





import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import OPTConfig, is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, slow

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


if is_flax_available():
 import os

 # The slow tests are often failing with OOM error on GPU
 # This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
 # but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
 _lowerCAmelCase							:      List[Any]						=	'''platform'''

 import jax
 import jax.numpy as jnp

 from transformers import FlaxOPTForCausalLM, FlaxOPTModel, GPTaTokenizer

def      __snake_case						(					_lowerCAmelCase : List[str]				,				_lowerCAmelCase : int				,				_lowerCAmelCase : Dict=None				,				_lowerCAmelCase : Dict=None	) ->						Tuple:

   '''simple docstring'''





   if attention_mask is None:
      A_					:				Tuple          =			np.where(input_ids != config.pad_token_id				,				1				,				0	)
   return {
       "input_ids": input_ids,
       "attention_mask": attention_mask,
   }


@require_flax
class 					__magic_name__  :





     """simple docstring"""



     def __init__(							self       :Dict			,      snake_case       :List[Any]			,      snake_case       :str=13			,      snake_case       :Dict=7			,      snake_case       :List[Any]=True			,      snake_case       :Dict=False			,      snake_case       :List[Any]=99			,      snake_case       :Dict=16			,      snake_case       :Optional[Any]=2			,      snake_case       :Tuple=4			,      snake_case       :List[Any]=4			,      snake_case       :List[Any]="gelu"			,      snake_case       :List[str]=0.1			,      snake_case       :Optional[int]=0.1			,      snake_case       :Union[str, Any]=20			,      snake_case       :Dict=2			,      snake_case       :Any=1			,      snake_case       :Tuple=0			,      snake_case       :Optional[Any]=16			,      snake_case       :Union[str, Any]=16			,      snake_case       :Optional[Any]=0.02			,      ):





        '''simple docstring'''




        A_					:				str          =			parent
        A_					:				Tuple          =			batch_size
        A_					:				Dict          =			seq_length
        A_					:				Any          =			is_training
        A_					:				Optional[Any]          =			use_labels
        A_					:				Optional[Any]          =			vocab_size
        A_					:				Optional[int]          =			hidden_size
        A_					:				Dict          =			num_hidden_layers
        A_					:				Dict          =			num_attention_heads
        A_					:				Tuple          =			intermediate_size
        A_					:				Tuple          =			hidden_act
        A_					:				Union[str, Any]          =			hidden_dropout_prob
        A_					:				Tuple          =			attention_probs_dropout_prob
        A_					:				Any          =			max_position_embeddings
        A_					:				Tuple          =			eos_token_id
        A_					:				Any          =			pad_token_id
        A_					:				Tuple          =			bos_token_id
        A_					:				str          =			embed_dim
        A_					:				List[str]          =			word_embed_proj_dim
        A_					:				Dict          =			initializer_range
        A_					:				Any          =			False



     def 						SCREAMING_SNAKE_CASE		(							self       :List[Any]  ):





        '''simple docstring'''




        A_					:				str          =			np.clip(ids_tensor([self.batch_size, self.seq_length - 1]			,      self.vocab_size  )			,      3			,      self.vocab_size  )
        A_					:				Union[str, Any]          =			np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)			,      dtype=np.intaa  ))			,      -1  )

        A_					:				Any          =			OPTConfig(
            vocab_size=self.vocab_size			,      hidden_size=self.hidden_size			,      num_hidden_layers=self.num_hidden_layers			,      num_attention_heads=self.num_attention_heads			,      ffn_dim=self.intermediate_size			,      dropout=self.hidden_dropout_prob			,      attention_dropout=self.attention_probs_dropout_prob			,      max_position_embeddings=self.max_position_embeddings			,      eos_token_id=self.eos_token_id			,      bos_token_id=self.bos_token_id			,      pad_token_id=self.pad_token_id			,      embed_dim=self.embed_dim			,      is_encoder_decoder=snake_case			,      word_embed_proj_dim=self.word_embed_proj_dim			,      initializer_range=self.initializer_range			,      use_cache=snake_case			,      )
        A_					:				List[str]          =			prepare_opt_inputs_dict(snake_case			,      snake_case  )
        return config, inputs_dict



     def 						SCREAMING_SNAKE_CASE		(							self       :Dict  ):





        '''simple docstring'''




        A_					:				List[Any]          =			self.prepare_config_and_inputs()
        return config, inputs_dict



     def 						SCREAMING_SNAKE_CASE		(							self       :List[str]			,      snake_case       :Tuple			,      snake_case       :List[Any]			,      snake_case       :List[str]  ):





        '''simple docstring'''




        A_					:				int          =			20
        A_					:				int          =			model_class_name(snake_case  )

        A_					:				Any          =			inputs_dict["input_ids"]
        A_					:				Union[str, Any]          =			inputs_dict["attention_mask"]

        A_					:				List[Any]          =			model.init_cache(input_ids.shape[0]			,      snake_case  )
        A_					:				Dict          =			jnp.ones((input_ids.shape[0], max_length)			,      dtype="i4"  )

        A_					:				str          =			jnp.broadcast_to(
            jnp.arange(input_ids.shape[-1] - 1  )[None, :]			,      (input_ids.shape[0], input_ids.shape[-1] - 1)			,      )
        A_					:				Union[str, Any]          =			model(
            input_ids[:, :-1]			,      attention_mask=snake_case			,      past_key_values=snake_case			,      position_ids=snake_case			,      )

        A_					:				Union[str, Any]          =			jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]			,      dtype="i4"  )
        A_					:				Dict          =			model(
            input_ids[:, -1:]			,      attention_mask=snake_case			,      past_key_values=outputs_cache.past_key_values			,      position_ids=snake_case			,      )

        A_					:				Union[str, Any]          =			model(snake_case  )

        A_					:				Dict          =			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 						SCREAMING_SNAKE_CASE		(							self       :Union[str, Any]			,      snake_case       :int			,      snake_case       :Dict			,      snake_case       :Any  ):





        '''simple docstring'''




        A_					:				Optional[int]          =			20
        A_					:				Any          =			model_class_name(snake_case  )

        A_					:				List[str]          =			(
            inputs_dict["input_ids"],
            inputs_dict["attention_mask"],
        )

        A_					:				Tuple          =			jnp.concatenate(
            [
                attention_mask,
                jnp.zeros((attention_mask.shape[0], max_length - attention_mask.shape[1])  ),
            ]			,      axis=-1			,      )

        A_					:				Optional[int]          =			model.init_cache(input_ids.shape[0]			,      snake_case  )
        A_					:				Any          =			jnp.broadcast_to(
            jnp.arange(input_ids.shape[-1] - 1  )[None, :]			,      (input_ids.shape[0], input_ids.shape[-1] - 1)			,      )

        A_					:				int          =			model(
            input_ids[:, :-1]			,      attention_mask=snake_case			,      past_key_values=snake_case			,      position_ids=snake_case			,      )
        A_					:				List[Any]          =			jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]			,      dtype="i4"  )
        A_					:				Union[str, Any]          =			model(
            input_ids[:, -1:]			,      past_key_values=outputs_cache.past_key_values			,      attention_mask=snake_case			,      position_ids=snake_case			,      )

        A_					:				str          =			model(snake_case			,      attention_mask=snake_case  )

        A_					:				List[str]          =			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 					__magic_name__  (							lowerCamelCase__    ,				unittest.TestCase    ,				lowerCamelCase__							):





     """simple docstring"""



     __UpperCamelCase					    =     (FlaxOPTModel, FlaxOPTForCausalLM) if is_flax_available() else ()
     __UpperCamelCase					    =     () if is_flax_available() else ()



     def 						SCREAMING_SNAKE_CASE		(							self       :Optional[int]  ):





        '''simple docstring'''




        A_					:				Tuple          =			FlaxOPTModelTester(self  )



     def 						SCREAMING_SNAKE_CASE		(							self       :List[str]  ):





        '''simple docstring'''




        A_					:				List[Any]          =			self.model_tester.prepare_config_and_inputs()
        for model_class in self.all_model_classes:
           self.model_tester.check_use_cache_forward(snake_case			,      snake_case			,      snake_case  )



     def 						SCREAMING_SNAKE_CASE		(							self       :Union[str, Any]  ):





        '''simple docstring'''




        A_					:				Any          =			self.model_tester.prepare_config_and_inputs()
        for model_class in self.all_model_classes:
           self.model_tester.check_use_cache_forward_with_attn_mask(snake_case			,      snake_case			,      snake_case  )





     @slow
     def 						SCREAMING_SNAKE_CASE		(							self       :Optional[int]  ):





        '''simple docstring'''




        for model_class_name in self.all_model_classes:
           A_					:				Dict          =			model_class_name.from_pretrained("facebook/opt-125m"  )
           A_					:				str          =			np.ones((1, 1)  ) * model.config.eos_token_id
           A_					:				List[str]          =			model(snake_case  )
           self.assertIsNotNone(snake_case  )




@require_sentencepiece
@require_flax
class 					__magic_name__  (							unittest.TestCase							):





     """simple docstring"""



     @slow
     def 						SCREAMING_SNAKE_CASE		(							self       :Optional[Any]  ):





        '''simple docstring'''




        A_					:				str          =			FlaxOPTModel.from_pretrained("facebook/opt-350m"  )
        A_					:				int          =			jnp.array([[0, 31_414, 232, 328, 740, 1_140, 12_695, 69, 46_078, 1_588, 2]]  )
        A_					:				int          =			model(input_ids=snake_case  ).last_hidden_state
        A_					:				int          =			(1, 11, 512)
        self.assertEqual(output.shape			,      snake_case  )
        A_					:				List[Any]          =			jnp.array(
            [[-0.2867, -1.9256, -0.3062], [-1.2711, -0.1337, -0.1897], [0.4109, 0.1187, -1.3142]]  )
        self.assertTrue(jnp.allclose(output[:, :3, :3]			,      snake_case			,      atol=4e-2  )  )




@require_flax
@slow
class 					__magic_name__  (							unittest.TestCase							):





     """simple docstring"""



     def 						SCREAMING_SNAKE_CASE		(							self       :str  ):





        '''simple docstring'''




        super().setUp()
        A_					:				Dict          =			"facebook/opt-350m"





     def 						SCREAMING_SNAKE_CASE		(							self       :Optional[int]  ):





        '''simple docstring'''




        A_					:				Any          =			FlaxOPTForCausalLM.from_pretrained(self.path_model  )
        A_					:				List[Any]          =			GPTaTokenizer.from_pretrained(self.path_model  )

        A_					:				int          =			[
            "Today is a beautiful day and I want to",
            "In the city of",
            "Paris is the capital of France and",
            "Computers and mobile phones have taken",
        ]
        # verify that prompt without BOS token is identical to Metaseq -> add_special_tokens=False
        A_					:				Any          =			tokenizer(snake_case			,      return_tensors="jax"			,      padding=snake_case			,      add_special_tokens=snake_case  )
        A_					:				int          =			model(inputs.input_ids			,      attention_mask=inputs.attention_mask  )[0].mean(axis=-1  )
        A_					:				Dict          =			jnp.array(
            [
                [1.3851, -13.8923, -10.5229, -10.7533, -0.2309, -10.2384, -0.5365, -9.0947, -5.1670],
                [-4.7073, -10.6276, -3.9415, -21.5242, -0.2822, -0.2822, -0.2822, -0.2822, -0.2822],
                [0.6247, -3.4229, -8.9179, -1.4297, -14.1650, 1.4146, -9.0218, -0.2703, -0.2703],
                [6.4783, -1.9913, -10.7926, -2.3336, 1.5092, -0.9974, -6.8213, 1.3477, 1.3477],
            ]  )
        self.assertTrue(jnp.allclose(snake_case			,      snake_case			,      atol=4e-2  )  )

        A_					:				Any          =			jax.jit(snake_case  )
        A_					:				List[str]          =			model(inputs.input_ids			,      attention_mask=inputs.attention_mask  )[0].mean(axis=-1  )
        self.assertTrue(jnp.allclose(snake_case			,      snake_case			,      atol=4e-2  )  )




@require_flax
@slow
class 					__magic_name__  (							unittest.TestCase							):





     """simple docstring"""



     @property
     def 						SCREAMING_SNAKE_CASE		(							self       :Optional[int]  ):





        '''simple docstring'''




        return [
            "Today is a beautiful day and I want",
            "In the city of",
            "Paris is the capital of France and",
            "Computers and mobile phones have taken",
        ]



     def 						SCREAMING_SNAKE_CASE		(							self       :int  ):





        '''simple docstring'''




        A_					:				Optional[Any]          =			"facebook/opt-125m"

        A_					:				Optional[int]          =			[
            "Today is a beautiful day and I want to",
            "In the city of New York, the city",
            "Paris is the capital of France and the capital",
            "Computers and mobile phones have taken over the",
        ]

        A_					:				str          =			[]

        A_					:				str          =			FlaxOPTForCausalLM.from_pretrained(snake_case  )
        A_					:				Any          =			GPTaTokenizer.from_pretrained(snake_case  )

        for prompt in self.prompts:
           A_					:				List[str]          =			tokenizer(snake_case			,      return_tensors="jax"  ).input_ids

           A_					:				List[Any]          =			model.generate(snake_case			,      max_length=10  )
           A_					:				Optional[int]          =			generated_ids[0]

           A_					:				str          =			tokenizer.batch_decode(snake_case			,      skip_special_tokens=snake_case  )
           predicted_outputs += generated_string

        self.assertListEqual(snake_case			,      snake_case  )



     def 						SCREAMING_SNAKE_CASE		(							self       :Tuple  ):





        '''simple docstring'''




        A_					:				Dict          =			"facebook/opt-350m"

        A_					:				int          =			[
            "Today is a beautiful day and I want to",
            "In the city of San Francisco, the city",
            "Paris is the capital of France and the capital",
            "Computers and mobile phones have taken over the",
        ]

        A_					:				List[Any]          =			[]
        A_					:				Tuple          =			FlaxOPTForCausalLM.from_pretrained(snake_case  )
        A_					:				Dict          =			GPTaTokenizer.from_pretrained(snake_case  )

        for prompt in self.prompts:
           A_					:				Optional[int]          =			tokenizer(snake_case			,      return_tensors="jax"  ).input_ids

           A_					:				Optional[int]          =			model.generate(snake_case			,      max_length=10  )
           A_					:				Tuple          =			generated_ids[0]

           A_					:				Tuple          =			tokenizer.batch_decode(snake_case			,      skip_special_tokens=snake_case  )
           predicted_outputs += generated_string

        self.assertListEqual(snake_case			,      snake_case  )



     def 						SCREAMING_SNAKE_CASE		(							self       :Dict  ):





        '''simple docstring'''




        A_					:				str          =			"facebook/opt-125m"
        A_					:				Tuple          =			[
            "Today is a beautiful day and I want to thank",
            "In the city of Rome Canaver Canaver Canaver Canaver",
        ]
        A_					:				str          =			FlaxOPTForCausalLM.from_pretrained(snake_case  )
        A_					:				Optional[int]          =			GPTaTokenizer.from_pretrained(snake_case  )
        A_					:				Optional[Any]          =			tokenizer(
            [
                "Today is a beautiful day and I want to",
                "In the city of",
            ]			,      return_tensors="jax"			,      padding=snake_case			,      )

        A_					:				Any          =			jax.jit(model.generate  )

        A_					:				Tuple          =			jit_generate(inputs["input_ids"]			,      attention_mask=inputs["attention_mask"]  ).sequences

        A_					:				Tuple          =			tokenizer.batch_decode(snake_case			,      skip_special_tokens=snake_case  )

        self.assertIsNotNone(snake_case			,      snake_case  )





     def 						SCREAMING_SNAKE_CASE		(							self       :Union[str, Any]  ):





        '''simple docstring'''




        A_					:				Optional[int]          =			"facebook/opt-350m"

        A_					:				Any          =			GPTaTokenizer.from_pretrained(snake_case  )
        A_					:				int          =			FlaxOPTForCausalLM.from_pretrained(snake_case  )

        A_					:				Tuple          =			"left"

        # use different length sentences to test batching
        A_					:				Tuple          =			[
            "Hello, my dog is a little",
            "Today, I",
        ]

        A_					:				List[Any]          =			tokenizer(snake_case			,      return_tensors="jax"			,      padding=snake_case  )
        A_					:				Any          =			inputs["input_ids"]

        A_					:				int          =			model.generate(input_ids=snake_case			,      attention_mask=inputs["attention_mask"]			,      trace=snake_case  )

        A_					:				Optional[int]          =			tokenizer(sentences[0]			,      return_tensors="jax"  ).input_ids
        A_					:				Dict          =			model.generate(input_ids=snake_case  )

        A_					:				int          =			inputs_non_padded.shape[-1] - inputs["attention_mask"][-1].sum()
        A_					:				Union[str, Any]          =			tokenizer(sentences[1]			,      return_tensors="jax"  ).input_ids
        A_					:				Optional[int]          =			model.generate(input_ids=snake_case			,      max_length=model.config.max_length - num_paddings  )

        A_					:				List[Any]          =			tokenizer.batch_decode(outputs[0]			,      skip_special_tokens=snake_case  )
        A_					:				Dict          =			tokenizer.decode(output_non_padded[0][0]			,      skip_special_tokens=snake_case  )
        A_					:				Any          =			tokenizer.decode(output_padded[0][0]			,      skip_special_tokens=snake_case  )

        A_					:				Optional[Any]          =			[
            "Hello, my dog is a little bit of a dork.\nI'm a little bit",
            "Today, I was in the middle of a conversation with a friend about the",
        ]
        self.assertListEqual(snake_case			,      snake_case  )
        self.assertListEqual(snake_case			,      [non_padded_sentence, padded_sentence]  )







import argparse

import torch

from transformers import ChineseCLIPConfig, ChineseCLIPModel

def      __snake_case						(					_lowerCAmelCase : str				,				_lowerCAmelCase : Optional[int]				,				_lowerCAmelCase : Optional[Any]	) ->						List[str]:
   A_      ,						A_      ,						A_					:				Dict          =			pt_weights[f"{prefix}.in_proj_weight"].chunk(3				,				dim=0	)
   A_      ,						A_      ,						A_					:				Union[str, Any]          =			pt_weights[f"{prefix}.in_proj_bias"].chunk(3				,				dim=0	)

   A_					:				int          =			pt_weights[f"{prefix}.out_proj.weight"]
   A_					:				Dict          =			pt_weights[f"{prefix}.out_proj.bias"]

   A_					:				Optional[int]          =			q_proj
   A_					:				Optional[int]          =			q_proj_bias

   A_					:				Tuple          =			k_proj
   A_					:				str          =			k_proj_bias

   A_					:				Optional[int]          =			v_proj
   A_					:				int          =			v_proj_bias

   A_					:				int          =			out_proj_weights
   A_					:				List[Any]          =			out_proj_bias

def      __snake_case						(					_lowerCAmelCase : int				,				_lowerCAmelCase : Dict				,				_lowerCAmelCase : int	) ->						Dict:
   copy_linear(hf_mlp.fca				,				_lowerCAmelCase				,				f"{prefix}.c_fc"	)
   copy_linear(hf_mlp.fca				,				_lowerCAmelCase				,				f"{prefix}.c_proj"	)

def      __snake_case						(					_lowerCAmelCase : Tuple				,				_lowerCAmelCase : Optional[Any]				,				_lowerCAmelCase : Optional[int]	) ->						List[str]:
   A_					:				List[str]          =			pt_weights[f"{prefix}.weight"].data
   A_					:				int          =			pt_weights[f"{prefix}.bias"].data

def      __snake_case						(					_lowerCAmelCase : Dict				,				_lowerCAmelCase : str				,				_lowerCAmelCase : Optional[Any]	) ->						str:
   # copy layer norms
   copy_linear(hf_layer.layer_norma				,				_lowerCAmelCase				,				f"{prefix}.ln_1"	)
   copy_linear(hf_layer.layer_norma				,				_lowerCAmelCase				,				f"{prefix}.ln_2"	)

   # copy MLP
   copy_mlp(hf_layer.mlp				,				_lowerCAmelCase				,				f"{prefix}.mlp"	)

   # copy attn
   copy_attn_layer(hf_layer.self_attn				,				_lowerCAmelCase				,				f"{prefix}.attn"	)

def      __snake_case						(					_lowerCAmelCase : Optional[Any]				,				_lowerCAmelCase : int				,				_lowerCAmelCase : Optional[Any]	) ->						List[Any]:
   for layer_id, hf_layer in enumerate(_lowerCAmelCase	):
      copy_layer(_lowerCAmelCase				,				_lowerCAmelCase				,				f"{prefix}.{layer_id}"	)

def      __snake_case						(					_lowerCAmelCase : Union[str, Any]				,				_lowerCAmelCase : Any	) ->						Dict:
   # copy projection
   A_					:				Optional[Any]          =			pt_weights["text_projection"].data.T

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

def      __snake_case						(					_lowerCAmelCase : int				,				_lowerCAmelCase : Any	) ->						List[str]:
   # copy projection
   A_					:				Tuple          =			pt_weights["visual.proj"].data.T

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

   # copy embeddings
   A_					:				List[Any]          =			pt_weights["visual.conv1.weight"].data
   A_					:				Union[str, Any]          =			pt_weights["visual.class_embedding"].data
   A_					:				Tuple          =			pt_weights["visual.positional_embedding"].data

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

@torch.no_grad()
def      __snake_case						(					_lowerCAmelCase : List[Any]				,				_lowerCAmelCase : int				,				_lowerCAmelCase : Optional[int]=None	) ->						str:
   assert config_path is not None, "Please specify the ChineseCLIP model config of the corresponding model size."
   A_					:				Optional[Any]          =			ChineseCLIPConfig.from_pretrained(_lowerCAmelCase	)

   A_					:				Tuple          =			ChineseCLIPModel(_lowerCAmelCase	).eval()

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

   copy_text_model_and_projection(_lowerCAmelCase				,				_lowerCAmelCase	)
   copy_vision_model_and_projection(_lowerCAmelCase				,				_lowerCAmelCase	)
   A_					:				List[Any]          =			pt_weights["logit_scale"].data

   hf_model.save_pretrained(_lowerCAmelCase	)


if __name__ == "__main__":
 _lowerCAmelCase							:      Optional[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.'''
 )
 _lowerCAmelCase							:      Tuple						=	parser.parse_args()

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