
import tempfile
import unittest

import numpy as np

import transformers
from transformers import GPTaConfig, GPTaTokenizer, 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, floats_tensor, 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.gpta.modeling_flax_gpta import FlaxGPTaLMHeadModel, FlaxGPTaModel

if is_torch_available():
       import torch





class 						__lowercase:


       """simple docstring"""







       def __init__(     self					:			str  ,      _lowerCAmelCase					:			Union[str, Any]  ,      _lowerCAmelCase					:			Tuple=14  ,      _lowerCAmelCase					:			Tuple=7  ,      _lowerCAmelCase					:			Dict=True  ,      _lowerCAmelCase					:			List[str]=True  ,      _lowerCAmelCase					:			Dict=False  ,      _lowerCAmelCase					:			Tuple=True  ,      _lowerCAmelCase					:			Optional[Any]=99  ,      _lowerCAmelCase					:			str=32  ,      _lowerCAmelCase					:			Dict=5  ,      _lowerCAmelCase					:			Optional[Any]=4  ,      _lowerCAmelCase					:			str=37  ,      _lowerCAmelCase					:			Any="gelu"  ,      _lowerCAmelCase					:			List[str]=0.1  ,      _lowerCAmelCase					:			Dict=0.1  ,      _lowerCAmelCase					:			int=512  ,      _lowerCAmelCase					:			Dict=0.02  ,      )					->   List[Any]:
         _lowerCAmelCase						=		parent
         _lowerCAmelCase						=		batch_size
         _lowerCAmelCase						=		seq_length
         _lowerCAmelCase						=		is_training
         _lowerCAmelCase						=		use_input_mask
         _lowerCAmelCase						=		use_token_type_ids
         _lowerCAmelCase						=		use_labels
         _lowerCAmelCase						=		vocab_size
         _lowerCAmelCase						=		hidden_size
         _lowerCAmelCase						=		num_hidden_layers
         _lowerCAmelCase						=		num_attention_heads
         _lowerCAmelCase						=		intermediate_size
         _lowerCAmelCase						=		hidden_act
         _lowerCAmelCase						=		hidden_dropout_prob
         _lowerCAmelCase						=		attention_probs_dropout_prob
         _lowerCAmelCase						=		max_position_embeddings
         _lowerCAmelCase						=		initializer_range
         _lowerCAmelCase						=		None
         _lowerCAmelCase						=		vocab_size - 1
         _lowerCAmelCase						=		vocab_size - 1
         _lowerCAmelCase						=		vocab_size - 1



       def SCREAMING_SNAKE_CASE_				(     self					:			Dict       )					->   Dict:
         _lowerCAmelCase						=		ids_tensor([self.batch_size, self.seq_length]  ,      self.vocab_size       )

         _lowerCAmelCase						=		None
         if self.use_input_mask:
           _lowerCAmelCase						=		random_attention_mask([self.batch_size, self.seq_length]       )

         _lowerCAmelCase						=		GPTaConfig(
             vocab_size=self.vocab_size  ,      n_embd=self.hidden_size  ,      n_layer=self.num_hidden_layers  ,      n_head=self.num_attention_heads  ,      n_positions=self.max_position_embeddings  ,      use_cache=UpperCAmelCase__  ,      bos_token_id=self.bos_token_id  ,      eos_token_id=self.eos_token_id  ,      pad_token_id=self.pad_token_id  ,      )

         return (config, input_ids, input_mask)



       def SCREAMING_SNAKE_CASE_				(     self					:			List[Any]       )					->   List[str]:
         _lowerCAmelCase						=		self.prepare_config_and_inputs()
         _lowerCAmelCase	,       _lowerCAmelCase	,       _lowerCAmelCase						=		config_and_inputs
         _lowerCAmelCase						=		{'input_ids': input_ids, 'attention_mask': attention_mask}
         return config, inputs_dict



       def SCREAMING_SNAKE_CASE_				(     self					:			List[Any]       )					->   int:
         _lowerCAmelCase	,       _lowerCAmelCase	,       _lowerCAmelCase						=		self.prepare_config_and_inputs()

         _lowerCAmelCase						=		floats_tensor([self.batch_size, self.seq_length, self.hidden_size]       )
         _lowerCAmelCase						=		ids_tensor([self.batch_size, self.seq_length]  ,      vocab_size=2       )

         return (
             config,
             input_ids,
             attention_mask,
             encoder_hidden_states,
             encoder_attention_mask,
         )



       def SCREAMING_SNAKE_CASE_				(     self					:			str  ,      _lowerCAmelCase					:			Optional[Any]  ,      _lowerCAmelCase					:			Tuple  ,      _lowerCAmelCase					:			Tuple  ,      _lowerCAmelCase					:			Dict       )					->   int:
         _lowerCAmelCase						=		20
         _lowerCAmelCase						=		model_class_name(UpperCAmelCase__       )

         _lowerCAmelCase						=		model.init_cache(input_ids.shape[0]  ,      UpperCAmelCase__       )
         _lowerCAmelCase						=		jnp.ones((input_ids.shape[0], max_decoder_length)  ,      dtype='i4'       )

         _lowerCAmelCase						=		jnp.broadcast_to(
             jnp.arange(input_ids.shape[-1] - 1       )[None, :]  ,      (input_ids.shape[0], input_ids.shape[-1] - 1)       )
         _lowerCAmelCase						=		model(
             input_ids[:, :-1]  ,      attention_mask=UpperCAmelCase__  ,      past_key_values=UpperCAmelCase__  ,      position_ids=UpperCAmelCase__  ,      )

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

         _lowerCAmelCase						=		model(UpperCAmelCase__       )

         _lowerCAmelCase						=		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 SCREAMING_SNAKE_CASE_				(     self					:			List[str]  ,      _lowerCAmelCase					:			Union[str, Any]  ,      _lowerCAmelCase					:			int  ,      _lowerCAmelCase					:			int  ,      _lowerCAmelCase					:			Optional[Any]       )					->   Tuple:
         _lowerCAmelCase						=		20
         _lowerCAmelCase						=		model_class_name(UpperCAmelCase__       )

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

         _lowerCAmelCase						=		model.init_cache(input_ids.shape[0]  ,      UpperCAmelCase__       )
         _lowerCAmelCase						=		jnp.broadcast_to(
             jnp.arange(input_ids.shape[-1] - 1       )[None, :]  ,      (input_ids.shape[0], input_ids.shape[-1] - 1)       )

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

         _lowerCAmelCase						=		model(UpperCAmelCase__  ,      attention_mask=UpperCAmelCase__       )

         _lowerCAmelCase						=		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 						__lowercase(     SCREAMING_SNAKE_CASE		,  SCREAMING_SNAKE_CASE		,  unittest.TestCase   ):


       """simple docstring"""







       UpperCamelCase_				 =							(FlaxGPTaModel, FlaxGPTaLMHeadModel) if is_flax_available() else ()
       UpperCamelCase_				 =							(FlaxGPTaLMHeadModel,) if is_flax_available() else ()



       def SCREAMING_SNAKE_CASE_				(     self					:			Dict       )					->   List[Any]:
         _lowerCAmelCase						=		FlaxGPTaModelTester(self       )



       def SCREAMING_SNAKE_CASE_				(     self					:			Optional[int]       )					->   Any:
         for model_class_name in self.all_model_classes:
           _lowerCAmelCase	,       _lowerCAmelCase	,       _lowerCAmelCase						=		self.model_tester.prepare_config_and_inputs()
           self.model_tester.check_use_cache_forward(UpperCAmelCase__  ,      UpperCAmelCase__  ,      UpperCAmelCase__  ,      UpperCAmelCase__       )



       def SCREAMING_SNAKE_CASE_				(     self					:			int       )					->   Dict:
         for model_class_name in self.all_model_classes:
           _lowerCAmelCase	,       _lowerCAmelCase	,       _lowerCAmelCase						=		self.model_tester.prepare_config_and_inputs()
           self.model_tester.check_use_cache_forward_with_attn_mask(
               UpperCAmelCase__  ,      UpperCAmelCase__  ,      UpperCAmelCase__  ,      UpperCAmelCase__       )



       @slow
       def SCREAMING_SNAKE_CASE_				(     self					:			str       )					->   str:
         _lowerCAmelCase						=		GPTaTokenizer.from_pretrained('gpt2'  ,      pad_token='</s>'  ,      padding_side='left'       )
         _lowerCAmelCase						=		tokenizer(['Hello this is a long string', 'Hey']  ,      return_tensors='np'  ,      padding=UpperCAmelCase__  ,      truncation=UpperCAmelCase__       )

         _lowerCAmelCase						=		FlaxGPTaLMHeadModel.from_pretrained('gpt2'       )
         _lowerCAmelCase						=		False
         _lowerCAmelCase						=		model.config.eos_token_id

         _lowerCAmelCase						=		jax.jit(model.generate       )

         _lowerCAmelCase						=		jit_generate(inputs['input_ids']  ,      attention_mask=inputs['attention_mask']       ).sequences

         _lowerCAmelCase						=		tokenizer.batch_decode(UpperCAmelCase__  ,      skip_special_tokens=UpperCAmelCase__       )

         _lowerCAmelCase						=		[
             'Hello this is a long string of words. I\'m going to start with the first one.\n',
             'Hey, I\'m not sure if I\'m going to be able to do',
         ]

         self.assertListEqual(UpperCAmelCase__  ,      UpperCAmelCase__       )



       @is_pt_flax_cross_test
       def SCREAMING_SNAKE_CASE_				(     self					:			Union[str, Any]       )					->   List[Any]:
         _lowerCAmelCase	,       _lowerCAmelCase						=		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
             _lowerCAmelCase						=		self._prepare_for_class(UpperCAmelCase__  ,      UpperCAmelCase__       )
             _lowerCAmelCase						=		{k: torch.tensor(v.tolist()       ) for k, v in prepared_inputs_dict.items()}

             # load corresponding PyTorch class
             _lowerCAmelCase						=		model_class.__name__[4:]  # Skip the "Flax" at the beginning
             _lowerCAmelCase						=		getattr(UpperCAmelCase__  ,      UpperCAmelCase__       )

             _lowerCAmelCase	,       _lowerCAmelCase						=		pt_inputs['input_ids'].shape
             _lowerCAmelCase						=		np.random.randint(0  ,      seq_length - 1  ,      size=(batch_size,)       )
             for batch_idx, start_index in enumerate(UpperCAmelCase__       ):
               _lowerCAmelCase						=		0
               _lowerCAmelCase						=		1
               _lowerCAmelCase						=		0
               _lowerCAmelCase						=		1
             _lowerCAmelCase						=		pt_model_class(UpperCAmelCase__       ).eval()
             _lowerCAmelCase						=		model_class(UpperCAmelCase__  ,      dtype=jnp.floataa       )

             _lowerCAmelCase						=		convert_pytorch_state_dict_to_flax(pt_model.state_dict()  ,      UpperCAmelCase__       )
             _lowerCAmelCase						=		fx_state

             with torch.no_grad():
               _lowerCAmelCase						=		pt_model(**UpperCAmelCase__       ).to_tuple()

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

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

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



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

             # load corresponding PyTorch class
             _lowerCAmelCase						=		model_class.__name__[4:]  # Skip the "Flax" at the beginning
             _lowerCAmelCase						=		getattr(UpperCAmelCase__  ,      UpperCAmelCase__       )

             _lowerCAmelCase						=		pt_model_class(UpperCAmelCase__       ).eval()
             _lowerCAmelCase						=		model_class(UpperCAmelCase__  ,      dtype=jnp.floataa       )

             _lowerCAmelCase						=		load_flax_weights_in_pytorch_model(UpperCAmelCase__  ,      fx_model.params       )
             _lowerCAmelCase	,       _lowerCAmelCase						=		pt_inputs['input_ids'].shape
             _lowerCAmelCase						=		np.random.randint(0  ,      seq_length - 1  ,      size=(batch_size,)       )
             for batch_idx, start_index in enumerate(UpperCAmelCase__       ):
               _lowerCAmelCase						=		0
               _lowerCAmelCase						=		1
               _lowerCAmelCase						=		0
               _lowerCAmelCase						=		1

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

             with torch.no_grad():
               _lowerCAmelCase						=		pt_model(**UpperCAmelCase__       ).to_tuple()

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

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

             with torch.no_grad():
               _lowerCAmelCase						=		pt_model_loaded(**UpperCAmelCase__       ).to_tuple()

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






       @slow
       def SCREAMING_SNAKE_CASE_				(     self					:			Union[str, Any]       )					->   Dict:
         for model_class_name in self.all_model_classes:
           _lowerCAmelCase						=		model_class_name.from_pretrained('gpt2'  ,      from_pt=UpperCAmelCase__       )
           _lowerCAmelCase						=		model(np.ones((1, 1)       )       )
           self.assertIsNotNone(UpperCAmelCase__       )




"""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__)
