
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 							SCREAMING_SNAKE_CASE__     :

							"""simple docstring"""






							def __init__(	self  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_=14  ,						SCREAMING_SNAKE_CASE_=7  ,						SCREAMING_SNAKE_CASE_=True  ,						SCREAMING_SNAKE_CASE_=True  ,						SCREAMING_SNAKE_CASE_=False  ,						SCREAMING_SNAKE_CASE_=True  ,						SCREAMING_SNAKE_CASE_=99  ,						SCREAMING_SNAKE_CASE_=32  ,						SCREAMING_SNAKE_CASE_=5  ,						SCREAMING_SNAKE_CASE_=4  ,						SCREAMING_SNAKE_CASE_=37  ,						SCREAMING_SNAKE_CASE_="gelu"  ,						SCREAMING_SNAKE_CASE_=0.1  ,						SCREAMING_SNAKE_CASE_=0.1  ,						SCREAMING_SNAKE_CASE_=512  ,						SCREAMING_SNAKE_CASE_=0.0_2  ,						)-> Tuple:





													'''simple docstring'''






													__UpperCamelCase  	=      parent
													__UpperCamelCase  	=      batch_size
													__UpperCamelCase  	=      seq_length
													__UpperCamelCase  	=      is_training
													__UpperCamelCase  	=      use_input_mask
													__UpperCamelCase  	=      use_token_type_ids
													__UpperCamelCase  	=      use_labels
													__UpperCamelCase  	=      vocab_size
													__UpperCamelCase  	=      hidden_size
													__UpperCamelCase  	=      num_hidden_layers
													__UpperCamelCase  	=      num_attention_heads
													__UpperCamelCase  	=      intermediate_size
													__UpperCamelCase  	=      hidden_act
													__UpperCamelCase  	=      hidden_dropout_prob
													__UpperCamelCase  	=      attention_probs_dropout_prob
													__UpperCamelCase  	=      max_position_embeddings
													__UpperCamelCase  	=      initializer_range
													__UpperCamelCase  	=      None
													__UpperCamelCase  	=      vocab_size - 1
													__UpperCamelCase  	=      vocab_size - 1
													__UpperCamelCase  	=      vocab_size - 1



							def      A__		(	self      )-> Dict:





													'''simple docstring'''






													__UpperCamelCase  	=      ids_tensor([self.batch_size, self.seq_length]  ,						self.vocab_size      )

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

													__UpperCamelCase  	=      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=lowerCAmelCase__  ,						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      A__		(	self      )-> List[str]:





													'''simple docstring'''






													__UpperCamelCase  	=      self.prepare_config_and_inputs()
													__UpperCamelCase  	=      config_and_inputs
													__UpperCamelCase  	=      {"""input_ids""": input_ids, """attention_mask""": attention_mask}
													return config, inputs_dict



							def      A__		(	self      )-> List[str]:





													'''simple docstring'''






													__UpperCamelCase  	=      self.prepare_config_and_inputs()

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

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



							def      A__		(	self  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_      )-> Union[str, Any]:





													'''simple docstring'''






													__UpperCamelCase  	=      20
													__UpperCamelCase  	=      model_class_name(lowerCAmelCase__      )

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

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

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

													__UpperCamelCase  	=      model(lowerCAmelCase__      )

													__UpperCamelCase  	=      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      A__		(	self  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_      )-> Any:





													'''simple docstring'''






													__UpperCamelCase  	=      20
													__UpperCamelCase  	=      model_class_name(lowerCAmelCase__      )

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

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

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

													__UpperCamelCase  	=      model(lowerCAmelCase__  ,						attention_mask=lowerCAmelCase__      )

													__UpperCamelCase  	=      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 							SCREAMING_SNAKE_CASE__     (				__snake_case ,		__snake_case ,		unittest.TestCase     ):

							"""simple docstring"""






							_snake_case  		=	(FlaxGPTaModel, FlaxGPTaLMHeadModel) if is_flax_available() else ()
							_snake_case  		=	(FlaxGPTaLMHeadModel,) if is_flax_available() else ()



							def      A__		(	self      )-> Union[str, Any]:





													'''simple docstring'''






													__UpperCamelCase  	=      FlaxGPTaModelTester(self      )



							def      A__		(	self      )-> List[str]:





													'''simple docstring'''






													for model_class_name in self.all_model_classes:
																			__UpperCamelCase  	=      self.model_tester.prepare_config_and_inputs()
																			self.model_tester.check_use_cache_forward(lowerCAmelCase__  ,						lowerCAmelCase__  ,						lowerCAmelCase__  ,						lowerCAmelCase__      )



							def      A__		(	self      )-> Any:





													'''simple docstring'''






													for model_class_name in self.all_model_classes:
																			__UpperCamelCase  	=      self.model_tester.prepare_config_and_inputs()
																			self.model_tester.check_use_cache_forward_with_attn_mask(
																			    lowerCAmelCase__  ,						lowerCAmelCase__  ,						lowerCAmelCase__  ,						lowerCAmelCase__      )



							@slow
							def      A__		(	self      )-> List[str]:





													'''simple docstring'''






													__UpperCamelCase  	=      GPTaTokenizer.from_pretrained('''gpt2'''  ,						pad_token='''</s>'''  ,						padding_side='''left'''      )
													__UpperCamelCase  	=      tokenizer(['''Hello this is a long string''', '''Hey''']  ,						return_tensors='''np'''  ,						padding=lowerCAmelCase__  ,						truncation=lowerCAmelCase__      )

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

													__UpperCamelCase  	=      jax.jit(model.generate      )

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

													__UpperCamelCase  	=      tokenizer.batch_decode(lowerCAmelCase__  ,						skip_special_tokens=lowerCAmelCase__      )

													__UpperCamelCase  	=      [
													    """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(lowerCAmelCase__  ,						lowerCAmelCase__      )



							@is_pt_flax_cross_test
							def      A__		(	self      )-> Any:





													'''simple docstring'''






													__UpperCamelCase  	=      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
																									__UpperCamelCase  	=      self._prepare_for_class(lowerCAmelCase__  ,						lowerCAmelCase__      )
																									__UpperCamelCase  	=      {k: torch.tensor(v.tolist()      ) for k, v in prepared_inputs_dict.items()}

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

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

																									__UpperCamelCase  	=      convert_pytorch_state_dict_to_flax(pt_model.state_dict()  ,						lowerCAmelCase__      )
																									__UpperCamelCase  	=      fx_state

																									with torch.no_grad():
																															__UpperCamelCase  	=      pt_model(**lowerCAmelCase__      ).to_tuple()

																									__UpperCamelCase  	=      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__      )
																															__UpperCamelCase  	=      model_class.from_pretrained(lowerCAmelCase__  ,						from_pt=lowerCAmelCase__      )

																									__UpperCamelCase  	=      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      A__		(	self      )-> Any:





													'''simple docstring'''






													__UpperCamelCase  	=      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
																									__UpperCamelCase  	=      self._prepare_for_class(lowerCAmelCase__  ,						lowerCAmelCase__      )
																									__UpperCamelCase  	=      {k: torch.tensor(v.tolist()      ) for k, v in prepared_inputs_dict.items()}

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

																									__UpperCamelCase  	=      pt_model_class(lowerCAmelCase__      ).eval()
																									__UpperCamelCase  	=      model_class(lowerCAmelCase__  ,						dtype=jnp.floataa      )

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

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

																									with torch.no_grad():
																															__UpperCamelCase  	=      pt_model(**lowerCAmelCase__      ).to_tuple()

																									__UpperCamelCase  	=      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__      )
																															__UpperCamelCase  	=      pt_model_class.from_pretrained(lowerCAmelCase__  ,						from_flax=lowerCAmelCase__      )

																									with torch.no_grad():
																															__UpperCamelCase  	=      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      A__		(	self      )-> List[Any]:





													'''simple docstring'''






													for model_class_name in self.all_model_classes:
																			__UpperCamelCase  	=      model_class_name.from_pretrained('''gpt2'''  ,						from_pt=lowerCAmelCase__      )
																			__UpperCamelCase  	=      model(np.ones((1, 1)      )      )
																			self.assertIsNotNone(lowerCAmelCase__      )





'''simple docstring'''

from __future__ import annotations

_lowerCamelCase :				Optional[int]														= """Alexander Joslin"""



def  _lowerCAmelCase				(				__a      )  ->      int:
     '''simple docstring'''







     if number <= 0:
          _UpperCamelCase  :Optional[Any]       =F'''n must be greater than 0. Got n = {number}'''
          raise ValueError(__a      )

     _UpperCamelCase  :Optional[Any]       =[number + 1] * (number + 1)

     # starting position
     _UpperCamelCase  :int       =0
     for i in range(1			,      __a      ):
          _UpperCamelCase  :List[str]       =min(table[i + 1]			,      table[i] + 1      )
          # check if out of bounds
          if i * 2 <= number:
               _UpperCamelCase  :int       =min(table[i * 2]			,      table[i] + 1      )
          # check if out of bounds
          if i * 3 <= number:
               _UpperCamelCase  :Tuple       =min(table[i * 3]			,      table[i] + 1      )
     return table[number]


if __name__ == "__main__":
      import doctest

      doctest.testmod()