


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 					lowerCAmelCase__	:




     '''simple docstring'''


     def __init__(						self			,						__lowerCamelCase			,						__lowerCamelCase=1_4			,						__lowerCamelCase=7			,						__lowerCamelCase=True			,						__lowerCamelCase=True			,						__lowerCamelCase=False			,						__lowerCamelCase=True			,						__lowerCamelCase=9_9			,						__lowerCamelCase=3_2			,						__lowerCamelCase=4			,						__lowerCamelCase=4			,						__lowerCamelCase=[[["global", "local"], 2]]			,						__lowerCamelCase=3_7			,						__lowerCamelCase="gelu"			,						__lowerCamelCase=0.1			,						__lowerCamelCase=0.1			,						__lowerCamelCase=5_1_2			,						__lowerCamelCase=7			,						__lowerCamelCase=0.0_2			,						)      ->    Union[str, Any]:
            _A				:	str       =			parent
            _A				:	List[Any]       =			batch_size
            _A				:	str       =			seq_length
            _A				:	Optional[Any]       =			is_training
            _A				:	Optional[int]       =			use_input_mask
            _A				:	Union[str, Any]       =			use_token_type_ids
            _A				:	Union[str, Any]       =			use_labels
            _A				:	Any       =			vocab_size
            _A				:	Union[str, Any]       =			hidden_size
            _A				:	int       =			num_hidden_layers
            _A				:	Union[str, Any]       =			num_attention_heads
            _A				:	List[str]       =			attention_types
            _A				:	Optional[Any]       =			intermediate_size
            _A				:	List[Any]       =			hidden_act
            _A				:	int       =			hidden_dropout_prob
            _A				:	Any       =			attention_probs_dropout_prob
            _A				:	str       =			max_position_embeddings
            _A				:	Optional[Any]       =			window_size
            _A				:	Optional[int]       =			initializer_range
            _A				:	Optional[Any]       =			None
            _A				:	Optional[int]       =			vocab_size - 1
            _A				:	Dict       =			vocab_size - 1
            _A				:	int       =			vocab_size - 1


     def 			_lowerCamelCase			(						self)      ->    List[Any]:
            _A				:	List[str]       =			ids_tensor([self.batch_size, self.seq_length]			,						self.vocab_size)

            _A				:	Tuple       =			None
            if self.use_input_mask:
                   _A				:	str       =			random_attention_mask([self.batch_size, self.seq_length])

            _A				:	Optional[int]       =			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=__lowerCamelCase			,						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 			_lowerCamelCase			(						self)      ->    Dict:
            _A				:	Optional[Any]       =			self.prepare_config_and_inputs()
            _A   ,      _A   ,      _A				:	Optional[int]       =			config_and_inputs
            _A				:	Optional[Any]       =			{"input_ids": input_ids, "attention_mask": attention_mask}
            return config, inputs_dict


     def 			_lowerCamelCase			(						self			,						__lowerCamelCase			,						__lowerCamelCase			,						__lowerCamelCase			,						__lowerCamelCase)      ->    Any:
            _A				:	Tuple       =			2_0
            _A				:	Union[str, Any]       =			model_class_name(__lowerCamelCase)

            _A				:	Union[str, Any]       =			model.init_cache(input_ids.shape[0]			,						__lowerCamelCase)
            _A				:	Optional[int]       =			jnp.ones((input_ids.shape[0], max_decoder_length)			,						dtype="i4")

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

            _A				:	Optional[int]       =			jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]			,						dtype="i4")
            _A				:	Union[str, Any]       =			model(
                input_ids[:, -1:]			,						attention_mask=__lowerCamelCase			,						past_key_values=outputs_cache.past_key_values			,						position_ids=__lowerCamelCase			,						)

            _A				:	List[str]       =			model(__lowerCamelCase)

            _A				:	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 			_lowerCamelCase			(						self			,						__lowerCamelCase			,						__lowerCamelCase			,						__lowerCamelCase			,						__lowerCamelCase)      ->    str:
            _A				:	List[Any]       =			2_0
            _A				:	List[str]       =			model_class_name(__lowerCamelCase)

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

            _A				:	List[Any]       =			model.init_cache(input_ids.shape[0]			,						__lowerCamelCase)
            _A				:	List[Any]       =			jnp.broadcast_to(
                jnp.arange(input_ids.shape[-1] - 1)[None, :]			,						(input_ids.shape[0], input_ids.shape[-1] - 1))

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

            _A				:	Tuple       =			model(__lowerCamelCase			,						attention_mask=__lowerCamelCase)

            _A				:	Union[str, 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}")







@require_flax
class 					lowerCAmelCase__	(    a					,   a					,   unittest.TestCase):




     '''simple docstring'''
     __SCREAMING_SNAKE_CASE        =			(FlaxGPTNeoModel, FlaxGPTNeoForCausalLM) if is_flax_available() else ()
     __SCREAMING_SNAKE_CASE        =			(FlaxGPTNeoForCausalLM,) if is_flax_available() else ()


     def 			_lowerCamelCase			(						self)      ->    Tuple:
            _A				:	str       =			FlaxGPTNeoModelTester(self)


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



            for model_class_name in self.all_model_classes:
                   _A   ,      _A   ,      _A				:	Union[str, Any]       =			self.model_tester.prepare_config_and_inputs()
                   self.model_tester.check_use_cache_forward(__lowerCamelCase			,						__lowerCamelCase			,						__lowerCamelCase			,						__lowerCamelCase)


     def 			_lowerCamelCase			(						self)      ->    Any:



            for model_class_name in self.all_model_classes:
                   _A   ,      _A   ,      _A				:	Tuple       =			self.model_tester.prepare_config_and_inputs()
                   self.model_tester.check_use_cache_forward_with_attn_mask(
                       __lowerCamelCase			,						__lowerCamelCase			,						__lowerCamelCase			,						__lowerCamelCase)


     @slow
     def 			_lowerCamelCase			(						self)      ->    Dict:
            _A				:	Optional[Any]       =			GPTaTokenizer.from_pretrained("gpt2"			,						pad_token="<|endoftext|>"			,						padding_side="left")
            _A				:	Dict       =			tokenizer(["Hello this is a long string", "Hey"]			,						return_tensors="np"			,						padding=__lowerCamelCase			,						truncation=__lowerCamelCase)

            _A				:	List[Any]       =			FlaxGPTNeoForCausalLM.from_pretrained("EleutherAI/gpt-neo-125M")
            _A				:	List[Any]       =			False
            _A				:	List[Any]       =			model.config.eos_token_id

            _A				:	str       =			jax.jit(model.generate)

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

            _A				:	Any       =			tokenizer.batch_decode(__lowerCamelCase			,						skip_special_tokens=__lowerCamelCase)

            _A				:	Union[str, Any]       =			[
                "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(__lowerCamelCase			,						__lowerCamelCase)


     @is_pt_flax_cross_test
     def 			_lowerCamelCase			(						self)      ->    List[str]:
            _A   ,      _A				:	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
                          _A				:	List[Any]       =			self._prepare_for_class(__lowerCamelCase			,						__lowerCamelCase)
                          _A				:	Optional[Any]       =			{k: torch.tensor(v.tolist()) for k, v in prepared_inputs_dict.items()}

                          # load corresponding PyTorch class
                          _A				:	str       =			model_class.__name__[4:]  # Skip the "Flax" at the beginning
                          _A				:	List[str]       =			getattr(__lowerCamelCase			,						__lowerCamelCase)

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

                          _A				:	str       =			convert_pytorch_state_dict_to_flax(pt_model.state_dict()			,						__lowerCamelCase)
                          _A				:	Any       =			fx_state

                          with torch.no_grad():
                                 _A				:	Optional[int]       =			pt_model(**__lowerCamelCase).to_tuple()

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

                          with tempfile.TemporaryDirectory() as tmpdirname:
                                 pt_model.save_pretrained(__lowerCamelCase)
                                 _A				:	Tuple       =			model_class.from_pretrained(__lowerCamelCase			,						from_pt=__lowerCamelCase)

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


     @is_pt_flax_cross_test
     def 			_lowerCamelCase			(						self)      ->    Dict:
            _A   ,      _A				:	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
                          _A				:	str       =			self._prepare_for_class(__lowerCamelCase			,						__lowerCamelCase)
                          _A				:	List[Any]       =			{k: torch.tensor(v.tolist()) for k, v in prepared_inputs_dict.items()}

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

                          _A				:	Dict       =			pt_model_class(__lowerCamelCase).eval()
                          _A				:	Optional[Any]       =			model_class(__lowerCamelCase			,						dtype=jnp.floataa)

                          _A				:	Dict       =			load_flax_weights_in_pytorch_model(__lowerCamelCase			,						fx_model.params)
                          _A   ,      _A				:	List[Any]       =			pt_inputs["input_ids"].shape
                          _A				:	List[Any]       =			np.random.randint(0			,						seq_length - 1			,						size=(batch_size,))
                          for batch_idx, start_index in enumerate(__lowerCamelCase):
                                 _A				:	List[str]       =			0
                                 _A				:	Union[str, Any]       =			1
                                 _A				:	str       =			0
                                 _A				:	Dict       =			1

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

                          with torch.no_grad():
                                 _A				:	int       =			pt_model(**__lowerCamelCase).to_tuple()

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

                          with tempfile.TemporaryDirectory() as tmpdirname:
                                 fx_model.save_pretrained(__lowerCamelCase)
                                 _A				:	Optional[int]       =			pt_model_class.from_pretrained(__lowerCamelCase			,						from_flax=__lowerCamelCase)

                          with torch.no_grad():
                                 _A				:	Optional[int]       =			pt_model_loaded(**__lowerCamelCase).to_tuple()

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


     @slow
     def 			_lowerCamelCase			(						self)      ->    List[str]:



            for model_class_name in self.all_model_classes:
                   _A				:	str       =			model_class_name.from_pretrained("EleutherAI/gpt-neo-125M")
                   _A				:	Union[str, Any]       =			model(np.ones((1, 1)))
                   self.assertIsNotNone(__lowerCamelCase)




import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_splinter import SplinterTokenizer


lowerCAmelCase__						=			logging.get_logger(__name__)

lowerCAmelCase__						=			{'vocab_file': 'vocab.txt'}

lowerCAmelCase__						=			{
    'vocab_file': {
        'tau/splinter-base': 'https://huggingface.co/tau/splinter-base/resolve/main/vocab.txt',
        'tau/splinter-base-qass': 'https://huggingface.co/tau/splinter-base-qass/resolve/main/vocab.txt',
        'tau/splinter-large': 'https://huggingface.co/tau/splinter-large/resolve/main/vocab.txt',
        'tau/splinter-large-qass': 'https://huggingface.co/tau/splinter-large-qass/resolve/main/vocab.txt',
    }
}

lowerCAmelCase__						=			{
    'tau/splinter-base': 5_12,
    'tau/splinter-base-qass': 5_12,
    'tau/splinter-large': 5_12,
    'tau/splinter-large-qass': 5_12,
}

lowerCAmelCase__						=			{
    'tau/splinter-base': {'do_lower_case': False},
    'tau/splinter-base-qass': {'do_lower_case': False},
    'tau/splinter-large': {'do_lower_case': False},
    'tau/splinter-large-qass': {'do_lower_case': False},
}







class 					lowerCAmelCase__	(    a):




     '''simple docstring'''
     __SCREAMING_SNAKE_CASE        =			VOCAB_FILES_NAMES
     __SCREAMING_SNAKE_CASE        =			PRETRAINED_VOCAB_FILES_MAP
     __SCREAMING_SNAKE_CASE        =			PRETRAINED_INIT_CONFIGURATION
     __SCREAMING_SNAKE_CASE        =			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     __SCREAMING_SNAKE_CASE        =			SplinterTokenizer


     def __init__(						self			,						__lowerCamelCase=None			,						__lowerCamelCase=None			,						__lowerCamelCase=True			,						__lowerCamelCase="[UNK]"			,						__lowerCamelCase="[SEP]"			,						__lowerCamelCase="[PAD]"			,						__lowerCamelCase="[CLS]"			,						__lowerCamelCase="[MASK]"			,						__lowerCamelCase="[QUESTION]"			,						__lowerCamelCase=True			,						__lowerCamelCase=None			,						**__lowerCamelCase			,						)      ->    Union[str, Any]:
            super().__init__(
                __lowerCamelCase			,						tokenizer_file=__lowerCamelCase			,						do_lower_case=__lowerCamelCase			,						unk_token=__lowerCamelCase			,						sep_token=__lowerCamelCase			,						pad_token=__lowerCamelCase			,						cls_token=__lowerCamelCase			,						mask_token=__lowerCamelCase			,						tokenize_chinese_chars=__lowerCamelCase			,						strip_accents=__lowerCamelCase			,						additional_special_tokens=(question_token,)			,						**__lowerCamelCase			,						)

            _A				:	Tuple       =			json.loads(self.backend_tokenizer.normalizer.__getstate__())
            if (
                pre_tok_state.get("lowercase"			,						__lowerCamelCase) != do_lower_case
                or pre_tok_state.get("strip_accents"			,						__lowerCamelCase) != strip_accents
            ):
                   _A				:	Dict       =			getattr(__lowerCamelCase			,						pre_tok_state.pop("type"))
                   _A				:	List[str]       =			do_lower_case
                   _A				:	Any       =			strip_accents
                   _A				:	int       =			pre_tok_class(**__lowerCamelCase)

            _A				:	int       =			do_lower_case


     @property
     def 			_lowerCamelCase			(						self)      ->    str:
            return self.convert_tokens_to_ids(self.question_token)


     def 			_lowerCamelCase			(						self			,						__lowerCamelCase			,						__lowerCamelCase = None)      ->    List[int]:
            if token_ids_a is None:
                   return [self.cls_token_id] + token_ids_a + [self.sep_token_id]

            _A				:	Tuple       =			[self.cls_token_id]
            _A				:	str       =			[self.sep_token_id]
            _A				:	int       =			[self.question_token_id] + [self.convert_tokens_to_ids(".")]
            if self.padding_side == "right":
                   # Input is question-then-context
                   return cls + token_ids_a + question_suffix + sep + token_ids_a + sep
            else:
                   # Input is context-then-question
                   return cls + token_ids_a + sep + token_ids_a + question_suffix + sep


     def 			_lowerCamelCase			(						self			,						__lowerCamelCase			,						__lowerCamelCase = None)      ->    List[int]:
            _A				:	str       =			[self.sep_token_id]
            _A				:	int       =			[self.cls_token_id]
            _A				:	Union[str, Any]       =			[self.question_token_id] + [self.convert_tokens_to_ids(".")]
            if token_ids_a is None:
                   return len(cls + token_ids_a + sep) * [0]

            if self.padding_side == "right":
                   # Input is question-then-context
                   return len(cls + token_ids_a + question_suffix + sep) * [0] + len(token_ids_a + sep) * [1]
            else:
                   # Input is context-then-question
                   return len(cls + token_ids_a + sep) * [0] + len(token_ids_a + question_suffix + sep) * [1]


     def 			_lowerCamelCase			(						self			,						__lowerCamelCase			,						__lowerCamelCase = None)      ->    Tuple[str]:
            _A				:	Tuple       =			self._tokenizer.model.save(__lowerCamelCase			,						name=__lowerCamelCase)
            return tuple(__lowerCamelCase)
