

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	,   _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=[[["global", "local"], 2]]	,   _SCREAMING_SNAKE_CASE=37	,   _SCREAMING_SNAKE_CASE="gelu"	,   _SCREAMING_SNAKE_CASE=0.1	,   _SCREAMING_SNAKE_CASE=0.1	,   _SCREAMING_SNAKE_CASE=512	,   _SCREAMING_SNAKE_CASE=7	,   _SCREAMING_SNAKE_CASE=0.02	,   ):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_			:					str          =       parent
        SCREAMING_SNAKE_CASE_			:					Union[str, Any]          =       batch_size
        SCREAMING_SNAKE_CASE_			:					Optional[Any]          =       seq_length
        SCREAMING_SNAKE_CASE_			:					Union[str, Any]          =       is_training
        SCREAMING_SNAKE_CASE_			:					Any          =       use_input_mask
        SCREAMING_SNAKE_CASE_			:					Optional[Any]          =       use_token_type_ids
        SCREAMING_SNAKE_CASE_			:					Tuple          =       use_labels
        SCREAMING_SNAKE_CASE_			:					Tuple          =       vocab_size
        SCREAMING_SNAKE_CASE_			:					List[Any]          =       hidden_size
        SCREAMING_SNAKE_CASE_			:					Any          =       num_hidden_layers
        SCREAMING_SNAKE_CASE_			:					Tuple          =       num_attention_heads
        SCREAMING_SNAKE_CASE_			:					Optional[Any]          =       attention_types
        SCREAMING_SNAKE_CASE_			:					Dict          =       intermediate_size
        SCREAMING_SNAKE_CASE_			:					Optional[int]          =       hidden_act
        SCREAMING_SNAKE_CASE_			:					Optional[Any]          =       hidden_dropout_prob
        SCREAMING_SNAKE_CASE_			:					Union[str, Any]          =       attention_probs_dropout_prob
        SCREAMING_SNAKE_CASE_			:					List[Any]          =       max_position_embeddings
        SCREAMING_SNAKE_CASE_			:					Optional[int]          =       window_size
        SCREAMING_SNAKE_CASE_			:					Tuple          =       initializer_range
        SCREAMING_SNAKE_CASE_			:					Tuple          =       None
        SCREAMING_SNAKE_CASE_			:					str          =       vocab_size - 1
        SCREAMING_SNAKE_CASE_			:					Dict          =       vocab_size - 1
        SCREAMING_SNAKE_CASE_			:					int          =       vocab_size - 1

  def 	UpperCAmelCase      (			self   ):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_			:					List[Any]          =       ids_tensor([self.batch_size, self.seq_length]	,   self.vocab_size   )

        SCREAMING_SNAKE_CASE_			:					Optional[Any]          =       None
        if self.use_input_mask:
              SCREAMING_SNAKE_CASE_			:					Tuple          =       random_attention_mask([self.batch_size, self.seq_length]   )

        SCREAMING_SNAKE_CASE_			:					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=_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 	UpperCAmelCase      (			self   ):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_			:					Tuple          =       self.prepare_config_and_inputs()
        SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_			:					int          =       config_and_inputs
        SCREAMING_SNAKE_CASE_			:					str          =       {'input_ids': input_ids, 'attention_mask': attention_mask}
        return config, inputs_dict

  def 	UpperCAmelCase      (			self	,   _SCREAMING_SNAKE_CASE	,   _SCREAMING_SNAKE_CASE	,   _SCREAMING_SNAKE_CASE	,   _SCREAMING_SNAKE_CASE   ):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_			:					Any          =       20
        SCREAMING_SNAKE_CASE_			:					List[Any]          =       model_class_name(_SCREAMING_SNAKE_CASE   )

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

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

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

        SCREAMING_SNAKE_CASE_			:					List[str]          =       model(_SCREAMING_SNAKE_CASE   )

        SCREAMING_SNAKE_CASE_			:					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 	UpperCAmelCase      (			self	,   _SCREAMING_SNAKE_CASE	,   _SCREAMING_SNAKE_CASE	,   _SCREAMING_SNAKE_CASE	,   _SCREAMING_SNAKE_CASE   ):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_			:					List[str]          =       20
        SCREAMING_SNAKE_CASE_			:					str          =       model_class_name(_SCREAMING_SNAKE_CASE   )

        SCREAMING_SNAKE_CASE_			:					List[str]          =       jnp.concatenate(
            [attention_mask, jnp.zeros((attention_mask.shape[0], max_decoder_length - attention_mask.shape[1])   )]	,   axis=-1	,   )

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

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

        SCREAMING_SNAKE_CASE_			:					int          =       model(_SCREAMING_SNAKE_CASE	,   attention_mask=_SCREAMING_SNAKE_CASE   )

        SCREAMING_SNAKE_CASE_			:					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 	_A						(					__magic_name__				,       __magic_name__				,       unittest.TestCase):
  SCREAMING_SNAKE_CASE      : Tuple    =					(FlaxGPTNeoModel, FlaxGPTNeoForCausalLM) if is_flax_available() else ()
  SCREAMING_SNAKE_CASE      : str    =					(FlaxGPTNeoForCausalLM,) if is_flax_available() else ()

  def 	UpperCAmelCase      (			self   ):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_			:					List[str]          =       FlaxGPTNeoModelTester(self   )

  def 	UpperCAmelCase      (			self   ):





        """simple docstring"""







        for model_class_name in self.all_model_classes:
              SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_			:					int          =       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 	UpperCAmelCase      (			self   ):





        """simple docstring"""







        for model_class_name in self.all_model_classes:
              SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_			:					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   )

  @slow
  def 	UpperCAmelCase      (			self   ):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_			:					Optional[Any]          =       GPTaTokenizer.from_pretrained('gpt2'	,   pad_token='<|endoftext|>'	,   padding_side='left'   )
        SCREAMING_SNAKE_CASE_			:					Any          =       tokenizer(['Hello this is a long string', 'Hey']	,   return_tensors='np'	,   padding=_SCREAMING_SNAKE_CASE	,   truncation=_SCREAMING_SNAKE_CASE   )

        SCREAMING_SNAKE_CASE_			:					List[Any]          =       FlaxGPTNeoForCausalLM.from_pretrained('EleutherAI/gpt-neo-125M'   )
        SCREAMING_SNAKE_CASE_			:					Optional[int]          =       False
        SCREAMING_SNAKE_CASE_			:					int          =       model.config.eos_token_id

        SCREAMING_SNAKE_CASE_			:					List[str]          =       jax.jit(model.generate   )

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

        SCREAMING_SNAKE_CASE_			:					List[str]          =       tokenizer.batch_decode(_SCREAMING_SNAKE_CASE	,   skip_special_tokens=_SCREAMING_SNAKE_CASE   )

        SCREAMING_SNAKE_CASE_			:					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 	UpperCAmelCase      (			self   ):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_			:					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
                    SCREAMING_SNAKE_CASE_			:					Tuple          =       self._prepare_for_class(_SCREAMING_SNAKE_CASE	,   _SCREAMING_SNAKE_CASE   )
                    SCREAMING_SNAKE_CASE_			:					int          =       {k: torch.tensor(v.tolist()   ) for k, v in prepared_inputs_dict.items()}

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

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

                    SCREAMING_SNAKE_CASE_			:					str          =       convert_pytorch_state_dict_to_flax(pt_model.state_dict()	,   _SCREAMING_SNAKE_CASE   )
                    SCREAMING_SNAKE_CASE_			:					Optional[Any]          =       fx_state

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

                    SCREAMING_SNAKE_CASE_			:					Union[str, 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   )
                          SCREAMING_SNAKE_CASE_			:					List[str]          =       model_class.from_pretrained(_SCREAMING_SNAKE_CASE	,   from_pt=_SCREAMING_SNAKE_CASE   )

                    SCREAMING_SNAKE_CASE_			:					int          =       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 	UpperCAmelCase      (			self   ):





        """simple docstring"""







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

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

                    SCREAMING_SNAKE_CASE_			:					int          =       pt_model_class(_SCREAMING_SNAKE_CASE   ).eval()
                    SCREAMING_SNAKE_CASE_			:					int          =       model_class(_SCREAMING_SNAKE_CASE	,   dtype=jnp.floataa   )

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

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

                    with torch.no_grad():
                          SCREAMING_SNAKE_CASE_			:					List[str]          =       pt_model(**_SCREAMING_SNAKE_CASE   ).to_tuple()

                    SCREAMING_SNAKE_CASE_			:					Tuple          =       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   )
                          SCREAMING_SNAKE_CASE_			:					List[str]          =       pt_model_class.from_pretrained(_SCREAMING_SNAKE_CASE	,   from_flax=_SCREAMING_SNAKE_CASE   )

                    with torch.no_grad():
                          SCREAMING_SNAKE_CASE_			:					List[str]          =       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 	UpperCAmelCase      (			self   ):





        """simple docstring"""







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









from typing import TYPE_CHECKING

from ...utils import _LazyModule


lowerCAmelCase       :				Tuple         =					{'tokenization_phobert': ['PhobertTokenizer']}


if TYPE_CHECKING:
    from .tokenization_phobert import PhobertTokenizer

else:
    import sys

    lowerCAmelCase       :				Optional[int]         =					_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)






