
'''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__	:






 def __init__(      self	:Optional[Any]						,					_A	:Tuple						,					_A	:Optional[int]=14						,					_A	:Optional[Any]=7						,					_A	:Dict=True						,					_A	:List[Any]=True						,					_A	:str=False						,					_A	:str=True						,					_A	:List[Any]=99						,					_A	:Union[str, Any]=32						,					_A	:List[Any]=4						,					_A	:int=4						,					_A	:Optional[int]=[[["global", "local"], 2]]						,					_A	:Optional[int]=37						,					_A	:str="gelu"						,					_A	:str=0.1						,					_A	:Union[str, Any]=0.1						,					_A	:Any=512						,					_A	:int=7						,					_A	:Union[str, Any]=0.02						,					)					->      List[Any]:







       '''simple docstring'''



       __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 						lowercase_    (      self	:Dict      )					->      Optional[Any]:







       '''simple docstring'''



       __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=_A						,					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 						lowercase_    (      self	:Tuple      )					->      int:







       '''simple docstring'''



       __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 						lowercase_    (      self	:Any						,					_A	:List[Any]						,					_A	:Optional[int]						,					_A	:List[Any]						,					_A	:Union[str, Any]      )					->      Union[str, Any]:







       '''simple docstring'''



       __A													=      20
       __A													=      model_class_name(_A      )

       __A													=      model.init_cache(input_ids.shape[0]						,					_A      )
       __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=_A						,					past_key_values=_A						,					position_ids=_A						,					)

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

       __A													=      model(_A      )

       __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 						lowercase_    (      self	:Tuple						,					_A	:Union[str, Any]						,					_A	:Optional[Any]						,					_A	:Tuple						,					_A	:int      )					->      str:







       '''simple docstring'''



       __A													=      20
       __A													=      model_class_name(_A      )

       __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]						,					_A      )
       __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=_A						,					past_key_values=_A						,					position_ids=_A						,					)
       __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=_A						,					position_ids=_A						,					)

       __A													=      model(_A						,					attention_mask=_A      )

       __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__	(     SCREAMING_SNAKE_CASE   ,  SCREAMING_SNAKE_CASE   ,  unittest.TestCase):
 UpperCAmelCase__     :     Union[str, Any]    					=       (FlaxGPTNeoModel, FlaxGPTNeoForCausalLM) if is_flax_available() else ()
 UpperCAmelCase__     :     List[str]    					=       (FlaxGPTNeoForCausalLM,) if is_flax_available() else ()






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







       '''simple docstring'''



       __A													=      FlaxGPTNeoModelTester(self      )






 def 						lowercase_    (      self	:Optional[Any]      )					->      List[Any]:







       '''simple docstring'''



       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(_A						,					_A						,					_A						,					_A      )






 def 						lowercase_    (      self	:int      )					->      int:







       '''simple docstring'''



       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(
                 _A						,					_A						,					_A						,					_A      )






 @slow
 def 						lowercase_    (      self	:Tuple      )					->      int:







       '''simple docstring'''



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

       __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(_A						,					skip_special_tokens=_A      )

       __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(_A						,					_A      )






 @is_pt_flax_cross_test
 def 						lowercase_    (      self	:List[str]      )					->      List[str]:







       '''simple docstring'''



       __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(_A						,					_A      )
                   __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(_A						,					_A      )

                   __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(_A      ):
                         __A													=      0
                         __A													=      1
                         __A													=      0
                         __A													=      1
                   __A													=      pt_model_class(_A      ).eval()
                   __A													=      model_class(_A						,					dtype=jnp.floataa      )

                   __A													=      convert_pytorch_state_dict_to_flax(pt_model.state_dict()						,					_A      )
                   __A													=      fx_state

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

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

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

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






 @is_pt_flax_cross_test
 def 						lowercase_    (      self	:Any      )					->      List[str]:







       '''simple docstring'''



       __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(_A						,					_A      )
                   __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(_A						,					_A      )

                   __A													=      pt_model_class(_A      ).eval()
                   __A													=      model_class(_A						,					dtype=jnp.floataa      )

                   __A													=      load_flax_weights_in_pytorch_model(_A						,					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(_A      ):
                         __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(**_A      ).to_tuple()

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

                   with tempfile.TemporaryDirectory() as tmpdirname:
                         fx_model.save_pretrained(_A      )
                         __A													=      pt_model_class.from_pretrained(_A						,					from_flax=_A      )

                   with torch.no_grad():
                         __A													=      pt_model_loaded(**_A      ).to_tuple()

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






 @slow
 def 						lowercase_    (      self	:Dict      )					->      str:







       '''simple docstring'''



       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(_A      )










"""simple docstring"""






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


_lowerCamelCase  =	{
    'configuration_open_llama': ['OPEN_LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'OpenLlamaConfig'],
}

try:
	if not is_sentencepiece_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_lowerCamelCase  =	['LlamaTokenizer']

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_lowerCamelCase  =	['LlamaTokenizerFast']

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_lowerCamelCase  =	[
	    'OpenLlamaForCausalLM',
	    'OpenLlamaModel',
	    'OpenLlamaPreTrainedModel',
	    'OpenLlamaForSequenceClassification',
	]


if TYPE_CHECKING:
	from .configuration_open_llama import OPEN_LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP, OpenLlamaConfig

	try:
		if not is_sentencepiece_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from transformers import LlamaTokenizer

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from transformers import LlamaTokenizerFast

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_open_llama import (
		    OpenLlamaForCausalLM,
		    OpenLlamaForSequenceClassification,
		    OpenLlamaModel,
		    OpenLlamaPreTrainedModel,
		)


else:
	import sys

	_lowerCamelCase  =	_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
