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 						_SCREAMING_SNAKE_CASE				:






		"""simple docstring"""







		def __init__(      self     :					Optional[Any]	,       __SCREAMING_SNAKE_CASE     :					Tuple	,       __SCREAMING_SNAKE_CASE     :					Tuple=14	,       __SCREAMING_SNAKE_CASE     :					List[Any]=7	,       __SCREAMING_SNAKE_CASE     :					Any=True	,       __SCREAMING_SNAKE_CASE     :					Optional[int]=True	,       __SCREAMING_SNAKE_CASE     :					List[Any]=False	,       __SCREAMING_SNAKE_CASE     :					Any=True	,       __SCREAMING_SNAKE_CASE     :					List[Any]=99	,       __SCREAMING_SNAKE_CASE     :					int=32	,       __SCREAMING_SNAKE_CASE     :					Tuple=4	,       __SCREAMING_SNAKE_CASE     :					List[Any]=4	,       __SCREAMING_SNAKE_CASE     :					List[Any]=[[["global", "local"], 2]]	,       __SCREAMING_SNAKE_CASE     :					Tuple=37	,       __SCREAMING_SNAKE_CASE     :					Optional[int]="gelu"	,       __SCREAMING_SNAKE_CASE     :					str=0.1	,       __SCREAMING_SNAKE_CASE     :					str=0.1	,       __SCREAMING_SNAKE_CASE     :					Optional[int]=512	,       __SCREAMING_SNAKE_CASE     :					Optional[int]=7	,       __SCREAMING_SNAKE_CASE     :					Optional[int]=0.0_2	,       ):






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



		def 					_a					(      self     :					List[str] ):






					"""simple docstring"""
					_snake_case														=   ids_tensor([self.batch_size, self.seq_length]	,       self.vocab_size )

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

					_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=__SCREAMING_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 					_a					(      self     :					Optional[int] ):






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



		def 					_a					(      self     :					Any	,       __SCREAMING_SNAKE_CASE     :					int	,       __SCREAMING_SNAKE_CASE     :					Optional[Any]	,       __SCREAMING_SNAKE_CASE     :					int	,       __SCREAMING_SNAKE_CASE     :					List[Any] ):






					"""simple docstring"""
					_snake_case														=   20
					_snake_case														=   model_class_name(__SCREAMING_SNAKE_CASE )

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

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

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

					_snake_case														=   model(__SCREAMING_SNAKE_CASE )

					_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 					_a					(      self     :					str	,       __SCREAMING_SNAKE_CASE     :					List[str]	,       __SCREAMING_SNAKE_CASE     :					Any	,       __SCREAMING_SNAKE_CASE     :					Any	,       __SCREAMING_SNAKE_CASE     :					List[Any] ):






					"""simple docstring"""
					_snake_case														=   20
					_snake_case														=   model_class_name(__SCREAMING_SNAKE_CASE )

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

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

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

					_snake_case														=   model(__SCREAMING_SNAKE_CASE	,       attention_mask=__SCREAMING_SNAKE_CASE )

					_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 						_SCREAMING_SNAKE_CASE				(  _lowercase   ,  _lowercase   ,  unittest.TestCase				):






		"""simple docstring"""







		A_    			=   (FlaxGPTNeoModel, FlaxGPTNeoForCausalLM) if is_flax_available() else ()
		A_    			=   (FlaxGPTNeoForCausalLM,) if is_flax_available() else ()



		def 					_a					(      self     :					Dict ):






					"""simple docstring"""
					_snake_case														=   FlaxGPTNeoModelTester(self )



		def 					_a					(      self     :					List[Any] ):






					"""simple docstring"""
					for model_class_name in self.all_model_classes:
								_snake_case							,			_snake_case							,			_snake_case														=   self.model_tester.prepare_config_and_inputs()
								self.model_tester.check_use_cache_forward(__SCREAMING_SNAKE_CASE	,       __SCREAMING_SNAKE_CASE	,       __SCREAMING_SNAKE_CASE	,       __SCREAMING_SNAKE_CASE )



		def 					_a					(      self     :					List[str] ):






					"""simple docstring"""
					for model_class_name in self.all_model_classes:
								_snake_case							,			_snake_case							,			_snake_case														=   self.model_tester.prepare_config_and_inputs()
								self.model_tester.check_use_cache_forward_with_attn_mask(
								    __SCREAMING_SNAKE_CASE	,       __SCREAMING_SNAKE_CASE	,       __SCREAMING_SNAKE_CASE	,       __SCREAMING_SNAKE_CASE )



		@slow
		def 					_a					(      self     :					List[str] ):






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

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

					_snake_case														=   jax.jit(model.generate )

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

					_snake_case														=   tokenizer.batch_decode(__SCREAMING_SNAKE_CASE	,       skip_special_tokens=__SCREAMING_SNAKE_CASE )

					_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(__SCREAMING_SNAKE_CASE	,       __SCREAMING_SNAKE_CASE )



		@is_pt_flax_cross_test
		def 					_a					(      self     :					Tuple ):






					"""simple docstring"""
					_snake_case							,			_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
											_snake_case														=   self._prepare_for_class(__SCREAMING_SNAKE_CASE	,       __SCREAMING_SNAKE_CASE )
											_snake_case														=   {k: torch.tensor(v.tolist() ) for k, v in prepared_inputs_dict.items()}

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

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

											_snake_case														=   convert_pytorch_state_dict_to_flax(pt_model.state_dict()	,       __SCREAMING_SNAKE_CASE )
											_snake_case														=   fx_state

											with torch.no_grad():
														_snake_case														=   pt_model(**__SCREAMING_SNAKE_CASE ).to_tuple()

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

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

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



		@is_pt_flax_cross_test
		def 					_a					(      self     :					Optional[Any] ):






					"""simple docstring"""
					_snake_case							,			_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
											_snake_case														=   self._prepare_for_class(__SCREAMING_SNAKE_CASE	,       __SCREAMING_SNAKE_CASE )
											_snake_case														=   {k: torch.tensor(v.tolist() ) for k, v in prepared_inputs_dict.items()}

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

											_snake_case														=   pt_model_class(__SCREAMING_SNAKE_CASE ).eval()
											_snake_case														=   model_class(__SCREAMING_SNAKE_CASE	,       dtype=jnp.floataa )

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

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

											with torch.no_grad():
														_snake_case														=   pt_model(**__SCREAMING_SNAKE_CASE ).to_tuple()

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

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

											with torch.no_grad():
														_snake_case														=   pt_model_loaded(**__SCREAMING_SNAKE_CASE ).to_tuple()

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







		@slow
		def 					_a					(      self     :					Any ):






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


def  UpperCAmelCase__(    lowerCamelCase_ = 1000	):



			"""simple docstring"""





			return sum(i for i in range(lowerCamelCase_	) if i % 3 == 0 or i % 5 == 0	)


if __name__ == "__main__":
			print(F"{solution() = }")

