




'''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      __UpperCAmelCase							:



					'''simple docstring'''





					def __init__(  self    :	Tuple			,					_lowercase    :	Dict			,					_lowercase    :	Any=14			,					_lowercase    :	int=7			,					_lowercase    :	Tuple=True			,					_lowercase    :	Tuple=True			,					_lowercase    :	List[str]=False			,					_lowercase    :	int=True			,					_lowercase    :	int=99			,					_lowercase    :	Any=32			,					_lowercase    :	Optional[int]=4			,					_lowercase    :	Union[str, Any]=4			,					_lowercase    :	Optional[Any]=[[["global", "local"], 2]]			,					_lowercase    :	Tuple=37			,					_lowercase    :	Optional[Any]="gelu"			,					_lowercase    :	List[Any]=0.1			,					_lowercase    :	Union[str, Any]=0.1			,					_lowercase    :	Any=512			,					_lowercase    :	Tuple=7			,					_lowercase    :	Union[str, Any]=0.02			,					)						->       Tuple:
								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_       		=   attention_types
								A_       		=   intermediate_size
								A_       		=   hidden_act
								A_       		=   hidden_dropout_prob
								A_       		=   attention_probs_dropout_prob
								A_       		=   max_position_embeddings
								A_       		=   window_size
								A_       		=   initializer_range
								A_       		=   None
								A_       		=   vocab_size - 1
								A_       		=   vocab_size - 1
								A_       		=   vocab_size - 1



					def 						__snake_case	(  self    :	Optional[int])						->       Any:
								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_       		=   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 						__snake_case	(  self    :	str)						->       Any:
								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 						__snake_case	(  self    :	Dict			,					_lowercase    :	Union[str, Any]			,					_lowercase    :	Any			,					_lowercase    :	Dict			,					_lowercase    :	List[Any])						->       Any:
								A_       		=   20
								A_       		=   model_class_name(_lowercase)

								A_       		=   model.init_cache(input_ids.shape[0]			,					_lowercase)
								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=_lowercase			,					past_key_values=_lowercase			,					position_ids=_lowercase			,					)

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

								A_       		=   model(_lowercase)

								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 						__snake_case	(  self    :	str			,					_lowercase    :	Tuple			,					_lowercase    :	List[Any]			,					_lowercase    :	Tuple			,					_lowercase    :	int)						->       Dict:
								A_       		=   20
								A_       		=   model_class_name(_lowercase)

								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]			,					_lowercase)
								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=_lowercase			,					past_key_values=_lowercase			,					position_ids=_lowercase			,					)
								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=_lowercase			,					position_ids=_lowercase			,					)

								A_       		=   model(_lowercase			,					attention_mask=_lowercase)

								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      __UpperCAmelCase							(    lowerCAmelCase ,lowerCAmelCase ,unittest.TestCase							):



					'''simple docstring'''





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



					def 						__snake_case	(  self    :	Dict)						->       Tuple:
								A_       		=   FlaxGPTNeoModelTester(self)



					def 						__snake_case	(  self    :	Optional[int])						->       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(_lowercase			,					_lowercase			,					_lowercase			,					_lowercase)



					def 						__snake_case	(  self    :	Optional[int])						->       Optional[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(
											    _lowercase			,					_lowercase			,					_lowercase			,					_lowercase)



					@slow
					def 						__snake_case	(  self    :	int)						->       Optional[int]:
								A_       		=   GPTaTokenizer.from_pretrained('gpt2'			,					pad_token='<|endoftext|>'			,					padding_side='left')
								A_       		=   tokenizer(['Hello this is a long string', 'Hey']			,					return_tensors='np'			,					padding=_lowercase			,					truncation=_lowercase)

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

								A_       		=   jax.jit(model.generate)

								A_       		=   jit_generate(
								    inputs['input_ids']			,					attention_mask=inputs['attention_mask']			,					pad_token_id=tokenizer.pad_token_id).sequences

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

								A_       		=   [
								    '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 						__snake_case	(  self    :	Any)						->       str:
								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(_lowercase			,					_lowercase)
														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(_lowercase			,					_lowercase)

														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(_lowercase):
																	A_       		=   0
																	A_       		=   1
																	A_       		=   0
																	A_       		=   1
														A_       		=   pt_model_class(_lowercase).eval()
														A_       		=   model_class(_lowercase			,					dtype=jnp.floataa)

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

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

														A_       		=   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)
																	A_       		=   model_class.from_pretrained(_lowercase			,					from_pt=_lowercase)

														A_       		=   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 						__snake_case	(  self    :	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(_lowercase			,					_lowercase)
														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(_lowercase			,					_lowercase)

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

														A_       		=   load_flax_weights_in_pytorch_model(_lowercase			,					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(_lowercase):
																	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(**_lowercase).to_tuple()

														A_       		=   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)
																	A_       		=   pt_model_class.from_pretrained(_lowercase			,					from_flax=_lowercase)

														with torch.no_grad():
																	A_       		=   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 						__snake_case	(  self    :	int)						->       str:
								for model_class_name in self.all_model_classes:
											A_       		=   model_class_name.from_pretrained('EleutherAI/gpt-neo-125M')
											A_       		=   model(np.ones((1, 1)))
											self.assertIsNotNone(_lowercase)











'''simple docstring'''
from typing import Dict, List, Optional, Tuple

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


_SCREAMING_SNAKE_CASE =	logging.get_logger(__name__)
class      __UpperCAmelCase							(    lowerCAmelCase							):



					'''simple docstring'''





					_UpperCamelCase  	=		["""input_ids""", """attention_mask"""]



					def __init__(  self    :	str			,					_lowercase    :	List[Any]="[PAD]"			,					_lowercase    :	Any="[BOS]"			,					_lowercase    :	Any="[EOS]"			,					_lowercase    :	str="[MASK]"			,					_lowercase    :	str="[CLS]"			,					_lowercase    :	Optional[Any]="[SEP]"			,					_lowercase    :	List[str]=2_048			,					**_lowercase    :	int			,					)						->       None:
								A_       		=   AddedToken(_lowercase			,					lstrip=_lowercase			,					rstrip=_lowercase) if isinstance(_lowercase			,					_lowercase) else pad_token
								A_       		=   AddedToken(_lowercase			,					lstrip=_lowercase			,					rstrip=_lowercase) if isinstance(_lowercase			,					_lowercase) else bos_token
								A_       		=   AddedToken(_lowercase			,					lstrip=_lowercase			,					rstrip=_lowercase) if isinstance(_lowercase			,					_lowercase) else eos_token
								A_       		=   AddedToken(_lowercase			,					lstrip=_lowercase			,					rstrip=_lowercase) if isinstance(_lowercase			,					_lowercase) else mask_token
								A_       		=   AddedToken(_lowercase			,					lstrip=_lowercase			,					rstrip=_lowercase) if isinstance(_lowercase			,					_lowercase) else cls_token
								A_       		=   AddedToken(_lowercase			,					lstrip=_lowercase			,					rstrip=_lowercase) if isinstance(_lowercase			,					_lowercase) else sep_token

								super().__init__(
								    pad_token=_lowercase			,					bos_token=_lowercase			,					eos_token=_lowercase			,					mask_token=_lowercase			,					cls_token=_lowercase			,					sep_token=_lowercase			,					model_max_length=_lowercase			,					**_lowercase			,					)

								A_       		=   2**8  # utf is 8 bits

								# define special tokens dict
								A_       		=   {
								    self.pad_token: 0,
								    self.bos_token: 1,
								    self.eos_token: 2,
								    self.mask_token: 3,
								    self.cls_token: 4,
								    self.sep_token: 5,
								}
								A_       		=   len(self.special_tokens_encoder)
								A_       		=   {v: k for k, v in self.special_tokens_encoder.items()}



					def 						__snake_case	(  self    :	List[str])						->       Dict[str, int]:
								A_       		=   self.special_tokens_encoder.copy()
								vocab.update(self.added_tokens_encoder)
								for i in range(self._utf_vocab_size):
											A_       		=   chr(_lowercase)
											A_       		=   i + len(self.special_tokens_encoder)
								return vocab



					@property
					def 						__snake_case	(  self    :	int)						->       Dict:
								return self._utf_vocab_size + self._num_special_tokens



					def 						__snake_case	(  self    :	Union[str, Any]			,					_lowercase    :	List[int]			,					_lowercase    :	Optional[List[int]] = None			,					_lowercase    :	bool = False)						->       List[int]:
								if already_has_special_tokens:
											return super().get_special_tokens_mask(
											    token_ids_a=_lowercase			,					token_ids_a=_lowercase			,					already_has_special_tokens=_lowercase)

								# normal case: some special tokens
								if token_ids_a is None:
											return [1] + [0] * len(_lowercase) + [1]
								return [1] + ([0] * len(_lowercase)) + [1] + ([0] * len(_lowercase)) + [1]



					def 						__snake_case	(  self    :	int			,					_lowercase    :	List[int]			,					_lowercase    :	Optional[List[int]] = None)						->       List[int]:
								if token_ids_a is None:
											return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
								else:
											return [self.cls_token_id] + token_ids_a + [self.sep_token_id] + token_ids_a + [self.sep_token_id]



					def 						__snake_case	(  self    :	str			,					_lowercase    :	str)						->       List[str]:
								A_       		=   [chr(_lowercase) for i in text.encode('utf-8')]
								return tokens



					def 						__snake_case	(  self    :	Union[str, Any]			,					_lowercase    :	Optional[Any])						->       Tuple:
								if token in self.special_tokens_encoder:
											A_       		=   self.special_tokens_encoder[token]
								elif token in self.added_tokens_encoder:
											A_       		=   self.added_tokens_encoder[token]
								elif len(_lowercase) != 1:
											A_       		=   self.unk_token_id
								else:
											A_       		=   ord(_lowercase) + self._num_special_tokens
								return token_id



					def 						__snake_case	(  self    :	str			,					_lowercase    :	List[str])						->       Dict:
								if index in self.special_tokens_decoder:
											A_       		=   self.special_tokens_decoder[index]
								elif index in self.added_tokens_decoder:
											A_       		=   self.added_tokens_decoder[index]
								else:
											A_       		=   chr(index - self._num_special_tokens)
								return token



					def 						__snake_case	(  self    :	str			,					_lowercase    :	Dict)						->       int:
								A_       		=   B''
								for token in tokens:
											if token in self.special_tokens_decoder:
														A_       		=   self.special_tokens_decoder[token].encode('utf-8')
											elif token in self.added_tokens_decoder:
														A_       		=   self.special_tokens_decoder[token].encode('utf-8')
											elif token in self.special_tokens_encoder:
														A_       		=   token.encode('utf-8')
											elif token in self.added_tokens_encoder:
														A_       		=   token.encode('utf-8')
											else:
														A_       		=   bytes([ord(_lowercase)])
											bstring += tok_string
								A_       		=   bstring.decode('utf-8'			,					errors='replace')
								return string



					def 						__snake_case	(  self    :	Optional[int]			,					_lowercase    :	str			,					_lowercase    :	Optional[str] = None)						->       Tuple[str]:
								return ()




