




import tempfile
import unittest

import numpy as np

import transformers
from transformers import GPTaTokenizer, GPTNeoConfig, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, slow

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
		import jax
		import jax.numpy as jnp

		from transformers.modeling_flax_pytorch_utils import (
		    convert_pytorch_state_dict_to_flax,
		    load_flax_weights_in_pytorch_model,
		)
		from transformers.models.gpt_neo.modeling_flax_gpt_neo import FlaxGPTNeoForCausalLM, FlaxGPTNeoModel

if is_torch_available():
		import torch




class        snake_case__				:
				"""simple docstring"""



				def __init__( self							:	Any   , __lowerCamelCase							:	Union[str, Any]   , __lowerCamelCase							:	Any=14   , __lowerCamelCase							:	Optional[Any]=7   , __lowerCamelCase							:	Optional[int]=True   , __lowerCamelCase							:	List[str]=True   , __lowerCamelCase							:	int=False   , __lowerCamelCase							:	List[str]=True   , __lowerCamelCase							:	Tuple=99   , __lowerCamelCase							:	int=32   , __lowerCamelCase							:	Tuple=4   , __lowerCamelCase							:	Tuple=4   , __lowerCamelCase							:	Any=[[["global", "local"], 2]]   , __lowerCamelCase							:	Dict=37   , __lowerCamelCase							:	Optional[Any]="gelu"   , __lowerCamelCase							:	Optional[Any]=0.1   , __lowerCamelCase							:	int=0.1   , __lowerCamelCase							:	List[Any]=5_12   , __lowerCamelCase							:	Tuple=7   , __lowerCamelCase							:	Optional[Any]=0.02   , )   ->	int:
							a       =     parent
							a       =     batch_size
							a       =     seq_length
							a       =     is_training
							a       =     use_input_mask
							a       =     use_token_type_ids
							a       =     use_labels
							a       =     vocab_size
							a       =     hidden_size
							a       =     num_hidden_layers
							a       =     num_attention_heads
							a       =     attention_types
							a       =     intermediate_size
							a       =     hidden_act
							a       =     hidden_dropout_prob
							a       =     attention_probs_dropout_prob
							a       =     max_position_embeddings
							a       =     window_size
							a       =     initializer_range
							a       =     None
							a       =     vocab_size - 1
							a       =     vocab_size - 1
							a       =     vocab_size - 1





				def 				__UpperCAmelCase				( self							:	Dict				)   ->	Union[str, Any]:
							a       =     ids_tensor([self.batch_size, self.seq_length]   , self.vocab_size				)

							a       =     None
							if self.use_input_mask:
										a       =     random_attention_mask([self.batch_size, self.seq_length]				)

							a       =     GPTNeoConfig(
							    vocab_size=self.vocab_size   , hidden_size=self.hidden_size   , num_layers=self.num_hidden_layers   , num_heads=self.num_attention_heads   , max_position_embeddings=self.max_position_embeddings   , use_cache=__lowerCamelCase   , bos_token_id=self.bos_token_id   , eos_token_id=self.eos_token_id   , pad_token_id=self.pad_token_id   , window_size=self.window_size   , attention_types=self.attention_types   , )

							return (config, input_ids, input_mask)





				def 				__UpperCAmelCase				( self							:	List[Any]				)   ->	List[str]:
							a       =     self.prepare_config_and_inputs()
							a       ,						a       ,						a       =     config_and_inputs
							a       =     {"input_ids": input_ids, "attention_mask": attention_mask}
							return config, inputs_dict





				def 				__UpperCAmelCase				( self							:	Optional[int]   , __lowerCamelCase							:	Union[str, Any]   , __lowerCamelCase							:	Tuple   , __lowerCamelCase							:	str   , __lowerCamelCase							:	Dict				)   ->	str:
							a       =     20
							a       =     model_class_name(__lowerCamelCase				)

							a       =     model.init_cache(input_ids.shape[0]   , __lowerCamelCase				)
							a       =     jnp.ones((input_ids.shape[0], max_decoder_length)   , dtype="i4"				)

							a       =     jnp.broadcast_to(
							    jnp.arange(input_ids.shape[-1] - 1				)[None, :]   , (input_ids.shape[0], input_ids.shape[-1] - 1)				)
							a       =     model(
							    input_ids[:, :-1]   , attention_mask=__lowerCamelCase   , past_key_values=__lowerCamelCase   , position_ids=__lowerCamelCase   , )

							a       =     jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]   , dtype="i4"				)
							a       =     model(
							    input_ids[:, -1:]   , attention_mask=__lowerCamelCase   , past_key_values=outputs_cache.past_key_values   , position_ids=__lowerCamelCase   , )

							a       =     model(__lowerCamelCase				)

							a       =     np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])				)				)
							self.parent.assertTrue(diff < 1e-3   , msg=f"""Max diff is {diff}"""				)





				def 				__UpperCAmelCase				( self							:	Tuple   , __lowerCamelCase							:	Optional[Any]   , __lowerCamelCase							:	Dict   , __lowerCamelCase							:	List[str]   , __lowerCamelCase							:	int				)   ->	Union[str, Any]:
							a       =     20
							a       =     model_class_name(__lowerCamelCase				)

							a       =     jnp.concatenate(
							    [attention_mask, jnp.zeros((attention_mask.shape[0], max_decoder_length - attention_mask.shape[1])				)]   , axis=-1   , )

							a       =     model.init_cache(input_ids.shape[0]   , __lowerCamelCase				)
							a       =     jnp.broadcast_to(
							    jnp.arange(input_ids.shape[-1] - 1				)[None, :]   , (input_ids.shape[0], input_ids.shape[-1] - 1)				)

							a       =     model(
							    input_ids[:, :-1]   , attention_mask=__lowerCamelCase   , past_key_values=__lowerCamelCase   , position_ids=__lowerCamelCase   , )
							a       =     jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]   , dtype="i4"				)
							a       =     model(
							    input_ids[:, -1:]   , past_key_values=outputs_cache.past_key_values   , attention_mask=__lowerCamelCase   , position_ids=__lowerCamelCase   , )

							a       =     model(__lowerCamelCase   , attention_mask=__lowerCamelCase				)

							a       =     np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])				)				)
							self.parent.assertTrue(diff < 1e-3   , msg=f"""Max diff is {diff}"""				)




@require_flax
class        snake_case__				(_UpperCamelCase       ,			_UpperCamelCase       ,			unittest.TestCase  ):
				"""simple docstring"""



				SCREAMING_SNAKE_CASE_			:				Optional[Any]									=   (FlaxGPTNeoModel, FlaxGPTNeoForCausalLM) if is_flax_available() else ()
				SCREAMING_SNAKE_CASE_			:				Optional[Any]									=   (FlaxGPTNeoForCausalLM,) if is_flax_available() else ()





				def 				__UpperCAmelCase				( self							:	str				)   ->	List[Any]:
							a       =     FlaxGPTNeoModelTester(self				)





				def 				__UpperCAmelCase				( self							:	Dict				)   ->	Optional[Any]:
							for model_class_name in self.all_model_classes:
										a       ,						a       ,						a       =     self.model_tester.prepare_config_and_inputs()
										self.model_tester.check_use_cache_forward(__lowerCamelCase   , __lowerCamelCase   , __lowerCamelCase   , __lowerCamelCase				)





				def 				__UpperCAmelCase				( self							:	Union[str, Any]				)   ->	Optional[int]:
							for model_class_name in self.all_model_classes:
										a       ,						a       ,						a       =     self.model_tester.prepare_config_and_inputs()
										self.model_tester.check_use_cache_forward_with_attn_mask(
										    __lowerCamelCase   , __lowerCamelCase   , __lowerCamelCase   , __lowerCamelCase				)





				@slow
				def 				__UpperCAmelCase				( self							:	Any				)   ->	List[Any]:
							a       =     GPTaTokenizer.from_pretrained("gpt2"   , pad_token="<|endoftext|>"   , padding_side="left"				)
							a       =     tokenizer(["Hello this is a long string", "Hey"]   , return_tensors="np"   , padding=__lowerCamelCase   , truncation=__lowerCamelCase				)

							a       =     FlaxGPTNeoForCausalLM.from_pretrained("EleutherAI/gpt-neo-125M"				)
							a       =     False
							a       =     model.config.eos_token_id

							a       =     jax.jit(model.generate				)

							a       =     jit_generate(
							    inputs["input_ids"]   , attention_mask=inputs["attention_mask"]   , pad_token_id=tokenizer.pad_token_id				).sequences

							a       =     tokenizer.batch_decode(__lowerCamelCase   , skip_special_tokens=__lowerCamelCase				)

							a       =     [
							    "Hello this is a long string of text.\n\nI'm trying to get the text of the",
							    "Hey, I'm a little late to the party. I'm going to",
							]

							self.assertListEqual(__lowerCamelCase   , __lowerCamelCase				)





				@is_pt_flax_cross_test
				def 				__UpperCAmelCase				( self							:	Tuple				)   ->	Dict:
							a       ,						a       =     self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
										with self.subTest(model_class.__name__				):
													# prepare inputs
													a       =     self._prepare_for_class(__lowerCamelCase   , __lowerCamelCase				)
													a       =     {k: torch.tensor(v.tolist()				) for k, v in prepared_inputs_dict.items()}

													# load corresponding PyTorch class
													a       =     model_class.__name__[4:]  # Skip the "Flax" at the beginning
													a       =     getattr(__lowerCamelCase   , __lowerCamelCase				)

													a       ,						a       =     pt_inputs["input_ids"].shape
													a       =     np.random.randint(0   , seq_length - 1   , size=(batch_size,)				)
													for batch_idx, start_index in enumerate(__lowerCamelCase				):
																a       =     0
																a       =     1
																a       =     0
																a       =     1
													a       =     pt_model_class(__lowerCamelCase				).eval()
													a       =     model_class(__lowerCamelCase   , dtype=jnp.floataa				)

													a       =     convert_pytorch_state_dict_to_flax(pt_model.state_dict()   , __lowerCamelCase				)
													a       =     fx_state

													with torch.no_grad():
																a       =     pt_model(**__lowerCamelCase				).to_tuple()

													a       =     fx_model(**__lowerCamelCase				).to_tuple()
													self.assertEqual(len(__lowerCamelCase				)   , len(__lowerCamelCase				)   , "Output lengths differ between Flax and PyTorch"				)
													for fx_output, pt_output in zip(__lowerCamelCase   , __lowerCamelCase				):
																self.assert_almost_equals(fx_output[:, -1]   , pt_output[:, -1].numpy()   , 4e-2				)

													with tempfile.TemporaryDirectory() as tmpdirname:
																pt_model.save_pretrained(__lowerCamelCase				)
																a       =     model_class.from_pretrained(__lowerCamelCase   , from_pt=__lowerCamelCase				)

													a       =     fx_model_loaded(**__lowerCamelCase				).to_tuple()
													self.assertEqual(
													    len(__lowerCamelCase				)   , len(__lowerCamelCase				)   , "Output lengths differ between Flax and PyTorch"				)
													for fx_output_loaded, pt_output in zip(__lowerCamelCase   , __lowerCamelCase				):
																self.assert_almost_equals(fx_output_loaded[:, -1]   , pt_output[:, -1].numpy()   , 4e-2				)





				@is_pt_flax_cross_test
				def 				__UpperCAmelCase				( self							:	Optional[int]				)   ->	Dict:
							a       ,						a       =     self.model_tester.prepare_config_and_inputs_for_common()
							for model_class in self.all_model_classes:
										with self.subTest(model_class.__name__				):
													# prepare inputs
													a       =     self._prepare_for_class(__lowerCamelCase   , __lowerCamelCase				)
													a       =     {k: torch.tensor(v.tolist()				) for k, v in prepared_inputs_dict.items()}

													# load corresponding PyTorch class
													a       =     model_class.__name__[4:]  # Skip the "Flax" at the beginning
													a       =     getattr(__lowerCamelCase   , __lowerCamelCase				)

													a       =     pt_model_class(__lowerCamelCase				).eval()
													a       =     model_class(__lowerCamelCase   , dtype=jnp.floataa				)

													a       =     load_flax_weights_in_pytorch_model(__lowerCamelCase   , fx_model.params				)
													a       ,						a       =     pt_inputs["input_ids"].shape
													a       =     np.random.randint(0   , seq_length - 1   , size=(batch_size,)				)
													for batch_idx, start_index in enumerate(__lowerCamelCase				):
																a       =     0
																a       =     1
																a       =     0
																a       =     1

													# make sure weights are tied in PyTorch
													pt_model.tie_weights()

													with torch.no_grad():
																a       =     pt_model(**__lowerCamelCase				).to_tuple()

													a       =     fx_model(**__lowerCamelCase				).to_tuple()
													self.assertEqual(len(__lowerCamelCase				)   , len(__lowerCamelCase				)   , "Output lengths differ between Flax and PyTorch"				)
													for fx_output, pt_output in zip(__lowerCamelCase   , __lowerCamelCase				):
																self.assert_almost_equals(fx_output[:, -1]   , pt_output[:, -1].numpy()   , 4e-2				)

													with tempfile.TemporaryDirectory() as tmpdirname:
																fx_model.save_pretrained(__lowerCamelCase				)
																a       =     pt_model_class.from_pretrained(__lowerCamelCase   , from_flax=__lowerCamelCase				)

													with torch.no_grad():
																a       =     pt_model_loaded(**__lowerCamelCase				).to_tuple()

													self.assertEqual(
													    len(__lowerCamelCase				)   , len(__lowerCamelCase				)   , "Output lengths differ between Flax and PyTorch"				)
													for fx_output, pt_output in zip(__lowerCamelCase   , __lowerCamelCase				):
																self.assert_almost_equals(fx_output[:, -1]   , pt_output[:, -1].numpy()   , 4e-2				)





				@slow
				def 				__UpperCAmelCase				( self							:	Optional[Any]				)   ->	str:
							for model_class_name in self.all_model_classes:
										a       =     model_class_name.from_pretrained("EleutherAI/gpt-neo-125M"				)
										a       =     model(np.ones((1, 1)				)				)
										self.assertIsNotNone(__lowerCamelCase				)












import argparse

import torch

from transformers import ChineseCLIPConfig, ChineseCLIPModel




def      __magic_name__							(					A						:   str,    A						:   List[str],    A						:   int  ):






			'''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      __magic_name__							(					A						:   Optional[int],    A						:   str,    A						:   Union[str, Any]  ):






			'''simple docstring'''







			copy_linear(hf_mlp.fca,    A,    F"""{prefix}.c_fc"""  )
			copy_linear(hf_mlp.fca,    A,    F"""{prefix}.c_proj"""  )




def      __magic_name__							(					A						:   int,    A						:   Tuple,    A						:   Optional[int]  ):






			'''simple docstring'''







			a       =     pt_weights[F"""{prefix}.weight"""].data
			a       =     pt_weights[F"""{prefix}.bias"""].data




def      __magic_name__							(					A						:   List[Any],    A						:   List[str],    A						:   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      __magic_name__							(					A						:   int,    A						:   List[str],    A						:   Any  ):






			'''simple docstring'''







			for layer_id, hf_layer in enumerate(A  ):
						copy_layer(A,    A,    F"""{prefix}.{layer_id}"""  )




def      __magic_name__							(					A						:   Any,    A						:   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      __magic_name__							(					A						:   Union[str, Any],    A						:   Optional[Any]  ):






			'''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      __magic_name__							(					A						:   Optional[Any],    A						:   List[str],    A						:   Dict=None  ):






			'''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__":
		__lowerCAmelCase						:							Dict			     =						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.'
		)
		__lowerCAmelCase						:							Dict			     =						parser.parse_args()

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






