



import inspect
import json
import os
import tempfile
import unittest

from transformers.models.speech_to_text_a import SpeechaTextaTokenizer
from transformers.models.speech_to_text_a.tokenization_speech_to_text_a import VOCAB_FILES_NAMES

from ...test_tokenization_common import TokenizerTesterMixin



class 			_lowercase							(     __lowerCamelCase,   unittest.TestCase			):





	"""simple docstring"""
	__A					=							SpeechaTextaTokenizer
	__A					=							False




	def   UpperCamelCase_ (self	):
						"""simple docstring"""

						super().setUp()

						a        =     "<s> <pad> </s> <unk> here@@ a couple of@@ words for the he@@ re@@ vocab".split(" "	)
						a        =     ["he re</w> 123", "here a 1456"]
						a        =     dict(zip(lowerCamelCase_	, range(len(lowerCamelCase_	)	)	)	)

						a        =     {"pad_token": "<pad>", "unk_token": "<unk>", "bos_token": "<s>", "eos_token": "</s>"}

						a        =     tempfile.mkdtemp()
						a        =     os.path.join(self.tmpdirname	, VOCAB_FILES_NAMES["vocab_file"]	)
						a        =     os.path.join(self.tmpdirname	, VOCAB_FILES_NAMES["merges_file"]	)

						with open(self.vocab_file	, "w"	, encoding="utf-8"	) as fp:
											fp.write(json.dumps(lowerCamelCase_	) + "\n"	)

						with open(self.merges_file	, "w"	) as fp:
											fp.write("\n".join(lowerCamelCase_	)	)




	def   UpperCamelCase_ (self	):
						"""simple docstring"""

						a        =     list(self.get_tokenizer().get_vocab().keys()	)

						self.assertEqual(vocab_keys[0]	, "<s>"	)
						self.assertEqual(vocab_keys[1]	, "<pad>"	)
						self.assertEqual(vocab_keys[-1]	, "vocab"	)
						self.assertEqual(len(lowerCamelCase_	)	, 14	)




	def   UpperCamelCase_ (self	):
						"""simple docstring"""

						self.assertEqual(self.get_tokenizer().vocab_size	, 14	)




	def   UpperCamelCase_ (self	):
						"""simple docstring"""

						a        =     SpeechaTextaTokenizer.from_pretrained(self.tmpdirname	)

						# make sure @@ is correctly concatenated
						a        =     [4, 6, 8, 7, 10]  # ["here@@", "couple", "words", "of@@", "the"]
						a        =     tokenizer.decode(lowerCamelCase_	)

						self.assertTrue(output_string == "herecouple words ofthe"	)




	def   UpperCamelCase_ (self	):
						"""simple docstring"""

						a        =     SpeechaTextaTokenizer.from_pretrained(self.tmpdirname	)

						with tempfile.TemporaryDirectory() as tmp_dirname:
											tokenizer.save_pretrained(lowerCamelCase_	)
											os.remove(os.path.join(lowerCamelCase_	, "merges.txt"	)	)

											# load tokenizer without merges file should not throw an error
											a        =     SpeechaTextaTokenizer.from_pretrained(lowerCamelCase_	)

						with tempfile.TemporaryDirectory() as tmp_dirname:
											# save tokenizer and load again
											tokenizer.save_pretrained(lowerCamelCase_	)
											a        =     SpeechaTextaTokenizer.from_pretrained(lowerCamelCase_	)

						self.assertIsNotNone(lowerCamelCase_	)




	def   UpperCamelCase_ (self	):
						"""simple docstring"""

						if not self.test_slow_tokenizer:
											return

						a        =     inspect.signature(self.tokenizer_class.__init__	)
						a        =     self.get_tokenizer()

						for parameter_name, parameter in signature.parameters.items():
											if parameter.default != inspect.Parameter.empty and parameter_name != "merges_file":
																self.assertIn(lowerCamelCase_	, tokenizer.init_kwargs	)





import argparse

import torch

from transformers import ChineseCLIPConfig, ChineseCLIPModel






def 					a(   A :					Union[str, Any]				,  A :					List[str]				,  A :					Any )	-> Tuple:
					"""simple docstring"""





					a  ,       a  ,       a        =     pt_weights[f'''{prefix}.in_proj_weight'''].chunk(3				,  dim=0 )
					a  ,       a  ,       a        =     pt_weights[f'''{prefix}.in_proj_bias'''].chunk(3				,  dim=0 )

					a        =     pt_weights[f'''{prefix}.out_proj.weight''']
					a        =     pt_weights[f'''{prefix}.out_proj.bias''']

					a        =     q_proj
					a        =     q_proj_bias

					a        =     k_proj
					a        =     k_proj_bias

					a        =     v_proj
					a        =     v_proj_bias

					a        =     out_proj_weights
					a        =     out_proj_bias






def 					a(   A :					List[str]				,  A :					List[Any]				,  A :					int )	-> List[Any]:
					"""simple docstring"""





					copy_linear(hf_mlp.fca				,  A				,  f'''{prefix}.c_fc''' )
					copy_linear(hf_mlp.fca				,  A				,  f'''{prefix}.c_proj''' )






def 					a(   A :					Optional[Any]				,  A :					List[Any]				,  A :					List[str] )	-> Dict:
					"""simple docstring"""





					a        =     pt_weights[f'''{prefix}.weight'''].data
					a        =     pt_weights[f'''{prefix}.bias'''].data






def 					a(   A :					int				,  A :					Optional[Any]				,  A :					List[Any] )	-> Union[str, Any]:
					"""simple docstring"""





					copy_linear(hf_layer.layer_norma				,  A				,  f'''{prefix}.ln_1''' )
					copy_linear(hf_layer.layer_norma				,  A				,  f'''{prefix}.ln_2''' )

					# copy MLP
					copy_mlp(hf_layer.mlp				,  A				,  f'''{prefix}.mlp''' )

					# copy attn
					copy_attn_layer(hf_layer.self_attn				,  A				,  f'''{prefix}.attn''' )






def 					a(   A :					str				,  A :					Optional[int]				,  A :					List[str] )	-> Dict:
					"""simple docstring"""





					for layer_id, hf_layer in enumerate(A ):
										copy_layer(A				,  A				,  f'''{prefix}.{layer_id}''' )






def 					a(   A :					Dict				,  A :					Union[str, Any] )	-> Optional[Any]:
					"""simple docstring"""





					a        =     pt_weights["text_projection"].data.T

					# copy text encoder
					for name, param in hf_model.text_model.named_parameters():
										a        =     pt_weights[f'''bert.{name}'''].data






def 					a(   A :					Optional[Any]				,  A :					Optional[Any] )	-> List[str]:
					"""simple docstring"""





					a        =     pt_weights["visual.proj"].data.T

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

					# copy embeddings
					a        =     pt_weights["visual.conv1.weight"].data
					a        =     pt_weights["visual.class_embedding"].data
					a        =     pt_weights["visual.positional_embedding"].data

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






@torch.no_grad()
def 					a(   A :					str				,  A :					List[str]				,  A :					Union[str, Any]=None )	-> Tuple:
					"""simple docstring"""





					assert config_path is not None, "Please specify the ChineseCLIP model config of the corresponding model size."
					a        =     ChineseCLIPConfig.from_pretrained(A )

					a        =     ChineseCLIPModel(A ).eval()

					a        =     torch.load(A				,  map_location="cpu" )["state_dict"]
					a        =     {(name[7:] if name.startswith("module." ) else name): value for name, value in pt_weights.items()}

					copy_text_model_and_projection(A				,  A )
					copy_vision_model_and_projection(A				,  A )
					a        =     pt_weights["logit_scale"].data

					hf_model.save_pretrained(A )


if __name__ == "__main__":
				_lowercase:				List[Any]       =	argparse.ArgumentParser()
				parser.add_argument(
				    "--pytorch_dump_folder_path",
				    default=None,
				    type=str,
				    help="Path to the output folder storing converted hf PyTorch model.",
				)
				parser.add_argument(
				    "--checkpoint_path", default=None, type=str, help="Path to original github format ChineseCLIP checkpoint."
				)
				parser.add_argument(
				    "--config_path", default=None, required=True, type=str, help="Path to hf config.json of model to convert."
				)
				_lowercase:				int       =	parser.parse_args()

				convert_chinese_clip_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)
				print("The conversion is finished!")
