

'''simple docstring'''







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   :

						def __init__(		self	:						List[str]    ,			lowercase__	:						Optional[Any]    ,			lowercase__	:						Tuple=14    ,			lowercase__	:						str=7    ,			lowercase__	:						Optional[Any]=True    ,			lowercase__	:						int=True    ,			lowercase__	:						Optional[int]=False    ,			lowercase__	:						List[Any]=True    ,			lowercase__	:						List[Any]=99    ,			lowercase__	:						Tuple=32    ,			lowercase__	:						Tuple=4    ,			lowercase__	:						Any=4    ,			lowercase__	:						Any=[[["global", "local"], 2]]    ,			lowercase__	:						Optional[Any]=37    ,			lowercase__	:						Tuple="gelu"    ,			lowercase__	:						str=0.1    ,			lowercase__	:						Optional[Any]=0.1    ,			lowercase__	:						str=5_12    ,			lowercase__	:						List[Any]=7    ,			lowercase__	:						Optional[Any]=0.02    ,			)						->List[str]:

							"""simple docstring"""



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

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

							"""simple docstring"""



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

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

							_lowercase							=     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=lowercase__    ,			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 	_UpperCAmelCase							(		self	:						Any)						->Union[str, Any]:

							"""simple docstring"""



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

						def 	_UpperCAmelCase							(		self	:						str    ,			lowercase__	:						Optional[int]    ,			lowercase__	:						List[Any]    ,			lowercase__	:						Optional[Any]    ,			lowercase__	:						Dict)						->Tuple:

							"""simple docstring"""



							_lowercase							=     20
							_lowercase							=     model_class_name(lowercase__)

							_lowercase							=     model.init_cache(input_ids.shape[0]    ,			lowercase__)
							_lowercase							=     jnp.ones((input_ids.shape[0], max_decoder_length)    ,			dtype="""i4""")

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

							_lowercase							=     jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]    ,			dtype="""i4""")
							_lowercase							=     model(
							    input_ids[:, -1:]    ,			attention_mask=lowercase__    ,			past_key_values=outputs_cache.past_key_values    ,			position_ids=lowercase__    ,			)

							_lowercase							=     model(lowercase__)

							_lowercase							=     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	:						Optional[int]    ,			lowercase__	:						List[Any]    ,			lowercase__	:						Union[str, Any]    ,			lowercase__	:						Dict    ,			lowercase__	:						Any)						->int:

							"""simple docstring"""



							_lowercase							=     20
							_lowercase							=     model_class_name(lowercase__)

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

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

							_lowercase							=     model(
							    input_ids[:, :-1]    ,			attention_mask=lowercase__    ,			past_key_values=lowercase__    ,			position_ids=lowercase__    ,			)
							_lowercase							=     jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]    ,			dtype="""i4""")
							_lowercase							=     model(
							    input_ids[:, -1:]    ,			past_key_values=outputs_cache.past_key_values    ,			attention_mask=lowercase__    ,			position_ids=lowercase__    ,			)

							_lowercase							=     model(lowercase__    ,			attention_mask=lowercase__)

							_lowercase							=     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   (		_snake_case							,_snake_case							,unittest.TestCase  ):
						__SCREAMING_SNAKE_CASE     :					str  =      (FlaxGPTNeoModel, FlaxGPTNeoForCausalLM) if is_flax_available() else ()
						__SCREAMING_SNAKE_CASE     :					int  =      (FlaxGPTNeoForCausalLM,) if is_flax_available() else ()

						def 	_UpperCAmelCase							(		self	:						Dict)						->List[str]:

							"""simple docstring"""



							_lowercase							=     FlaxGPTNeoModelTester(self)

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

							"""simple docstring"""



							for model_class_name in self.all_model_classes:
								_lowercase		,		_lowercase		,		_lowercase							=     self.model_tester.prepare_config_and_inputs()
								self.model_tester.check_use_cache_forward(lowercase__    ,			lowercase__    ,			lowercase__    ,			lowercase__)

						def 	_UpperCAmelCase							(		self	:						Any)						->List[Any]:

							"""simple docstring"""



							for model_class_name in self.all_model_classes:
								_lowercase		,		_lowercase		,		_lowercase							=     self.model_tester.prepare_config_and_inputs()
								self.model_tester.check_use_cache_forward_with_attn_mask(
								    lowercase__    ,			lowercase__    ,			lowercase__    ,			lowercase__)

						@slow
						def 	_UpperCAmelCase							(		self	:						Any)						->Any:

							"""simple docstring"""



							_lowercase							=     GPTaTokenizer.from_pretrained("""gpt2"""    ,			pad_token="""<|endoftext|>"""    ,			padding_side="""left""")
							_lowercase							=     tokenizer(["""Hello this is a long string""", """Hey"""]    ,			return_tensors="""np"""    ,			padding=lowercase__    ,			truncation=lowercase__)

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

							_lowercase							=     jax.jit(model.generate)

							_lowercase							=     jit_generate(
							    inputs["""input_ids"""]    ,			attention_mask=inputs["""attention_mask"""]    ,			pad_token_id=tokenizer.pad_token_id).sequences

							_lowercase							=     tokenizer.batch_decode(lowercase__    ,			skip_special_tokens=lowercase__)

							_lowercase							=     [
							    """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(lowercase__    ,			lowercase__)

						@is_pt_flax_cross_test
						def 	_UpperCAmelCase							(		self	:						Optional[Any])						->List[str]:

							"""simple docstring"""



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

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

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

									_lowercase							=     convert_pytorch_state_dict_to_flax(pt_model.state_dict()    ,			lowercase__)
									_lowercase							=     fx_state

									with torch.no_grad():
										_lowercase							=     pt_model(**lowercase__).to_tuple()

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

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

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

						@is_pt_flax_cross_test
						def 	_UpperCAmelCase							(		self	:						Optional[Any])						->str:

							"""simple docstring"""



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

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

									_lowercase							=     pt_model_class(lowercase__).eval()
									_lowercase							=     model_class(lowercase__    ,			dtype=jnp.floataa)

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

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

									with torch.no_grad():
										_lowercase							=     pt_model(**lowercase__).to_tuple()

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

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

									with torch.no_grad():
										_lowercase							=     pt_model_loaded(**lowercase__).to_tuple()

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


						@slow
						def 	_UpperCAmelCase							(		self	:						int)						->Optional[int]:

							"""simple docstring"""



							for model_class_name in self.all_model_classes:
								_lowercase							=     model_class_name.from_pretrained("""EleutherAI/gpt-neo-125M""")
								_lowercase							=     model(np.ones((1, 1)))
								self.assertIsNotNone(lowercase__)






"""simple docstring"""




def __snake_case (      UpperCamelCase    )       ->    float:


		"""simple docstring"""





		return -num if num < 0 else num

def __snake_case (      UpperCamelCase    )       ->    int:


		"""simple docstring"""





		if len(UpperCamelCase    ) == 0:
				raise ValueError('''abs_min() arg is an empty sequence'''    )
		a__   		=     x[0]
		for i in x:
				if abs_val(UpperCamelCase    ) < abs_val(UpperCamelCase    ):
						a__   		=     i
		return j

def __snake_case (      UpperCamelCase    )       ->    int:


		"""simple docstring"""





		if len(UpperCamelCase    ) == 0:
				raise ValueError('''abs_max() arg is an empty sequence'''    )
		a__   		=     x[0]
		for i in x:
				if abs(UpperCamelCase    ) > abs(UpperCamelCase    ):
						a__   		=     i
		return j

def __snake_case (      UpperCamelCase    )       ->    int:


		"""simple docstring"""





		if len(UpperCamelCase    ) == 0:
				raise ValueError('''abs_max_sort() arg is an empty sequence'''    )
		return sorted(UpperCamelCase						,					key=UpperCamelCase    )[-1]

def __snake_case (      )       ->    Optional[int]:


		"""simple docstring"""





		assert abs_val(0    ) == 0
		assert abs_val(34    ) == 34
		assert abs_val(-100_000_000_000    ) == 100_000_000_000

		a__   		=     [-3, -1, 2, -11]
		assert abs_max(UpperCamelCase    ) == -11
		assert abs_max_sort(UpperCamelCase    ) == -11
		assert abs_min(UpperCamelCase    ) == -1


if __name__ == "__main__":
					import doctest

					doctest.testmod()

					test_abs_val()
					print(abs_val(-34))  # --> 34




