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

 def    __snake_case   (     self    :						Tuple					)	->							Tuple:
        _UpperCamelCase       :    Optional[int]					  =					ids_tensor([self.batch_size, self.seq_length]    ,     self.vocab_size					)

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

        _UpperCamelCase       :    str					  =					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    __snake_case   (     self    :						Optional[int]					)	->							str:
        _UpperCamelCase       :    Optional[int]					  =					self.prepare_config_and_inputs()
        _UpperCamelCase  , _UpperCamelCase  , _UpperCamelCase       :    str					  =					config_and_inputs
        _UpperCamelCase       :    Tuple					  =					{'input_ids': input_ids, 'attention_mask': attention_mask}
        return config, inputs_dict

 def    __snake_case   (     self    :						Any    ,     lowerCamelCase    :						Tuple    ,     lowerCamelCase    :						Dict    ,     lowerCamelCase    :						Optional[Any]    ,     lowerCamelCase    :						int					)	->							str:
        _UpperCamelCase       :    int					  =					2_0
        _UpperCamelCase       :    int					  =					model_class_name(__lowerCAmelCase					)

        _UpperCamelCase       :    int					  =					model.init_cache(input_ids.shape[0]    ,     __lowerCAmelCase					)
        _UpperCamelCase       :    Optional[int]					  =					jnp.ones((input_ids.shape[0], max_decoder_length)    ,     dtype='i4'					)

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

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

        _UpperCamelCase       :    str					  =					model(__lowerCAmelCase					)

        _UpperCamelCase       :    Dict					  =					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    :						Optional[Any]    ,     lowerCamelCase    :						List[str]    ,     lowerCamelCase    :						int    ,     lowerCamelCase    :						Union[str, Any]    ,     lowerCamelCase    :						Any					)	->							Any:
        _UpperCamelCase       :    Dict					  =					2_0
        _UpperCamelCase       :    Any					  =					model_class_name(__lowerCAmelCase					)

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

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

        _UpperCamelCase       :    Any					  =					model(
            input_ids[:, :-1]    ,     attention_mask=__lowerCAmelCase    ,     past_key_values=__lowerCAmelCase    ,     position_ids=__lowerCAmelCase    ,     )
        _UpperCamelCase       :    Optional[int]					  =					jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]    ,     dtype='i4'					)
        _UpperCamelCase       :    Tuple					  =					model(
            input_ids[:, -1:]    ,     past_key_values=outputs_cache.past_key_values    ,     attention_mask=__lowerCAmelCase    ,     position_ids=__lowerCAmelCase    ,     )

        _UpperCamelCase       :    Tuple					  =					model(__lowerCAmelCase    ,     attention_mask=__lowerCAmelCase					)

        _UpperCamelCase       :    Dict					  =					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					(    lowerCAmelCase__,		lowerCAmelCase__,		unittest.TestCase  ):



 '''simple docstring'''
 _lowerCamelCase  :		str			      =	(FlaxGPTNeoModel, FlaxGPTNeoForCausalLM) if is_flax_available() else ()
 _lowerCamelCase  :		Optional[Any]			      =	(FlaxGPTNeoForCausalLM,) if is_flax_available() else ()

 def    __snake_case   (     self    :						List[str]					)	->							Union[str, Any]:
        _UpperCamelCase       :    Optional[Any]					  =					FlaxGPTNeoModelTester(self					)

 def    __snake_case   (     self    :						int					)	->							List[str]:


        for model_class_name in self.all_model_classes:
               _UpperCamelCase  , _UpperCamelCase  , _UpperCamelCase       :    Optional[int]					  =					self.model_tester.prepare_config_and_inputs()
               self.model_tester.check_use_cache_forward(__lowerCAmelCase    ,     __lowerCAmelCase    ,     __lowerCAmelCase    ,     __lowerCAmelCase					)

 def    __snake_case   (     self    :						Dict					)	->							Dict:


        for model_class_name in self.all_model_classes:
               _UpperCamelCase  , _UpperCamelCase  , _UpperCamelCase       :    Optional[Any]					  =					self.model_tester.prepare_config_and_inputs()
               self.model_tester.check_use_cache_forward_with_attn_mask(
                   __lowerCAmelCase    ,     __lowerCAmelCase    ,     __lowerCAmelCase    ,     __lowerCAmelCase					)

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

        _UpperCamelCase       :    Tuple					  =					FlaxGPTNeoForCausalLM.from_pretrained('EleutherAI/gpt-neo-125M'					)
        _UpperCamelCase       :    Tuple					  =					False
        _UpperCamelCase       :    Union[str, Any]					  =					model.config.eos_token_id

        _UpperCamelCase       :    Union[str, Any]					  =					jax.jit(model.generate					)

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

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

        _UpperCamelCase       :    Tuple					  =					[
            '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    __snake_case   (     self    :						List[str]					)	->							Tuple:
        _UpperCamelCase  , _UpperCamelCase       :    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
                      _UpperCamelCase       :    str					  =					self._prepare_for_class(__lowerCAmelCase    ,     __lowerCAmelCase					)
                      _UpperCamelCase       :    Tuple					  =					{k: torch.tensor(v.tolist()					) for k, v in prepared_inputs_dict.items()}

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

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

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

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

                      _UpperCamelCase       :    Optional[Any]					  =					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					)
                             _UpperCamelCase       :    Tuple					  =					model_class.from_pretrained(__lowerCAmelCase    ,     from_pt=__lowerCAmelCase					)

                      _UpperCamelCase       :    Union[str, Any]					  =					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    __snake_case   (     self    :						Any					)	->							Tuple:
        _UpperCamelCase  , _UpperCamelCase       :    Union[str, 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
                      _UpperCamelCase       :    Union[str, Any]					  =					self._prepare_for_class(__lowerCAmelCase    ,     __lowerCAmelCase					)
                      _UpperCamelCase       :    int					  =					{k: torch.tensor(v.tolist()					) for k, v in prepared_inputs_dict.items()}

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

                      _UpperCamelCase       :    Any					  =					pt_model_class(__lowerCAmelCase					).eval()
                      _UpperCamelCase       :    List[str]					  =					model_class(__lowerCAmelCase    ,     dtype=jnp.floataa					)

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

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

                      with torch.no_grad():
                             _UpperCamelCase       :    Optional[int]					  =					pt_model(**__lowerCAmelCase					).to_tuple()

                      _UpperCamelCase       :    List[str]					  =					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					)
                             _UpperCamelCase       :    Union[str, Any]					  =					pt_model_class.from_pretrained(__lowerCAmelCase    ,     from_flax=__lowerCAmelCase					)

                      with torch.no_grad():
                             _UpperCamelCase       :    Tuple					  =					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    __snake_case   (     self    :						Optional[int]					)	->							List[str]:


        for model_class_name in self.all_model_classes:
               _UpperCamelCase       :    Optional[Any]					  =					model_class_name.from_pretrained('EleutherAI/gpt-neo-125M'					)
               _UpperCamelCase       :    Dict					  =					model(np.ones((1, 1)					)					)
               self.assertIsNotNone(__lowerCAmelCase					)




def SCREAMING_SNAKE_CASE			(       lowercase_:		list[int]			):

       # validate:
       if not isinstance(lowercase_						,  lowercase_			) or not all(
           isinstance(lowercase_						,  lowercase_			) and item >= 0 for item in citations			):
              raise ValueError('The citations should be a list of non negative integers.'			)

       citations.sort()
       _UpperCamelCase       :    List[str]					  =					len(lowercase_			)

       for i in range(lowercase_			):
              if citations[len_citations - 1 - i] <= i:
                     return i

       return len_citations


if __name__ == "__main__":
 import doctest

 doctest.testmod()



