_UpperCamelCase     :   Tuple      =       pt_model.token_embedding.weight
						_UpperCamelCase     :   List[Any]      =       pt_model.positional_embedding

						# copy layer norm
						copy_linear(hf_encoder.final_layer_norm			,		pt_model.ln_final  )

						# copy hidden layers
						copy_layers(hf_encoder.encoder.layers			,		pt_model.transformer.resblocks  )





def 			a_							(				_lowercase			,		_lowercase  ):
						# copy projection
						_UpperCamelCase     :   str      =       pt_model.text_projection.data.T

						# copy text encoder
						copy_encoder(hf_model.text_model			,		_lowercase  )





def 			a_							(				_lowercase			,		_lowercase  ):
						# copy projection
						_UpperCamelCase     :   Tuple      =       pt_model.visual.proj.data.T

						# copy layer norms
						copy_linear(hf_model.vision_model.pre_layrnorm			,		pt_model.visual.ln_pre  )
						copy_linear(hf_model.vision_model.post_layernorm			,		pt_model.visual.ln_post  )

						# copy embeds
						_UpperCamelCase     :   str      =       pt_model.visual.conva.weight.data
						_UpperCamelCase     :   Dict      =       pt_model.visual.class_embedding
						_UpperCamelCase     :   Union[str, Any]      =       pt_model.visual.positional_embedding.data

						# copy encoder
						copy_layers(hf_model.vision_model.encoder.layers			,		pt_model.visual.transformer.resblocks  )





@torch.no_grad()
def 			a_							(				_lowercase			,		_lowercase			,		_lowercase=None  ):
						if config_path is not None:
												_UpperCamelCase     :   List[Any]      =       CLIPConfig.from_pretrained(_lowercase  )
						else:
												_UpperCamelCase     :   Optional[int]      =       CLIPConfig(projection_dim=512			,		text_config={}			,		vision_config={}  )

						_UpperCamelCase     :   str      =       CLIPModel(_lowercase  ).eval()

						_UpperCamelCase  ,				_UpperCamelCase     :   Optional[int]      =       load(_lowercase			,		device='''cpu'''			,		jit=_lowercase  )
						_UpperCamelCase     :   Union[str, Any]      =       pt_model.eval()

						copy_text_model_and_projection(_lowercase			,		_lowercase  )
						copy_vison_model_and_projection(_lowercase			,		_lowercase  )
						_UpperCamelCase     :   int      =       pt_model.logit_scale

						_UpperCamelCase     :   List[Any]      =       torch.arange(0			,		77  ).unsqueeze(0  )
						_UpperCamelCase     :   Optional[int]      =       torch.randn(1			,		3			,		224			,		224  )

						_UpperCamelCase     :   Tuple      =       hf_model(input_ids=_lowercase			,		pixel_values=_lowercase			,		return_dict=_lowercase  )
						_UpperCamelCase     :   Dict      =       hf_outputs.logits_per_image
						_UpperCamelCase     :   Any      =       hf_outputs.logits_per_text
						_UpperCamelCase  ,				_UpperCamelCase     :   Any      =       pt_model(_lowercase			,		_lowercase  )

						assert torch.allclose(_lowercase			,		_lowercase			,		atol=1E-3  )
						assert torch.allclose(_lowercase			,		_lowercase			,		atol=1E-3  )

						hf_model.save_pretrained(_lowercase  )


if __name__ == "__main__":
						UpperCamelCase_	 =argparse.ArgumentParser()
						parser.add_argument("""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
						parser.add_argument("""--checkpoint_path""", default=None, type=str, help="""Path to fairseq checkpoint""")
						parser.add_argument("""--config_path""", default=None, type=str, help="""Path to hf config.json of model to convert""")
						UpperCamelCase_	 =parser.parse_args()

						convert_clip_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)



