



"""simple docstring"""





import tempfile
import unittest

import numpy as np

import transformers
from transformers import GPTaTokenizer, GPTJConfig, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, tooslow

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.gptj.modeling_flax_gptj import FlaxGPTJForCausalLM, FlaxGPTJModel

if is_torch_available():
					import torch



class    _lowerCAmelCase    :





		"""simple docstring"""






		def __init__(					self							,    _lowercase							,    _lowercase=1_4							,    _lowercase=7							,    _lowercase=True							,    _lowercase=True							,    _lowercase=False							,    _lowercase=True							,    _lowercase=9_9							,    _lowercase=3_2							,    _lowercase=4							,    _lowercase=4							,    _lowercase=4							,    _lowercase=3_7							,    _lowercase="gelu"							,    _lowercase=0.1							,    _lowercase=0.1							,    _lowercase=5_1_2							,    _lowercase=0.02							,    ) ->						int:







								'''simple docstring'''

								snake_case_   : Optional[Any]						=			parent
								snake_case_   : Optional[int]						=			batch_size
								snake_case_   : List[str]						=			seq_length
								snake_case_   : Union[str, Any]						=			is_training
								snake_case_   : List[Any]						=			use_input_mask
								snake_case_   : List[Any]						=			use_token_type_ids
								snake_case_   : Optional[Any]						=			use_labels
								snake_case_   : Optional[int]						=			vocab_size
								snake_case_   : List[str]						=			hidden_size
								snake_case_   : Any						=			rotary_dim
								snake_case_   : Dict						=			num_hidden_layers
								snake_case_   : Dict						=			num_attention_heads
								snake_case_   : Optional[Any]						=			intermediate_size
								snake_case_   : str						=			hidden_act
								snake_case_   : Dict						=			hidden_dropout_prob
								snake_case_   : Union[str, Any]						=			attention_probs_dropout_prob
								snake_case_   : int						=			max_position_embeddings
								snake_case_   : str						=			initializer_range
								snake_case_   : Optional[int]						=			None
								snake_case_   : int						=			vocab_size - 1
								snake_case_   : Any						=			vocab_size - 1
								snake_case_   : Any						=			vocab_size - 1





		def    UpperCAmelCase__							(					self   ) ->						Optional[int]:







								'''simple docstring'''

								snake_case_   : Optional[Any]						=			ids_tensor([self.batch_size, self.seq_length]							,    self.vocab_size   )

								snake_case_   : List[Any]						=			None
								if self.use_input_mask:
														snake_case_   : Tuple						=			random_attention_mask([self.batch_size, self.seq_length]   )

								snake_case_   : List[Any]						=			GPTJConfig(
								    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							,    rotary_dim=self.rotary_dim							,    )

								return (config, input_ids, input_mask)





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







								'''simple docstring'''

								snake_case_   : Optional[int]						=			self.prepare_config_and_inputs()
								snake_case_			,		snake_case_			,		snake_case_   : Union[str, Any]						=			config_and_inputs
								snake_case_   : Dict						=			{"""input_ids""": input_ids, """attention_mask""": attention_mask}
								return config, inputs_dict





		def    UpperCAmelCase__							(					self							,    _lowercase							,    _lowercase							,    _lowercase							,    _lowercase   ) ->						int:







								'''simple docstring'''

								snake_case_   : Optional[Any]						=			2_0
								snake_case_   : Any						=			model_class_name(A_   )

								snake_case_   : Any						=			model.init_cache(input_ids.shape[0]							,    A_   )
								snake_case_   : Optional[Any]						=			jnp.ones((input_ids.shape[0], max_decoder_length)							,    dtype="""i4"""   )

								snake_case_   : List[Any]						=			jnp.broadcast_to(
								    jnp.arange(input_ids.shape[-1] - 1   )[None, :]							,    (input_ids.shape[0], input_ids.shape[-1] - 1)   )
								snake_case_   : Tuple						=			model(
								    input_ids[:, :-1]							,    attention_mask=A_							,    past_key_values=A_							,    position_ids=A_							,    )

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

								snake_case_   : str						=			model(A_   )

								snake_case_   : Any						=			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    UpperCAmelCase__							(					self							,    _lowercase							,    _lowercase							,    _lowercase							,    _lowercase   ) ->						Optional[Any]:







								'''simple docstring'''

								snake_case_   : List[str]						=			2_0
								snake_case_   : int						=			model_class_name(A_   )

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

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

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

								snake_case_   : Optional[Any]						=			model(A_							,    attention_mask=A_   )

								snake_case_   : 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    _lowerCAmelCase    (				_lowercase  ,				_lowercase  ,				unittest.TestCase		):





		"""simple docstring"""






		_lowerCamelCase			  =       (FlaxGPTJModel, FlaxGPTJForCausalLM) if is_flax_available() else ()
		_lowerCamelCase			  =       (FlaxGPTJForCausalLM,) if is_flax_available() else ()





		def    UpperCAmelCase__							(					self   ) ->						Any:







								'''simple docstring'''

								snake_case_   : str						=			FlaxGPTJModelTester(self   )





		def    UpperCAmelCase__							(					self   ) ->						str:







								'''simple docstring'''

								for model_class_name in self.all_model_classes:
														snake_case_			,		snake_case_			,		snake_case_   : Optional[Any]						=			self.model_tester.prepare_config_and_inputs()
														self.model_tester.check_use_cache_forward(A_							,    A_							,    A_							,    A_   )





		def    UpperCAmelCase__							(					self   ) ->						Tuple:







								'''simple docstring'''

								for model_class_name in self.all_model_classes:
														snake_case_			,		snake_case_			,		snake_case_   : List[str]						=			self.model_tester.prepare_config_and_inputs()
														self.model_tester.check_use_cache_forward_with_attn_mask(
														    A_							,    A_							,    A_							,    A_   )





		@tooslow
		def    UpperCAmelCase__							(					self   ) ->						Any:







								'''simple docstring'''

								snake_case_   : Optional[Any]						=			GPTaTokenizer.from_pretrained("""gpt2"""							,    pad_token="""<|endoftext|>"""							,    padding_side="""left"""   )
								snake_case_   : List[Any]						=			tokenizer(["""Hello this is a long string""", """Hey"""]							,    return_tensors="""np"""							,    padding=A_							,    truncation=A_   )

								snake_case_   : str						=			FlaxGPTJForCausalLM.from_pretrained("""EleutherAI/gpt-j-6B"""   )
								snake_case_   : Optional[int]						=			False
								snake_case_   : Union[str, Any]						=			model.config.eos_token_id

								snake_case_   : int						=			jax.jit(model.generate   )

								snake_case_   : Tuple						=			jit_generate(
								    inputs["""input_ids"""]							,    attention_mask=inputs["""attention_mask"""]							,    pad_token_id=tokenizer.pad_token_id   ).sequences

								snake_case_   : Dict						=			tokenizer.batch_decode(A_							,    skip_special_tokens=A_   )

								snake_case_   : Tuple						=			[
								    """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(A_							,    A_   )





		@is_pt_flax_cross_test
		def    UpperCAmelCase__							(					self   ) ->						List[Any]:







								'''simple docstring'''

								snake_case_			,		snake_case_   : Optional[Any]						=			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
																				snake_case_   : str						=			self._prepare_for_class(A_							,    A_   )
																				snake_case_   : str						=			{k: torch.tensor(v.tolist()   ) for k, v in prepared_inputs_dict.items()}

																				# load corresponding PyTorch class
																				snake_case_   : Tuple						=			model_class.__name__[4:]  # Skip the "Flax" at the beginning
																				snake_case_   : List[Any]						=			getattr(A_							,    A_   )

																				snake_case_			,		snake_case_   : Tuple						=			pt_inputs["""input_ids"""].shape
																				snake_case_   : List[str]						=			np.random.randint(0							,    seq_length - 1							,    size=(batch_size,)   )
																				for batch_idx, start_index in enumerate(A_   ):
																										snake_case_   : List[Any]						=			0
																										snake_case_   : Optional[Any]						=			1
																										snake_case_   : Optional[Any]						=			0
																										snake_case_   : List[Any]						=			1
																				snake_case_   : List[str]						=			pt_model_class(A_   ).eval()
																				snake_case_   : int						=			model_class(A_							,    dtype=jnp.floataa   )

																				snake_case_   : Dict						=			convert_pytorch_state_dict_to_flax(pt_model.state_dict()							,    A_   )
																				snake_case_   : Optional[Any]						=			fx_state

																				with torch.no_grad():
																										snake_case_   : int						=			pt_model(**A_   ).to_tuple()

																				snake_case_   : List[Any]						=			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_   )
																										snake_case_   : Any						=			model_class.from_pretrained(A_							,    from_pt=A_   )

																				snake_case_   : int						=			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    UpperCAmelCase__							(					self   ) ->						Dict:







								'''simple docstring'''

								snake_case_			,		snake_case_   : List[Any]						=			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
																				snake_case_   : Union[str, Any]						=			self._prepare_for_class(A_							,    A_   )
																				snake_case_   : Optional[Any]						=			{k: torch.tensor(v.tolist()   ) for k, v in prepared_inputs_dict.items()}

																				# load corresponding PyTorch class
																				snake_case_   : Dict						=			model_class.__name__[4:]  # Skip the "Flax" at the beginning
																				snake_case_   : int						=			getattr(A_							,    A_   )

																				snake_case_   : List[Any]						=			pt_model_class(A_   ).eval()
																				snake_case_   : Optional[int]						=			model_class(A_							,    dtype=jnp.floataa   )

																				snake_case_   : Optional[int]						=			load_flax_weights_in_pytorch_model(A_							,    fx_model.params   )
																				snake_case_			,		snake_case_   : Any						=			pt_inputs["""input_ids"""].shape
																				snake_case_   : Tuple						=			np.random.randint(0							,    seq_length - 1							,    size=(batch_size,)   )
																				for batch_idx, start_index in enumerate(A_   ):
																										snake_case_   : Dict						=			0
																										snake_case_   : Tuple						=			1
																										snake_case_   : int						=			0
																										snake_case_   : Tuple						=			1

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

																				with torch.no_grad():
																										snake_case_   : Any						=			pt_model(**A_   ).to_tuple()

																				snake_case_   : int						=			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_   )
																										snake_case_   : Optional[int]						=			pt_model_class.from_pretrained(A_							,    from_flax=A_   )

																				with torch.no_grad():
																										snake_case_   : str						=			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   )





		@tooslow
		def    UpperCAmelCase__							(					self   ) ->						str:







								'''simple docstring'''

								for model_class_name in self.all_model_classes:
														snake_case_   : Union[str, Any]						=			model_class_name.from_pretrained("""EleutherAI/gpt-j-6B"""   )
														snake_case_   : int						=			model(np.ones((1, 1)   )   )
														self.assertIsNotNone(A_   )






"""simple docstring"""





def 	__lowerCAmelCase						(		__UpperCamelCase      :			int					, __UpperCamelCase      :			int    ):


						'''simple docstring'''






						if a < 0 or b < 0:
												raise ValueError("""the value of both inputs must be positive"""    )

						snake_case_   : List[str]						=			str(bin(__UpperCamelCase    )    )[2:]  # remove the leading "0b"
						snake_case_   : Union[str, Any]						=			str(bin(__UpperCamelCase    )    )[2:]  # remove the leading "0b"

						snake_case_   : int						=			max(len(__UpperCamelCase    )					, len(__UpperCamelCase    )    )

						return "0b" + "".join(
						    str(int(char_a != char_b    )    )
						    for char_a, char_b in zip(a_binary.zfill(__UpperCamelCase    )					, b_binary.zfill(__UpperCamelCase    )    )    )


if __name__ == "__main__":
					import doctest

					doctest.testmod()

