

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 		a_	:



      """simple docstring"""







      def __init__(				self					:							str      ,snake_case					:							Optional[int]      ,snake_case					:							List[Any]=14      ,snake_case					:							Any=7      ,snake_case					:							Union[str, Any]=True      ,snake_case					:							Optional[int]=True      ,snake_case					:							Optional[int]=False      ,snake_case					:							List[Any]=True      ,snake_case					:							Dict=99      ,snake_case					:							Union[str, Any]=32      ,snake_case					:							List[Any]=4      ,snake_case					:							Dict=4      ,snake_case					:							Tuple=[[["global", "local"], 2]]      ,snake_case					:							str=37      ,snake_case					:							List[str]="gelu"      ,snake_case					:							Union[str, Any]=0.1      ,snake_case					:							Union[str, Any]=0.1      ,snake_case					:							List[str]=512      ,snake_case					:							List[Any]=7      ,snake_case					:							List[str]=0.02      ,):
           SCREAMING_SNAKE_CASE										=parent
           SCREAMING_SNAKE_CASE										=batch_size
           SCREAMING_SNAKE_CASE										=seq_length
           SCREAMING_SNAKE_CASE										=is_training
           SCREAMING_SNAKE_CASE										=use_input_mask
           SCREAMING_SNAKE_CASE										=use_token_type_ids
           SCREAMING_SNAKE_CASE										=use_labels
           SCREAMING_SNAKE_CASE										=vocab_size
           SCREAMING_SNAKE_CASE										=hidden_size
           SCREAMING_SNAKE_CASE										=num_hidden_layers
           SCREAMING_SNAKE_CASE										=num_attention_heads
           SCREAMING_SNAKE_CASE										=attention_types
           SCREAMING_SNAKE_CASE										=intermediate_size
           SCREAMING_SNAKE_CASE										=hidden_act
           SCREAMING_SNAKE_CASE										=hidden_dropout_prob
           SCREAMING_SNAKE_CASE										=attention_probs_dropout_prob
           SCREAMING_SNAKE_CASE										=max_position_embeddings
           SCREAMING_SNAKE_CASE										=window_size
           SCREAMING_SNAKE_CASE										=initializer_range
           SCREAMING_SNAKE_CASE										=None
           SCREAMING_SNAKE_CASE										=vocab_size - 1
           SCREAMING_SNAKE_CASE										=vocab_size - 1
           SCREAMING_SNAKE_CASE										=vocab_size - 1
      def 	_lowerCAmelCase					(				self					:							List[str]    ):
           SCREAMING_SNAKE_CASE										=ids_tensor([self.batch_size, self.seq_length]      ,self.vocab_size    )

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

           SCREAMING_SNAKE_CASE										=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=snake_case      ,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					:							int    ):
           SCREAMING_SNAKE_CASE										=self.prepare_config_and_inputs()
           SCREAMING_SNAKE_CASE						,  SCREAMING_SNAKE_CASE						,  SCREAMING_SNAKE_CASE										=config_and_inputs
           SCREAMING_SNAKE_CASE										={'input_ids': input_ids, 'attention_mask': attention_mask}
           return config, inputs_dict
      def 	_lowerCAmelCase					(				self					:							Optional[Any]      ,snake_case					:							List[str]      ,snake_case					:							Optional[int]      ,snake_case					:							int      ,snake_case					:							List[str]    ):
           SCREAMING_SNAKE_CASE										=20
           SCREAMING_SNAKE_CASE										=model_class_name(snake_case    )

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

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

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

           SCREAMING_SNAKE_CASE										=model(snake_case    )

           SCREAMING_SNAKE_CASE										=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					:							int      ,snake_case					:							List[str]      ,snake_case					:							int      ,snake_case					:							Optional[Any]      ,snake_case					:							int    ):
           SCREAMING_SNAKE_CASE										=20
           SCREAMING_SNAKE_CASE										=model_class_name(snake_case    )

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

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

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

           SCREAMING_SNAKE_CASE										=model(snake_case      ,attention_mask=snake_case    )

           SCREAMING_SNAKE_CASE										=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"""







      __UpperCAmelCase      		=				(FlaxGPTNeoModel, FlaxGPTNeoForCausalLM) if is_flax_available() else ()
      __UpperCAmelCase      		=				(FlaxGPTNeoForCausalLM,) if is_flax_available() else ()
      def 	_lowerCAmelCase					(				self					:							List[Any]    ):
           SCREAMING_SNAKE_CASE										=FlaxGPTNeoModelTester(self    )
      def 	_lowerCAmelCase					(				self					:							int    ):
           for model_class_name in self.all_model_classes:
                SCREAMING_SNAKE_CASE						,  SCREAMING_SNAKE_CASE						,  SCREAMING_SNAKE_CASE										=self.model_tester.prepare_config_and_inputs()
                self.model_tester.check_use_cache_forward(snake_case      ,snake_case      ,snake_case      ,snake_case    )
      def 	_lowerCAmelCase					(				self					:							int    ):
           for model_class_name in self.all_model_classes:
                SCREAMING_SNAKE_CASE						,  SCREAMING_SNAKE_CASE						,  SCREAMING_SNAKE_CASE										=self.model_tester.prepare_config_and_inputs()
                self.model_tester.check_use_cache_forward_with_attn_mask(
                    snake_case      ,snake_case      ,snake_case      ,snake_case    )
      @slow
      def 	_lowerCAmelCase					(				self					:							Optional[Any]    ):
           SCREAMING_SNAKE_CASE										=GPTaTokenizer.from_pretrained('gpt2'      ,pad_token='<|endoftext|>'      ,padding_side='left'    )
           SCREAMING_SNAKE_CASE										=tokenizer(['Hello this is a long string', 'Hey']      ,return_tensors='np'      ,padding=snake_case      ,truncation=snake_case    )

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

           SCREAMING_SNAKE_CASE										=jax.jit(model.generate    )

           SCREAMING_SNAKE_CASE										=jit_generate(
               inputs['input_ids']      ,attention_mask=inputs['attention_mask']      ,pad_token_id=tokenizer.pad_token_id    ).sequences

           SCREAMING_SNAKE_CASE										=tokenizer.batch_decode(snake_case      ,skip_special_tokens=snake_case    )

           SCREAMING_SNAKE_CASE										=[
               '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(snake_case      ,snake_case    )
      @is_pt_flax_cross_test
      def 	_lowerCAmelCase					(				self					:							Dict    ):
           SCREAMING_SNAKE_CASE						,  SCREAMING_SNAKE_CASE										=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
                     SCREAMING_SNAKE_CASE										=self._prepare_for_class(snake_case      ,snake_case    )
                     SCREAMING_SNAKE_CASE										={k: torch.tensor(v.tolist()    ) for k, v in prepared_inputs_dict.items()}

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

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

                     SCREAMING_SNAKE_CASE										=convert_pytorch_state_dict_to_flax(pt_model.state_dict()      ,snake_case    )
                     SCREAMING_SNAKE_CASE										=fx_state

                     with torch.no_grad():
                          SCREAMING_SNAKE_CASE										=pt_model(**snake_case    ).to_tuple()

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

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

                     SCREAMING_SNAKE_CASE										=fx_model_loaded(**snake_case    ).to_tuple()
                     self.assertEqual(
                         len(snake_case    )      ,len(snake_case    )      ,'Output lengths differ between Flax and PyTorch'    )
                     for fx_output_loaded, pt_output in zip(snake_case      ,snake_case    ):
                          self.assert_almost_equals(fx_output_loaded[:, -1]      ,pt_output[:, -1].numpy()      ,4e-2    )
      @is_pt_flax_cross_test
      def 	_lowerCAmelCase					(				self					:							List[Any]    ):
           SCREAMING_SNAKE_CASE						,  SCREAMING_SNAKE_CASE										=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
                     SCREAMING_SNAKE_CASE										=self._prepare_for_class(snake_case      ,snake_case    )
                     SCREAMING_SNAKE_CASE										={k: torch.tensor(v.tolist()    ) for k, v in prepared_inputs_dict.items()}

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

                     SCREAMING_SNAKE_CASE										=pt_model_class(snake_case    ).eval()
                     SCREAMING_SNAKE_CASE										=model_class(snake_case      ,dtype=jnp.floataa    )

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

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

                     with torch.no_grad():
                          SCREAMING_SNAKE_CASE										=pt_model(**snake_case    ).to_tuple()

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

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

                     with torch.no_grad():
                          SCREAMING_SNAKE_CASE										=pt_model_loaded(**snake_case    ).to_tuple()

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


      @slow
      def 	_lowerCAmelCase					(				self					:							List[Any]    ):
           for model_class_name in self.all_model_classes:
                SCREAMING_SNAKE_CASE										=model_class_name.from_pretrained('EleutherAI/gpt-neo-125M'    )
                SCREAMING_SNAKE_CASE										=model(np.ones((1, 1)    )    )
                self.assertIsNotNone(snake_case    )




def       snake_case__    (       lowerCAmelCase_,   lowerCAmelCase_,   lowerCAmelCase_      ):





     """simple docstring"""







     if power < 0:
          return -1
     base %= mod
     SCREAMING_SNAKE_CASE										=1

     while power > 0:
          if power & 1:
               SCREAMING_SNAKE_CASE										=(result * base) % mod
          SCREAMING_SNAKE_CASE										=power >> 1
          SCREAMING_SNAKE_CASE										=(base * base) % mod

     return result







def       snake_case__    (       ):





     """simple docstring"""







     print(modular_exponential(3,   200,   13      )      )


if __name__ == "__main__":
 import doctest

 doctest.testmod()

 main()

