
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 		__magic_name__   :





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





  def       _A							(							self    :	Union[str, Any]				):
       lowerCAmelCase		:					Optional[Any]  	=     ids_tensor([self.batch_size, self.seq_length]						,  self.vocab_size				)

       lowerCAmelCase		:					Union[str, Any]  	=     None
       if self.use_input_mask:
            lowerCAmelCase		:					int  	=     random_attention_mask([self.batch_size, self.seq_length]				)

       lowerCAmelCase		:					Dict  	=     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       _A							(							self    :	Optional[Any]				):
       lowerCAmelCase		:					Tuple  	=     self.prepare_config_and_inputs()
       lowerCAmelCase		:					List[str]  	=     config_and_inputs
       lowerCAmelCase		:					Optional[Any]  	=     {'''input_ids''': input_ids, '''attention_mask''': attention_mask}
       return config, inputs_dict





  def       _A							(							self    :	int						,  lowerCamelCase__    :	Tuple						,  lowerCamelCase__    :	List[str]						,  lowerCamelCase__    :	Optional[Any]						,  lowerCamelCase__    :	Dict				):
       lowerCAmelCase		:					List[Any]  	=     2_0
       lowerCAmelCase		:					Tuple  	=     model_class_name(lowerCamelCase__				)

       lowerCAmelCase		:					Optional[Any]  	=     model.init_cache(input_ids.shape[0]						,  lowerCamelCase__				)
       lowerCAmelCase		:					List[Any]  	=     jnp.ones((input_ids.shape[0], max_decoder_length)						,  dtype='''i4'''				)

       lowerCAmelCase		:					Optional[int]  	=     jnp.broadcast_to(
           jnp.arange(input_ids.shape[-1] - 1				)[None, :]						,  (input_ids.shape[0], input_ids.shape[-1] - 1)				)
       lowerCAmelCase		:					Tuple  	=     model(
           input_ids[:, :-1]						,  attention_mask=lowerCamelCase__						,  past_key_values=lowerCamelCase__						,  position_ids=lowerCamelCase__						,  )

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

       lowerCAmelCase		:					int  	=     model(lowerCamelCase__				)

       lowerCAmelCase		:					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       _A							(							self    :	List[Any]						,  lowerCamelCase__    :	Tuple						,  lowerCamelCase__    :	Tuple						,  lowerCamelCase__    :	List[Any]						,  lowerCamelCase__    :	List[Any]				):
       lowerCAmelCase		:					List[str]  	=     2_0
       lowerCAmelCase		:					int  	=     model_class_name(lowerCamelCase__				)

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

       lowerCAmelCase		:					List[str]  	=     model.init_cache(input_ids.shape[0]						,  lowerCamelCase__				)
       lowerCAmelCase		:					List[str]  	=     jnp.broadcast_to(
           jnp.arange(input_ids.shape[-1] - 1				)[None, :]						,  (input_ids.shape[0], input_ids.shape[-1] - 1)				)

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

       lowerCAmelCase		:					Optional[int]  	=     model(lowerCamelCase__						,  attention_mask=lowerCamelCase__				)

       lowerCAmelCase		:					Optional[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}'''				)



@require_flax
class 		__magic_name__   (						snake_case,				snake_case,				unittest.TestCase    ):
  _lowerCAmelCase		=	(FlaxGPTNeoModel, FlaxGPTNeoForCausalLM) if is_flax_available() else ()
  _lowerCAmelCase		=	(FlaxGPTNeoForCausalLM,) if is_flax_available() else ()





  def       _A							(							self    :	Any				):
       lowerCAmelCase		:					Optional[Any]  	=     FlaxGPTNeoModelTester(self				)





  def       _A							(							self    :	str				):
       for model_class_name in self.all_model_classes:
            lowerCAmelCase		:					Dict  	=     self.model_tester.prepare_config_and_inputs()
            self.model_tester.check_use_cache_forward(lowerCamelCase__						,  lowerCamelCase__						,  lowerCamelCase__						,  lowerCamelCase__				)





  def       _A							(							self    :	str				):
       for model_class_name in self.all_model_classes:
            lowerCAmelCase		:					str  	=     self.model_tester.prepare_config_and_inputs()
            self.model_tester.check_use_cache_forward_with_attn_mask(
                lowerCamelCase__						,  lowerCamelCase__						,  lowerCamelCase__						,  lowerCamelCase__				)





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

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

       lowerCAmelCase		:					Optional[Any]  	=     jax.jit(model.generate				)

       lowerCAmelCase		:					Optional[int]  	=     jit_generate(
           inputs['''input_ids''']						,  attention_mask=inputs['''attention_mask''']						,  pad_token_id=tokenizer.pad_token_id				).sequences

       lowerCAmelCase		:					Optional[int]  	=     tokenizer.batch_decode(lowerCamelCase__						,  skip_special_tokens=lowerCamelCase__				)

       lowerCAmelCase		:					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(lowerCamelCase__						,  lowerCamelCase__				)





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

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

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

                 lowerCAmelCase		:					Union[str, Any]  	=     convert_pytorch_state_dict_to_flax(pt_model.state_dict()						,  lowerCamelCase__				)
                 lowerCAmelCase		:					int  	=     fx_state

                 with torch.no_grad():
                      lowerCAmelCase		:					Dict  	=     pt_model(**lowerCamelCase__				).to_tuple()

                 lowerCAmelCase		:					Union[str, 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__				)
                      lowerCAmelCase		:					Any  	=     model_class.from_pretrained(lowerCamelCase__						,  from_pt=lowerCamelCase__				)

                 lowerCAmelCase		:					Dict  	=     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       _A							(							self    :	int				):
       lowerCAmelCase		:					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
                 lowerCAmelCase		:					Union[str, Any]  	=     self._prepare_for_class(lowerCamelCase__						,  lowerCamelCase__				)
                 lowerCAmelCase		:					Dict  	=     {k: torch.tensor(v.tolist()				) for k, v in prepared_inputs_dict.items()}

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

                 lowerCAmelCase		:					Union[str, Any]  	=     pt_model_class(lowerCamelCase__				).eval()
                 lowerCAmelCase		:					Union[str, Any]  	=     model_class(lowerCamelCase__						,  dtype=jnp.floataa				)

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

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

                 with torch.no_grad():
                      lowerCAmelCase		:					Tuple  	=     pt_model(**lowerCamelCase__				).to_tuple()

                 lowerCAmelCase		:					int  	=     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__				)
                      lowerCAmelCase		:					Union[str, Any]  	=     pt_model_class.from_pretrained(lowerCamelCase__						,  from_flax=lowerCamelCase__				)

                 with torch.no_grad():
                      lowerCAmelCase		:					Any  	=     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       _A							(							self    :	int				):
       for model_class_name in self.all_model_classes:
            lowerCAmelCase		:					Dict  	=     model_class_name.from_pretrained('''EleutherAI/gpt-neo-125M'''				)
            lowerCAmelCase		:					Union[str, Any]  	=     model(np.ones((1, 1)				)				)
            self.assertIsNotNone(lowerCamelCase__				)


from ..utils import requires_backends


__SCREAMING_SNAKE_CASE			:   Union[str, Any]         =     None


class 		__magic_name__   :





		def __init__(							self    :	Optional[int]						,  *lowerCamelCase__    :	List[str]						,  **lowerCamelCase__    :	Optional[Any]				):
							requires_backends(self						,  ['''detectron2''']				)





		@classmethod
		def       _A							(							cls    :	Dict						,  *lowerCamelCase__    :	List[str]						,  **lowerCamelCase__    :	Optional[Any]				):
							requires_backends(cls						,  ['''detectron2''']				)
