






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:      Optional[Any]						,   __A:      Dict						,   __A:      int=14						,   __A:      str=7						,   __A:      List[str]=True						,   __A:      Any=True						,   __A:      Any=False						,   __A:      str=True						,   __A:      int=99						,   __A:      Optional[Any]=32						,   __A:      int=5						,   __A:      Dict=4						,   __A:      Optional[Any]=37						,   __A:      Optional[int]="gelu"						,   __A:      Any=0.1						,   __A:      Optional[int]=0.1						,   __A:      Optional[int]=5_12						,   __A:      List[str]=0.02						,   )			-> List[Any]:
												_A									=       parent
												_A									=       batch_size
												_A									=       seq_length
												_A									=       is_training
												_A									=       use_input_mask
												_A									=       use_token_type_ids
												_A									=       use_labels
												_A									=       vocab_size
												_A									=       hidden_size
												_A									=       num_hidden_layers
												_A									=       num_attention_heads
												_A									=       intermediate_size
												_A									=       hidden_act
												_A									=       hidden_dropout_prob
												_A									=       attention_probs_dropout_prob
												_A									=       max_position_embeddings
												_A									=       initializer_range
												_A									=       None
												_A									=       vocab_size - 1
												_A									=       vocab_size - 1
												_A									=       vocab_size - 1

							def 					__A       (     self:      Tuple      )			-> str:
												_A									=       ids_tensor([self.batch_size, self.seq_length]						,   self.vocab_size      )

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

												_A									=       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 					__A       (     self:      Dict      )			-> List[str]:
												_A									=       self.prepare_config_and_inputs()
												_A							,_A							,_A									=       config_and_inputs
												_A									=       {'''input_ids''': input_ids, '''attention_mask''': attention_mask}
												return config, inputs_dict

							def 					__A       (     self:      Optional[int]      )			-> Dict:
												_A							,_A							,_A									=       self.prepare_config_and_inputs()

												_A									=       floats_tensor([self.batch_size, self.seq_length, self.hidden_size]      )
												_A									=       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:      Any						,   __A:      Union[str, Any]						,   __A:      List[str]						,   __A:      Tuple						,   __A:      Dict      )			-> str:
												_A									=       20
												_A									=       model_class_name(__A      )

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

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

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

												_A									=       model(__A      )

												_A									=       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:      Tuple						,   __A:      Tuple						,   __A:      Optional[int]						,   __A:      List[str]						,   __A:      str      )			-> List[str]:
												_A									=       20
												_A									=       model_class_name(__A      )

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

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

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

												_A									=       model(__A						,   attention_mask=__A      )

												_A									=       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"""

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

							def 					__A       (     self:      Tuple      )			-> List[str]:
												_A									=       FlaxGPTaModelTester(self      )

							def 					__A       (     self:      Any      )			-> Optional[int]:
												for model_class_name in self.all_model_classes:
																	_A							,_A							,_A									=       self.model_tester.prepare_config_and_inputs()
																	self.model_tester.check_use_cache_forward(__A						,   __A						,   __A						,   __A      )

							def 					__A       (     self:      Optional[int]      )			-> Union[str, Any]:
												for model_class_name in self.all_model_classes:
																	_A							,_A							,_A									=       self.model_tester.prepare_config_and_inputs()
																	self.model_tester.check_use_cache_forward_with_attn_mask(
																	    __A						,   __A						,   __A						,   __A      )

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

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

												_A									=       jax.jit(model.generate      )

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

												_A									=       tokenizer.batch_decode(__A						,   skip_special_tokens=__A      )

												_A									=       [
												    '''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 					__A       (     self:      List[Any]      )			-> List[Any]:
												_A							,_A									=       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
																						_A									=       self._prepare_for_class(__A						,   __A      )
																						_A									=       {k: torch.tensor(v.tolist()      ) for k, v in prepared_inputs_dict.items()}

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

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

																						_A									=       convert_pytorch_state_dict_to_flax(pt_model.state_dict()						,   __A      )
																						_A									=       fx_state

																						with torch.no_grad():
																											_A									=       pt_model(**__A      ).to_tuple()

																						_A									=       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      )
																											_A									=       model_class.from_pretrained(__A						,   from_pt=__A      )

																						_A									=       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 					__A       (     self:      List[Any]      )			-> int:
												_A							,_A									=       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
																						_A									=       self._prepare_for_class(__A						,   __A      )
																						_A									=       {k: torch.tensor(v.tolist()      ) for k, v in prepared_inputs_dict.items()}

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

																						_A									=       pt_model_class(__A      ).eval()
																						_A									=       model_class(__A						,   dtype=jnp.floataa      )

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

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

																						with torch.no_grad():
																											_A									=       pt_model(**__A      ).to_tuple()

																						_A									=       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      )
																											_A									=       pt_model_class.from_pretrained(__A						,   from_flax=__A      )

																						with torch.no_grad():
																											_A									=       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 					__A       (     self:      Tuple      )			-> Optional[Any]:
												for model_class_name in self.all_model_classes:
																	_A									=       model_class_name.from_pretrained('''gpt2'''						,   from_pt=__A      )
																	_A									=       model(np.ones((1, 1)      )      )
																	self.assertIsNotNone(__A      )











from __future__ import annotations

from collections.abc import Generator




def    __A    (   _lowercase       ):

					'''simple docstring'''







					if not isinstance(_lowercase    ,	_lowercase       ) or n < 1:
										raise Exception('''Sequence only defined for positive integers'''       )

					yield n
					while n != 1:
										if n % 2 == 0:
															n //= 2
										else:
															_A									=       3 * n + 1
										yield n




def    __A    (   ):

					'''simple docstring'''







					_A									=       43
					_A									=       tuple(collatz_sequence(_lowercase       )       )
					print(_lowercase       )
					print(f"""Collatz sequence from {n} took {len(_lowercase       )} steps."""       )


if __name__ == "__main__":
				main()



