



import unittest

from transformers import ErnieMTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin


UpperCAmelCase							=       get_tests_dir("""fixtures/spiece.model""")

@require_sentencepiece
@require_tokenizers
class 			__snake_case   ( SCREAMING_SNAKE_CASE      ,unittest.TestCase):







							'''simple docstring'''






							UpperCamelCase__					:   List[Any]       =       ErnieMTokenizer
							UpperCamelCase__					:   List[Any]       =       False
							UpperCamelCase__					:   List[Any]       =       True
							UpperCamelCase__					:   int       =       False
							UpperCamelCase__					:   Union[str, Any]       =       False


							def _a   (				self		):
											super().setUp()

											# We have a SentencePiece fixture for testing
											a__					   =		ErnieMTokenizer(a_	,	unk_token="""<unk>"""	,	pad_token="""<pad>"""		)
											tokenizer.save_pretrained(self.tmpdirname		)


							def _a   (				self	,	a_		):
											a__					   =		"""this is a test"""
											a__					   =		"""this is a test"""
											return input_text, output_text


							def _a   (				self		):
											a__					   =		"""<pad>"""
											a__					   =		0

											self.assertEqual(self.get_tokenizer()._convert_token_to_id(a_		)	,	a_		)
											self.assertEqual(self.get_tokenizer()._convert_id_to_token(a_		)	,	a_		)


							def _a   (				self		):
											a__					   =		list(self.get_tokenizer().get_vocab().keys()		)

											self.assertEqual(vocab_keys[0]	,	"""<pad>"""		)
											self.assertEqual(vocab_keys[1]	,	"""<unk>"""		)
											self.assertEqual(vocab_keys[-1]	,	"""▁eloquent"""		)
											self.assertEqual(len(a_		)	,	30_000		)


							def _a   (				self		):
											self.assertEqual(self.get_tokenizer().vocab_size	,	30_000		)


							def _a   (				self		):
											if not self.test_rust_tokenizer:
															return

											a__					   =		self.get_tokenizer()
											a__					   =		self.get_rust_tokenizer()

											a__					   =		"""I was born in 92000, and this is falsé."""

											a__					   =		tokenizer.tokenize(a_		)
											a__					   =		rust_tokenizer.tokenize(a_		)
											self.assertListEqual(a_	,	a_		)

											a__					   =		tokenizer.encode(a_	,	add_special_tokens=a_		)
											a__					   =		rust_tokenizer.encode(a_	,	add_special_tokens=a_		)
											self.assertListEqual(a_	,	a_		)

											a__					   =		self.get_rust_tokenizer()
											a__					   =		tokenizer.encode(a_		)
											a__					   =		rust_tokenizer.encode(a_		)
											self.assertListEqual(a_	,	a_		)


							def _a   (				self		):
											a__					   =		ErnieMTokenizer(a_	,	do_lower_case=a_	,	unk_token="""<unk>"""	,	pad_token="""<pad>"""		)

											a__					   =		tokenizer.tokenize("""This is a test"""		)
											self.assertListEqual(a_	,	["""▁this""", """▁is""", """▁a""", """▁test"""]		)

											self.assertListEqual(tokenizer.convert_tokens_to_ids(a_		)	,	[48, 25, 21, 1_289]		)

											a__					   =		tokenizer.tokenize("""I was born in 92000, and this is falsé."""		)
											# ErnieMTokenizer(paddlenlp implementation) outputs '9' instead of '_9' so to mimic that '_9' is changed to '9'
											self.assertListEqual(
											    a_	,	["""▁i""", """▁was""", """▁born""", """▁in""", """9""", """2000""", """,""", """▁and""", """▁this""", """▁is""", """▁fal""", """s""", """é""", """."""]		)
											a__					   =		tokenizer.convert_tokens_to_ids(a_		)
											self.assertListEqual(a_	,	[31, 23, 386, 19, 518, 3_050, 15, 17, 48, 25, 8_256, 18, 1, 9]		)

											a__					   =		tokenizer.convert_ids_to_tokens(a_		)
											self.assertListEqual(
											    a_	,	["""▁i""", """▁was""", """▁born""", """▁in""", """9""", """2000""", """,""", """▁and""", """▁this""", """▁is""", """▁fal""", """s""", """<unk>""", """."""]	,	)


							def _a   (				self		):
											a__					   =		ErnieMTokenizer(a_	,	unk_token="""<unk>"""	,	pad_token="""<pad>"""		)

											a__					   =		tokenizer.encode("""sequence builders"""		)
											a__					   =		tokenizer.encode("""multi-sequence build"""		)

											a__					   =		tokenizer.build_inputs_with_special_tokens(a_		)
											a__					   =		tokenizer.build_inputs_with_special_tokens(a_	,	a_		)

											assert encoded_sentence == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id]
											assert encoded_pair == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] + [
											    tokenizer.sep_token_id
											] + text_a + [tokenizer.sep_token_id]







							@slow
							def _a   (				self		):
											# fmt: off
											a__					   =		{"""input_ids""": [[0, 11_062, 82_772, 7, 15, 82_772, 538, 51_529, 237, 17_198, 1_290, 206, 9, 215_175, 1_314, 136, 17_198, 1_290, 206, 9, 56_359, 42, 122_009, 9, 16_466, 16, 87_344, 4_537, 9, 4_717, 78_381, 6, 159_958, 7, 15, 24_480, 618, 4, 527, 22_693, 9, 304, 4, 2_777, 24_480, 9_874, 4, 43_523, 594, 4, 803, 18_392, 33_189, 18, 4, 43_523, 24_447, 5, 5, 5, 16, 100, 24_955, 83_658, 9_626, 144_057, 15, 839, 22_335, 16, 136, 24_955, 83_658, 83_479, 15, 39_102, 724, 16, 678, 645, 6_460, 1_328, 4_589, 42, 122_009, 115_774, 23, 3_559, 1_328, 46_876, 7, 136, 53_894, 1_940, 42_227, 41_159, 17_721, 823, 425, 4, 27_512, 98_722, 206, 136, 5_531, 4_970, 919, 17_336, 5, 2], [0, 20_080, 618, 83, 82_775, 47, 479, 9, 1_517, 73, 53_894, 333, 80_581, 110_117, 18_811, 5_256, 1_295, 51, 152_526, 297, 7_986, 390, 124_416, 538, 35_431, 214, 98, 15_044, 25_737, 136, 7_108, 43_701, 23, 756, 135_355, 7, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 581, 63_773, 119_455, 6, 147_797, 88_203, 7, 645, 70, 21, 3_285, 10_269, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]}
											# fmt: on

											self.tokenizer_integration_test_util(
											    expected_encoding=a_	,	model_name="""susnato/ernie-m-base_pytorch"""	,	sequences=[
											        """Transformers (formerly known as pytorch-transformers and pytorch-pretrained-bert) provides """
											        """general-purpose architectures (BERT, GPT-2, RoBERTa, XLM, DistilBert, XLNet...) for Natural """
											        """Language Understanding (NLU) and Natural Language Generation (NLG) with over32+ pretrained """
											        """models in100+ languages and deep interoperability between Jax, PyTorch and TensorFlow.""",
											        """BERT is designed to pre-train deep bidirectional representations from unlabeled text by jointly """
											        """conditioning on both left and right context in all layers.""",
											        """The quick brown fox jumps over the lazy dog.""",
											    ]	,	)




import argparse

import torch
from clip import load

from transformers import CLIPConfig, CLIPModel






def        lowerCamelCase_  (				UpperCAmelCase_				:  Optional[int]				,							UpperCAmelCase_				:  Tuple						)    ->					str:




	'''simple docstring'''




	_UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase :		Tuple								=    pt_attn_layer.in_proj_weight.chunk(3				,							dim=0						)
	_UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase :		str								=    pt_attn_layer.in_proj_bias.chunk(3				,							dim=0						)

	_UpperCamelCase :		Dict								=    pt_attn_layer.out_proj.weight
	_UpperCamelCase :		Tuple								=    pt_attn_layer.out_proj.bias

	_UpperCamelCase :		Tuple								=    q_proj
	_UpperCamelCase :		Dict								=    q_proj_bias

	_UpperCamelCase :		Dict								=    k_proj
	_UpperCamelCase :		str								=    k_proj_bias

	_UpperCamelCase :		Dict								=    v_proj
	_UpperCamelCase :		Optional[int]								=    v_proj_bias

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






def        lowerCamelCase_  (				UpperCAmelCase_				:  Optional[int]				,							UpperCAmelCase_				:  Union[str, Any]						)    ->					List[Any]:




	'''simple docstring'''




	copy_linear(hf_mlp.fca				,							pt_mlp.c_fc						)
	copy_linear(hf_mlp.fca				,							pt_mlp.c_proj						)






def        lowerCamelCase_  (				UpperCAmelCase_				:  List[str]				,							UpperCAmelCase_				:  List[str]						)    ->					List[Any]:




	'''simple docstring'''




	_UpperCamelCase :		int								=    pt_linear.weight
	_UpperCamelCase :		int								=    pt_linear.bias






def        lowerCamelCase_  (				UpperCAmelCase_				:  str				,							UpperCAmelCase_				:  Union[str, Any]						)    ->					Dict:




	'''simple docstring'''




	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        lowerCamelCase_  (				UpperCAmelCase_				:  Optional[Any]				,							UpperCAmelCase_				:  List[str]						)    ->					Dict:




	'''simple docstring'''




	for hf_layer, pt_layer in zip(UpperCAmelCase_				,							UpperCAmelCase_						):
		copy_layer(UpperCAmelCase_				,							UpperCAmelCase_						)






def        lowerCamelCase_  (				UpperCAmelCase_				:  Any				,							UpperCAmelCase_				:  Optional[Any]						)    ->					int:




	'''simple docstring'''




	_UpperCamelCase :		Optional[int]								=    pt_model.token_embedding.weight
	_UpperCamelCase :		Tuple								=    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        lowerCamelCase_  (				UpperCAmelCase_				:  int				,							UpperCAmelCase_				:  Tuple						)    ->					Union[str, Any]:




	'''simple docstring'''




	_UpperCamelCase :		Any								=    pt_model.text_projection.data.T

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






def        lowerCamelCase_  (				UpperCAmelCase_				:  str				,							UpperCAmelCase_				:  Dict						)    ->					Optional[int]:




	'''simple docstring'''




	_UpperCamelCase :		str								=    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 :		Tuple								=    pt_model.visual.conva.weight.data
	_UpperCamelCase :		Dict								=    pt_model.visual.class_embedding
	_UpperCamelCase :		str								=    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        lowerCamelCase_  (				UpperCAmelCase_				:  Dict				,							UpperCAmelCase_				:  str				,							UpperCAmelCase_				:  List[str]=None						)    ->					Union[str, Any]:




	'''simple docstring'''




	if config_path is not None:
		_UpperCamelCase :		Dict								=    CLIPConfig.from_pretrained(UpperCAmelCase_						)
	else:
		_UpperCamelCase :		Tuple								=    CLIPConfig(projection_dim=5_1_2				,							text_config={}				,							vision_config={}						)

	_UpperCamelCase :		Tuple								=    CLIPModel(UpperCAmelCase_						).eval()

	_UpperCamelCase						,		_UpperCamelCase :		Optional[Any]								=    load(UpperCAmelCase_				,							device='cpu'				,							jit=UpperCAmelCase_						)
	_UpperCamelCase :		Optional[Any]								=    pt_model.eval()

	copy_text_model_and_projection(UpperCAmelCase_				,							UpperCAmelCase_						)
	copy_vison_model_and_projection(UpperCAmelCase_				,							UpperCAmelCase_						)
	_UpperCamelCase :		Optional[Any]								=    pt_model.logit_scale

	_UpperCamelCase :		Optional[int]								=    torch.arange(0				,							7_7						).unsqueeze(0						)
	_UpperCamelCase :		Dict								=    torch.randn(1				,							3				,							2_2_4				,							2_2_4						)

	_UpperCamelCase :		Optional[Any]								=    hf_model(input_ids=UpperCAmelCase_				,							pixel_values=UpperCAmelCase_				,							return_dict=UpperCAmelCase_						)
	_UpperCamelCase :		Optional[Any]								=    hf_outputs.logits_per_image
	_UpperCamelCase :		Optional[int]								=    hf_outputs.logits_per_text
	_UpperCamelCase						,		_UpperCamelCase :		int								=    pt_model(UpperCAmelCase_				,							UpperCAmelCase_						)

	assert torch.allclose(UpperCAmelCase_				,							UpperCAmelCase_				,							atol=1e-3						)
	assert torch.allclose(UpperCAmelCase_				,							UpperCAmelCase_				,							atol=1e-3						)

	hf_model.save_pretrained(UpperCAmelCase_						)


if __name__ == "__main__":
							lowerCAmelCase__             =		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""")
							lowerCAmelCase__             =		parser.parse_args()

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




