"""simple docstring"""


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   UpperCAmelCase_	:
    def __init__(	self			:       Any   ,						_lowercase			:       Union[str, Any]   ,						_lowercase			:       Any=1_4   ,						_lowercase			:       Optional[Any]=7   ,						_lowercase			:       Optional[int]=True   ,						_lowercase			:       List[str]=True   ,						_lowercase			:       int=False   ,						_lowercase			:       List[str]=True   ,						_lowercase			:       Tuple=9_9   ,						_lowercase			:       int=3_2   ,						_lowercase			:       Tuple=4   ,						_lowercase			:       Tuple=4   ,						_lowercase			:       Any=[[["global", "local"], 2]]   ,						_lowercase			:       Dict=3_7   ,						_lowercase			:       Optional[Any]="gelu"   ,						_lowercase			:       Optional[Any]=0.1   ,						_lowercase			:       int=0.1   ,						_lowercase			:       List[Any]=5_1_2   ,						_lowercase			:       Tuple=7   ,						_lowercase			:       Optional[Any]=0.02   ,						)						->    int:
       _lowercase							     =       parent
       _lowercase							     =       batch_size
       _lowercase							     =       seq_length
       _lowercase							     =       is_training
       _lowercase							     =       use_input_mask
       _lowercase							     =       use_token_type_ids
       _lowercase							     =       use_labels
       _lowercase							     =       vocab_size
       _lowercase							     =       hidden_size
       _lowercase							     =       num_hidden_layers
       _lowercase							     =       num_attention_heads
       _lowercase							     =       attention_types
       _lowercase							     =       intermediate_size
       _lowercase							     =       hidden_act
       _lowercase							     =       hidden_dropout_prob
       _lowercase							     =       attention_probs_dropout_prob
       _lowercase							     =       max_position_embeddings
       _lowercase							     =       window_size
       _lowercase							     =       initializer_range
       _lowercase							     =       None
       _lowercase							     =       vocab_size - 1
       _lowercase							     =       vocab_size - 1
       _lowercase							     =       vocab_size - 1
    def 		_lowerCamelCase      (	self			:       Dict		)						->    Union[str, Any]:
       _lowercase							     =       ids_tensor([self.batch_size, self.seq_length]   ,						self.vocab_size		)

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

       _lowercase							     =       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 		_lowerCamelCase      (	self			:       List[Any]		)						->    List[str]:
       _lowercase							     =       self.prepare_config_and_inputs()
       _lowercase     ,  _lowercase     ,  _lowercase							     =       config_and_inputs
       _lowercase							     =       {"input_ids": input_ids, "attention_mask": attention_mask}
       return config, inputs_dict
    def 		_lowerCamelCase      (	self			:       Optional[int]   ,						_lowercase			:       Union[str, Any]   ,						_lowercase			:       Tuple   ,						_lowercase			:       str   ,						_lowercase			:       Dict		)						->    str:
       _lowercase							     =       2_0
       _lowercase							     =       model_class_name(__lowerCamelCase		)

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

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

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

       _lowercase							     =       model(__lowerCamelCase		)

       _lowercase							     =       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 		_lowerCamelCase      (	self			:       Tuple   ,						_lowercase			:       Optional[Any]   ,						_lowercase			:       Dict   ,						_lowercase			:       List[str]   ,						_lowercase			:       int		)						->    Union[str, Any]:
       _lowercase							     =       2_0
       _lowercase							     =       model_class_name(__lowerCamelCase		)

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

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

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

       _lowercase							     =       model(__lowerCamelCase   ,						attention_mask=__lowerCamelCase		)

       _lowercase							     =       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   UpperCAmelCase_	(				_UpperCamelCase,    _UpperCamelCase,    unittest.TestCase   ):
    snake_case_											=      (FlaxGPTNeoModel, FlaxGPTNeoForCausalLM) if is_flax_available() else ()
    snake_case_											=      (FlaxGPTNeoForCausalLM,) if is_flax_available() else ()
    def 		_lowerCamelCase      (	self			:       str		)						->    List[Any]:
       _lowercase							     =       FlaxGPTNeoModelTester(self		)
    def 		_lowerCamelCase      (	self			:       Dict		)						->    Optional[Any]:
       for model_class_name in self.all_model_classes:
          _lowercase     ,  _lowercase     ,  _lowercase							     =       self.model_tester.prepare_config_and_inputs()
          self.model_tester.check_use_cache_forward(__lowerCamelCase   ,						__lowerCamelCase   ,						__lowerCamelCase   ,						__lowerCamelCase		)
    def 		_lowerCamelCase      (	self			:       Union[str, Any]		)						->    Optional[int]:
       for model_class_name in self.all_model_classes:
          _lowercase     ,  _lowercase     ,  _lowercase							     =       self.model_tester.prepare_config_and_inputs()
          self.model_tester.check_use_cache_forward_with_attn_mask(
              __lowerCamelCase   ,						__lowerCamelCase   ,						__lowerCamelCase   ,						__lowerCamelCase		)
    @slow
    def 		_lowerCamelCase      (	self			:       Any		)						->    List[Any]:
       _lowercase							     =       GPTaTokenizer.from_pretrained("gpt2"   ,						pad_token="<|endoftext|>"   ,						padding_side="left"		)
       _lowercase							     =       tokenizer(["Hello this is a long string", "Hey"]   ,						return_tensors="np"   ,						padding=__lowerCamelCase   ,						truncation=__lowerCamelCase		)

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

       _lowercase							     =       jax.jit(model.generate		)

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

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

       _lowercase							     =       [
           "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 		_lowerCamelCase      (	self			:       Tuple		)						->    Dict:
       _lowercase     ,  _lowercase							     =       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
             _lowercase							     =       self._prepare_for_class(__lowerCamelCase   ,						__lowerCamelCase		)
             _lowercase							     =       {k: torch.tensor(v.tolist()		) for k, v in prepared_inputs_dict.items()}

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

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

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

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

             _lowercase							     =       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		)
                _lowercase							     =       model_class.from_pretrained(__lowerCamelCase   ,						from_pt=__lowerCamelCase		)

             _lowercase							     =       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 		_lowerCamelCase      (	self			:       Optional[int]		)						->    Dict:
       _lowercase     ,  _lowercase							     =       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
             _lowercase							     =       self._prepare_for_class(__lowerCamelCase   ,						__lowerCamelCase		)
             _lowercase							     =       {k: torch.tensor(v.tolist()		) for k, v in prepared_inputs_dict.items()}

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

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

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

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

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

             _lowercase							     =       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		)
                _lowercase							     =       pt_model_class.from_pretrained(__lowerCamelCase   ,						from_flax=__lowerCamelCase		)

             with torch.no_grad():
                _lowercase							     =       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 		_lowerCamelCase      (	self			:       Optional[Any]		)						->    str:
       for model_class_name in self.all_model_classes:
          _lowercase							     =       model_class_name.from_pretrained("EleutherAI/gpt-neo-125M"		)
          _lowercase							     =       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!')






