
"""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 				A__ :

		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=4 ,	_SCREAMING_SNAKE_CASE=4 ,	_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=5_12 ,	_SCREAMING_SNAKE_CASE=0.02 ,	):
			__lowerCAmelCase    :		Any		    =   parent
			__lowerCAmelCase    :		Tuple		    =   batch_size
			__lowerCAmelCase    :		Optional[int]		    =   seq_length
			__lowerCAmelCase    :		int		    =   is_training
			__lowerCAmelCase    :		Dict		    =   use_input_mask
			__lowerCAmelCase    :		int		    =   use_token_type_ids
			__lowerCAmelCase    :		List[str]		    =   use_labels
			__lowerCAmelCase    :		Optional[Any]		    =   vocab_size
			__lowerCAmelCase    :		Tuple		    =   hidden_size
			__lowerCAmelCase    :		Tuple		    =   rotary_dim
			__lowerCAmelCase    :		Any		    =   num_hidden_layers
			__lowerCAmelCase    :		List[Any]		    =   num_attention_heads
			__lowerCAmelCase    :		Optional[Any]		    =   intermediate_size
			__lowerCAmelCase    :		Optional[Any]		    =   hidden_act
			__lowerCAmelCase    :		List[str]		    =   hidden_dropout_prob
			__lowerCAmelCase    :		Optional[Any]		    =   attention_probs_dropout_prob
			__lowerCAmelCase    :		Dict		    =   max_position_embeddings
			__lowerCAmelCase    :		Dict		    =   initializer_range
			__lowerCAmelCase    :		int		    =   None
			__lowerCAmelCase    :		Optional[Any]		    =   vocab_size - 1
			__lowerCAmelCase    :		int		    =   vocab_size - 1
			__lowerCAmelCase    :		Tuple		    =   vocab_size - 1

		def 							__lowerCamelCase							(   self				):
			__lowerCAmelCase    :		Optional[Any]		    =   ids_tensor([self.batch_size, self.seq_length] ,	self.vocab_size				)

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

			__lowerCAmelCase    :		Dict		    =   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=_SCREAMING_SNAKE_CASE ,	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 							__lowerCamelCase							(   self				):
			__lowerCAmelCase    :		Union[str, Any]		    =   self.prepare_config_and_inputs()
			__lowerCAmelCase    ,     __lowerCAmelCase    ,     __lowerCAmelCase    :		Optional[int]		    =   config_and_inputs
			__lowerCAmelCase    :		List[str]		    =   {'input_ids': input_ids, 'attention_mask': attention_mask}
			return config, inputs_dict

		def 							__lowerCamelCase							(   self ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				):
			__lowerCAmelCase    :		Tuple		    =   20
			__lowerCAmelCase    :		int		    =   model_class_name(_SCREAMING_SNAKE_CASE				)

			__lowerCAmelCase    :		List[str]		    =   model.init_cache(input_ids.shape[0] ,	_SCREAMING_SNAKE_CASE				)
			__lowerCAmelCase    :		Any		    =   jnp.ones((input_ids.shape[0], max_decoder_length) ,	dtype='i4'				)

			__lowerCAmelCase    :		Optional[int]		    =   jnp.broadcast_to(
			    jnp.arange(input_ids.shape[-1] - 1				)[None, :] ,	(input_ids.shape[0], input_ids.shape[-1] - 1)				)
			__lowerCAmelCase    :		List[Any]		    =   model(
			    input_ids[:, :-1] ,	attention_mask=_SCREAMING_SNAKE_CASE ,	past_key_values=_SCREAMING_SNAKE_CASE ,	position_ids=_SCREAMING_SNAKE_CASE ,	)

			__lowerCAmelCase    :		Dict		    =   jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]] ,	dtype='i4'				)
			__lowerCAmelCase    :		List[Any]		    =   model(
			    input_ids[:, -1:] ,	attention_mask=_SCREAMING_SNAKE_CASE ,	past_key_values=outputs_cache.past_key_values ,	position_ids=_SCREAMING_SNAKE_CASE ,	)

			__lowerCAmelCase    :		List[Any]		    =   model(_SCREAMING_SNAKE_CASE				)

			__lowerCAmelCase    :		int		    =   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 							__lowerCamelCase							(   self ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				):
			__lowerCAmelCase    :		Union[str, Any]		    =   20
			__lowerCAmelCase    :		str		    =   model_class_name(_SCREAMING_SNAKE_CASE				)

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

			__lowerCAmelCase    :		Optional[int]		    =   model.init_cache(input_ids.shape[0] ,	_SCREAMING_SNAKE_CASE				)
			__lowerCAmelCase    :		str		    =   jnp.broadcast_to(
			    jnp.arange(input_ids.shape[-1] - 1				)[None, :] ,	(input_ids.shape[0], input_ids.shape[-1] - 1)				)

			__lowerCAmelCase    :		Tuple		    =   model(
			    input_ids[:, :-1] ,	attention_mask=_SCREAMING_SNAKE_CASE ,	past_key_values=_SCREAMING_SNAKE_CASE ,	position_ids=_SCREAMING_SNAKE_CASE ,	)
			__lowerCAmelCase    :		Dict		    =   jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]] ,	dtype='i4'				)
			__lowerCAmelCase    :		str		    =   model(
			    input_ids[:, -1:] ,	past_key_values=outputs_cache.past_key_values ,	attention_mask=_SCREAMING_SNAKE_CASE ,	position_ids=_SCREAMING_SNAKE_CASE ,	)

			__lowerCAmelCase    :		Optional[Any]		    =   model(_SCREAMING_SNAKE_CASE ,	attention_mask=_SCREAMING_SNAKE_CASE				)

			__lowerCAmelCase    :		Tuple		    =   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__ (			_lowerCamelCase ,  _lowerCamelCase ,  unittest.TestCase):
		A_		: Union[str, Any]            =			(FlaxGPTJModel, FlaxGPTJForCausalLM) if is_flax_available() else ()
		A_		: List[str]            =			(FlaxGPTJForCausalLM,) if is_flax_available() else ()

		def 							__lowerCamelCase							(   self				):
			__lowerCAmelCase    :		str		    =   FlaxGPTJModelTester(self				)

		def 							__lowerCamelCase							(   self				):
			for model_class_name in self.all_model_classes:
				__lowerCAmelCase    ,     __lowerCAmelCase    ,     __lowerCAmelCase    :		Tuple		    =   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 							__lowerCamelCase							(   self				):
			for model_class_name in self.all_model_classes:
				__lowerCAmelCase    ,     __lowerCAmelCase    ,     __lowerCAmelCase    :		Tuple		    =   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				)

		@tooslow
		def 							__lowerCamelCase							(   self				):
			__lowerCAmelCase    :		Dict		    =   GPTaTokenizer.from_pretrained('gpt2' ,	pad_token='<|endoftext|>' ,	padding_side='left'				)
			__lowerCAmelCase    :		Dict		    =   tokenizer(['Hello this is a long string', 'Hey'] ,	return_tensors='np' ,	padding=_SCREAMING_SNAKE_CASE ,	truncation=_SCREAMING_SNAKE_CASE				)

			__lowerCAmelCase    :		Tuple		    =   FlaxGPTJForCausalLM.from_pretrained('EleutherAI/gpt-j-6B'				)
			__lowerCAmelCase    :		str		    =   False
			__lowerCAmelCase    :		List[str]		    =   model.config.eos_token_id

			__lowerCAmelCase    :		List[Any]		    =   jax.jit(model.generate				)

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

			__lowerCAmelCase    :		List[Any]		    =   tokenizer.batch_decode(_SCREAMING_SNAKE_CASE ,	skip_special_tokens=_SCREAMING_SNAKE_CASE				)

			__lowerCAmelCase    :		int		    =   [
			    '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 							__lowerCamelCase							(   self				):
			__lowerCAmelCase    ,     __lowerCAmelCase    :		Optional[int]		    =   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
					__lowerCAmelCase    :		Optional[int]		    =   self._prepare_for_class(_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				)
					__lowerCAmelCase    :		int		    =   {k: torch.tensor(v.tolist()				) for k, v in prepared_inputs_dict.items()}

					# load corresponding PyTorch class
					__lowerCAmelCase    :		str		    =   model_class.__name__[4:]  # Skip the "Flax" at the beginning
					__lowerCAmelCase    :		Union[str, Any]		    =   getattr(_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				)

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

					__lowerCAmelCase    :		Union[str, Any]		    =   convert_pytorch_state_dict_to_flax(pt_model.state_dict() ,	_SCREAMING_SNAKE_CASE				)
					__lowerCAmelCase    :		List[Any]		    =   fx_state

					with torch.no_grad():
						__lowerCAmelCase    :		Tuple		    =   pt_model(**_SCREAMING_SNAKE_CASE				).to_tuple()

					__lowerCAmelCase    :		List[Any]		    =   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				)
						__lowerCAmelCase    :		Optional[int]		    =   model_class.from_pretrained(_SCREAMING_SNAKE_CASE ,	from_pt=_SCREAMING_SNAKE_CASE				)

					__lowerCAmelCase    :		Union[str, Any]		    =   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 							__lowerCamelCase							(   self				):
			__lowerCAmelCase    ,     __lowerCAmelCase    :		Dict		    =   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
					__lowerCAmelCase    :		Dict		    =   self._prepare_for_class(_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				)
					__lowerCAmelCase    :		Any		    =   {k: torch.tensor(v.tolist()				) for k, v in prepared_inputs_dict.items()}

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

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

					__lowerCAmelCase    :		List[str]		    =   load_flax_weights_in_pytorch_model(_SCREAMING_SNAKE_CASE ,	fx_model.params				)
					__lowerCAmelCase    ,     __lowerCAmelCase    :		List[Any]		    =   pt_inputs['input_ids'].shape
					__lowerCAmelCase    :		Optional[Any]		    =   np.random.randint(0 ,	seq_length - 1 ,	size=(batch_size,)				)
					for batch_idx, start_index in enumerate(_SCREAMING_SNAKE_CASE				):
						__lowerCAmelCase    :		List[str]		    =   0
						__lowerCAmelCase    :		Optional[Any]		    =   1
						__lowerCAmelCase    :		Union[str, Any]		    =   0
						__lowerCAmelCase    :		Optional[int]		    =   1

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

					with torch.no_grad():
						__lowerCAmelCase    :		Dict		    =   pt_model(**_SCREAMING_SNAKE_CASE				).to_tuple()

					__lowerCAmelCase    :		int		    =   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				)
						__lowerCAmelCase    :		Optional[int]		    =   pt_model_class.from_pretrained(_SCREAMING_SNAKE_CASE ,	from_flax=_SCREAMING_SNAKE_CASE				)

					with torch.no_grad():
						__lowerCAmelCase    :		Optional[Any]		    =   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				)







		@tooslow
		def 							__lowerCamelCase							(   self				):
			for model_class_name in self.all_model_classes:
				__lowerCAmelCase    :		List[str]		    =   model_class_name.from_pretrained('EleutherAI/gpt-j-6B'				)
				__lowerCAmelCase    :		str		    =   model(np.ones((1, 1)				)				)
				self.assertIsNotNone(_SCREAMING_SNAKE_CASE				)

"""simple docstring"""







def 					__lowerCAmelCase       (_UpperCamelCase ,						_UpperCamelCase ,						_UpperCamelCase	):
	__lowerCAmelCase    :		List[str]		    =   len(_UpperCamelCase	)
	__lowerCAmelCase    :		Tuple		    =   [[0] * n for i in range(_UpperCamelCase	)]
	for i in range(_UpperCamelCase	):
		__lowerCAmelCase    :		Any		    =   y_points[i]

	for i in range(2 ,						_UpperCamelCase	):
		for j in range(_UpperCamelCase ,						_UpperCamelCase	):
			__lowerCAmelCase    :		int		    =   (
			    (xa - x_points[j - i + 1]) * q[j][i - 1]
			    - (xa - x_points[j]) * q[j - 1][i - 1]
			) / (x_points[j] - x_points[j - i + 1])

	return [q[n - 1][n - 1], q]


if __name__ == "__main__":
							import doctest

							doctest.testmod()