




import tempfile
import unittest

import numpy as np

import transformers
from transformers import GPTaConfig, GPTaTokenizer, 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, floats_tensor, 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.gpta.modeling_flax_gpta import FlaxGPTaLMHeadModel, FlaxGPTaModel

if is_torch_available():
      import torch






class       A      :
     """simple docstring"""

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




     def 			__snake_case (     self	: Optional[int]      )					->							Optional[int]:
           lowerCamelCase__   	=					ids_tensor([self.batch_size, self.seq_length]						,	self.vocab_size      )

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

           lowerCamelCase__   	=					GPTaConfig(
               vocab_size=self.vocab_size						,	n_embd=self.hidden_size						,	n_layer=self.num_hidden_layers						,	n_head=self.num_attention_heads						,	n_positions=self.max_position_embeddings						,	use_cache=A__						,	bos_token_id=self.bos_token_id						,	eos_token_id=self.eos_token_id						,	pad_token_id=self.pad_token_id						,	)

           return (config, input_ids, input_mask)




     def 			__snake_case (     self	: Optional[Any]      )					->							Union[str, Any]:
           lowerCamelCase__   	=					self.prepare_config_and_inputs()
           lowerCamelCase__	,		lowerCamelCase__	,		lowerCamelCase__   	=					config_and_inputs
           lowerCamelCase__   	=					{'input_ids': input_ids, 'attention_mask': attention_mask}
           return config, inputs_dict




     def 			__snake_case (     self	: Union[str, Any]      )					->							List[Any]:
           lowerCamelCase__	,		lowerCamelCase__	,		lowerCamelCase__   	=					self.prepare_config_and_inputs()

           lowerCamelCase__   	=					floats_tensor([self.batch_size, self.seq_length, self.hidden_size]      )
           lowerCamelCase__   	=					ids_tensor([self.batch_size, self.seq_length]						,	vocab_size=2      )

           return (
               config,
               input_ids,
               attention_mask,
               encoder_hidden_states,
               encoder_attention_mask,
           )




     def 			__snake_case (     self	: Tuple						,	A__	: Any						,	A__	: Union[str, Any]						,	A__	: Any						,	A__	: Union[str, Any]      )					->							List[str]:
           lowerCamelCase__   	=					2_0
           lowerCamelCase__   	=					model_class_name(A__      )

           lowerCamelCase__   	=					model.init_cache(input_ids.shape[0]						,	A__      )
           lowerCamelCase__   	=					jnp.ones((input_ids.shape[0], max_decoder_length)						,	dtype='i4'      )

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

           lowerCamelCase__   	=					jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]						,	dtype='i4'      )
           lowerCamelCase__   	=					model(
               input_ids[:, -1:]						,	attention_mask=A__						,	past_key_values=outputs_cache.past_key_values						,	position_ids=A__						,	)

           lowerCamelCase__   	=					model(A__      )

           lowerCamelCase__   	=					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 			__snake_case (     self	: Dict						,	A__	: int						,	A__	: str						,	A__	: Optional[int]						,	A__	: Dict      )					->							List[str]:
           lowerCamelCase__   	=					2_0
           lowerCamelCase__   	=					model_class_name(A__      )

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

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

           lowerCamelCase__   	=					model(
               input_ids[:, :-1]						,	attention_mask=A__						,	past_key_values=A__						,	position_ids=A__						,	)
           lowerCamelCase__   	=					jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]						,	dtype='i4'      )
           lowerCamelCase__   	=					model(
               input_ids[:, -1:]						,	past_key_values=outputs_cache.past_key_values						,	attention_mask=A__						,	position_ids=A__						,	)

           lowerCamelCase__   	=					model(A__						,	attention_mask=A__      )

           lowerCamelCase__   	=					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       A      (		_lowerCAmelCase      ,    _lowerCAmelCase      ,    unittest.TestCase		):
     """simple docstring"""

     _snake_case:						Union[str, Any]  =(FlaxGPTaModel, FlaxGPTaLMHeadModel) if is_flax_available() else ()
     _snake_case:						Dict  =(FlaxGPTaLMHeadModel,) if is_flax_available() else ()




     def 			__snake_case (     self	: List[str]      )					->							Tuple:
           lowerCamelCase__   	=					FlaxGPTaModelTester(self      )




     def 			__snake_case (     self	: Any      )					->							Union[str, Any]:
           for model_class_name in self.all_model_classes:
                 lowerCamelCase__	,		lowerCamelCase__	,		lowerCamelCase__   	=					self.model_tester.prepare_config_and_inputs()
                 self.model_tester.check_use_cache_forward(A__						,	A__						,	A__						,	A__      )




     def 			__snake_case (     self	: int      )					->							str:
           for model_class_name in self.all_model_classes:
                 lowerCamelCase__	,		lowerCamelCase__	,		lowerCamelCase__   	=					self.model_tester.prepare_config_and_inputs()
                 self.model_tester.check_use_cache_forward_with_attn_mask(
                     A__						,	A__						,	A__						,	A__      )




     @slow
     def 			__snake_case (     self	: List[Any]      )					->							Optional[Any]:
           lowerCamelCase__   	=					GPTaTokenizer.from_pretrained('gpt2'						,	pad_token='</s>'						,	padding_side='left'      )
           lowerCamelCase__   	=					tokenizer(['Hello this is a long string', 'Hey']						,	return_tensors='np'						,	padding=A__						,	truncation=A__      )

           lowerCamelCase__   	=					FlaxGPTaLMHeadModel.from_pretrained('gpt2'      )
           lowerCamelCase__   	=					False
           lowerCamelCase__   	=					model.config.eos_token_id

           lowerCamelCase__   	=					jax.jit(model.generate      )

           lowerCamelCase__   	=					jit_generate(inputs['input_ids']						,	attention_mask=inputs['attention_mask']      ).sequences

           lowerCamelCase__   	=					tokenizer.batch_decode(A__						,	skip_special_tokens=A__      )

           lowerCamelCase__   	=					[
               'Hello this is a long string of words. I\'m going to start with the first one.\n',
               'Hey, I\'m not sure if I\'m going to be able to do',
           ]

           self.assertListEqual(A__						,	A__      )




     @is_pt_flax_cross_test
     def 			__snake_case (     self	: Dict      )					->							Dict:
           lowerCamelCase__	,		lowerCamelCase__   	=					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
                       lowerCamelCase__   	=					self._prepare_for_class(A__						,	A__      )
                       lowerCamelCase__   	=					{k: torch.tensor(v.tolist()      ) for k, v in prepared_inputs_dict.items()}

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

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

                       lowerCamelCase__   	=					convert_pytorch_state_dict_to_flax(pt_model.state_dict()						,	A__      )
                       lowerCamelCase__   	=					fx_state

                       with torch.no_grad():
                             lowerCamelCase__   	=					pt_model(**A__      ).to_tuple()

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

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

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




     @is_pt_flax_cross_test
     def 			__snake_case (     self	: Optional[Any]      )					->							str:
           lowerCamelCase__	,		lowerCamelCase__   	=					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
                       lowerCamelCase__   	=					self._prepare_for_class(A__						,	A__      )
                       lowerCamelCase__   	=					{k: torch.tensor(v.tolist()      ) for k, v in prepared_inputs_dict.items()}

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

                       lowerCamelCase__   	=					pt_model_class(A__      ).eval()
                       lowerCamelCase__   	=					model_class(A__						,	dtype=jnp.floataa      )

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

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

                       with torch.no_grad():
                             lowerCamelCase__   	=					pt_model(**A__      ).to_tuple()

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

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

                       with torch.no_grad():
                             lowerCamelCase__   	=					pt_model_loaded(**A__      ).to_tuple()

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




     @slow
     def 			__snake_case (     self	: Tuple      )					->							Dict:
           for model_class_name in self.all_model_classes:
                 lowerCamelCase__   	=					model_class_name.from_pretrained('gpt2'						,	from_pt=A__      )
                 lowerCamelCase__   	=					model(np.ones((1, 1)      )      )
                 self.assertIsNotNone(A__      )





def       lowerCAmelCase_      (	lowerCamelCase:							str    )       ->      int:


      '''simple docstring'''




      lowerCamelCase__   	=					53_81
      for x in s:
            lowerCamelCase__   	=					((hash_value << 5) + hash_value) + ord(lowerCamelCase    )
      return hash_value & 0Xffff_ffff