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 __snake_case							:




   def __init__(       self     :							List[Any]			,      _lowercase     :							str			,      _lowercase     :							Union[str, Any]=14			,      _lowercase     :							Optional[Any]=7			,      _lowercase     :							Optional[int]=True			,      _lowercase     :							Any=True			,      _lowercase     :							str=False			,      _lowercase     :							Union[str, Any]=True			,      _lowercase     :							str=99			,      _lowercase     :							Optional[int]=32			,      _lowercase     :							Any=5			,      _lowercase     :							Tuple=4			,      _lowercase     :							Any=37			,      _lowercase     :							int="gelu"			,      _lowercase     :							Dict=0.1			,      _lowercase     :							Optional[Any]=0.1			,      _lowercase     :							Optional[int]=5_12			,      _lowercase     :							Optional[Any]=0.02			,      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__     				= parent
     SCREAMING_SNAKE_CASE__     				= batch_size
     SCREAMING_SNAKE_CASE__     				= seq_length
     SCREAMING_SNAKE_CASE__     				= is_training
     SCREAMING_SNAKE_CASE__     				= use_input_mask
     SCREAMING_SNAKE_CASE__     				= use_token_type_ids
     SCREAMING_SNAKE_CASE__     				= use_labels
     SCREAMING_SNAKE_CASE__     				= vocab_size
     SCREAMING_SNAKE_CASE__     				= hidden_size
     SCREAMING_SNAKE_CASE__     				= num_hidden_layers
     SCREAMING_SNAKE_CASE__     				= num_attention_heads
     SCREAMING_SNAKE_CASE__     				= intermediate_size
     SCREAMING_SNAKE_CASE__     				= hidden_act
     SCREAMING_SNAKE_CASE__     				= hidden_dropout_prob
     SCREAMING_SNAKE_CASE__     				= attention_probs_dropout_prob
     SCREAMING_SNAKE_CASE__     				= max_position_embeddings
     SCREAMING_SNAKE_CASE__     				= initializer_range
     SCREAMING_SNAKE_CASE__     				= None
     SCREAMING_SNAKE_CASE__     				= vocab_size - 1
     SCREAMING_SNAKE_CASE__     				= vocab_size - 1
     SCREAMING_SNAKE_CASE__     				= vocab_size - 1




   def       __a		(       self     :							List[str] ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__     				= ids_tensor([self.batch_size, self.seq_length]			,      self.vocab_size )

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

     SCREAMING_SNAKE_CASE__     				= 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=_lowerCamelCase			,      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       __a		(       self     :							Optional[int] ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__     				= self.prepare_config_and_inputs()
     SCREAMING_SNAKE_CASE__				,	SCREAMING_SNAKE_CASE__				,	SCREAMING_SNAKE_CASE__     				= config_and_inputs
     SCREAMING_SNAKE_CASE__     				= {"""input_ids""": input_ids, """attention_mask""": attention_mask}
     return config, inputs_dict




   def       __a		(       self     :							List[Any] ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__				,	SCREAMING_SNAKE_CASE__				,	SCREAMING_SNAKE_CASE__     				= self.prepare_config_and_inputs()

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

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




   def       __a		(       self     :							Any			,      _lowercase     :							Optional[int]			,      _lowercase     :							Dict			,      _lowercase     :							Optional[Any]			,      _lowercase     :							Tuple ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__     				= 20
     SCREAMING_SNAKE_CASE__     				= model_class_name(_lowerCamelCase )

     SCREAMING_SNAKE_CASE__     				= model.init_cache(input_ids.shape[0]			,      _lowerCamelCase )
     SCREAMING_SNAKE_CASE__     				= jnp.ones((input_ids.shape[0], max_decoder_length)			,      dtype="""i4""" )

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

     SCREAMING_SNAKE_CASE__     				= jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]			,      dtype="""i4""" )
     SCREAMING_SNAKE_CASE__     				= model(
         input_ids[:, -1:]			,      attention_mask=_lowerCamelCase			,      past_key_values=outputs_cache.past_key_values			,      position_ids=_lowerCamelCase			,      )

     SCREAMING_SNAKE_CASE__     				= model(_lowerCamelCase )

     SCREAMING_SNAKE_CASE__     				= 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     :							Union[str, Any]			,      _lowercase     :							int			,      _lowercase     :							Union[str, Any]			,      _lowercase     :							List[str]			,      _lowercase     :							List[str] ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__     				= 20
     SCREAMING_SNAKE_CASE__     				= model_class_name(_lowerCamelCase )

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

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

     SCREAMING_SNAKE_CASE__     				= model(
         input_ids[:, :-1]			,      attention_mask=_lowerCamelCase			,      past_key_values=_lowerCamelCase			,      position_ids=_lowerCamelCase			,      )
     SCREAMING_SNAKE_CASE__     				= jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]			,      dtype="""i4""" )
     SCREAMING_SNAKE_CASE__     				= model(
         input_ids[:, -1:]			,      past_key_values=outputs_cache.past_key_values			,      attention_mask=_lowerCamelCase			,      position_ids=_lowerCamelCase			,      )

     SCREAMING_SNAKE_CASE__     				= model(_lowerCamelCase			,      attention_mask=_lowerCamelCase )

     SCREAMING_SNAKE_CASE__     				= 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 __snake_case							(		_lowerCAmelCase  ,						_lowerCAmelCase  ,						unittest.TestCase						):
   lowerCAmelCase_								=				(FlaxGPTaModel, FlaxGPTaLMHeadModel) if is_flax_available() else ()
   lowerCAmelCase_								=				(FlaxGPTaLMHeadModel,) if is_flax_available() else ()




   def       __a		(       self     :							Any ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__     				= FlaxGPTaModelTester(self )




   def       __a		(       self     :							str ):



     """simple docstring"""






     for model_class_name in self.all_model_classes:
       SCREAMING_SNAKE_CASE__				,	SCREAMING_SNAKE_CASE__				,	SCREAMING_SNAKE_CASE__     				= self.model_tester.prepare_config_and_inputs()
       self.model_tester.check_use_cache_forward(_lowerCamelCase			,      _lowerCamelCase			,      _lowerCamelCase			,      _lowerCamelCase )




   def       __a		(       self     :							str ):



     """simple docstring"""






     for model_class_name in self.all_model_classes:
       SCREAMING_SNAKE_CASE__				,	SCREAMING_SNAKE_CASE__				,	SCREAMING_SNAKE_CASE__     				= 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     :							Dict ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__     				= GPTaTokenizer.from_pretrained("""gpt2"""			,      pad_token="""</s>"""			,      padding_side="""left""" )
     SCREAMING_SNAKE_CASE__     				= tokenizer(["""Hello this is a long string""", """Hey"""]			,      return_tensors="""np"""			,      padding=_lowerCamelCase			,      truncation=_lowerCamelCase )

     SCREAMING_SNAKE_CASE__     				= FlaxGPTaLMHeadModel.from_pretrained("""gpt2""" )
     SCREAMING_SNAKE_CASE__     				= False
     SCREAMING_SNAKE_CASE__     				= model.config.eos_token_id

     SCREAMING_SNAKE_CASE__     				= jax.jit(model.generate )

     SCREAMING_SNAKE_CASE__     				= jit_generate(inputs["""input_ids"""]			,      attention_mask=inputs["""attention_mask"""] ).sequences

     SCREAMING_SNAKE_CASE__     				= tokenizer.batch_decode(_lowerCamelCase			,      skip_special_tokens=_lowerCamelCase )

     SCREAMING_SNAKE_CASE__     				= [
         """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(_lowerCamelCase			,      _lowerCamelCase )




   @is_pt_flax_cross_test
   def       __a		(       self     :							Any ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__				,	SCREAMING_SNAKE_CASE__     				= 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__     				= self._prepare_for_class(_lowerCamelCase			,      _lowerCamelCase )
         SCREAMING_SNAKE_CASE__     				= {k: torch.tensor(v.tolist() ) for k, v in prepared_inputs_dict.items()}

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

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

         SCREAMING_SNAKE_CASE__     				= convert_pytorch_state_dict_to_flax(pt_model.state_dict()			,      _lowerCamelCase )
         SCREAMING_SNAKE_CASE__     				= fx_state

         with torch.no_grad():
           SCREAMING_SNAKE_CASE__     				= pt_model(**_lowerCamelCase ).to_tuple()

         SCREAMING_SNAKE_CASE__     				= 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 )
           SCREAMING_SNAKE_CASE__     				= model_class.from_pretrained(_lowerCamelCase			,      from_pt=_lowerCamelCase )

         SCREAMING_SNAKE_CASE__     				= 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     :							Optional[int] ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__				,	SCREAMING_SNAKE_CASE__     				= 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__     				= self._prepare_for_class(_lowerCamelCase			,      _lowerCamelCase )
         SCREAMING_SNAKE_CASE__     				= {k: torch.tensor(v.tolist() ) for k, v in prepared_inputs_dict.items()}

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

         SCREAMING_SNAKE_CASE__     				= pt_model_class(_lowerCamelCase ).eval()
         SCREAMING_SNAKE_CASE__     				= model_class(_lowerCamelCase			,      dtype=jnp.floataa )

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

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

         with torch.no_grad():
           SCREAMING_SNAKE_CASE__     				= pt_model(**_lowerCamelCase ).to_tuple()

         SCREAMING_SNAKE_CASE__     				= 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 )
           SCREAMING_SNAKE_CASE__     				= pt_model_class.from_pretrained(_lowerCamelCase			,      from_flax=_lowerCamelCase )

         with torch.no_grad():
           SCREAMING_SNAKE_CASE__     				= 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     :							str ):



     """simple docstring"""






     for model_class_name in self.all_model_classes:
       SCREAMING_SNAKE_CASE__     				= model_class_name.from_pretrained("""gpt2"""			,      from_pt=_lowerCamelCase )
       SCREAMING_SNAKE_CASE__     				= model(np.ones((1, 1) ) )
       self.assertIsNotNone(_lowerCamelCase )








"""simple docstring"""


import warnings

from ...utils import logging
from .image_processing_vilt import ViltImageProcessor


lowercase_								=	logging.get_logger(__name__)


class    snake_case						(				_lowerCAmelCase		):


   '''simple docstring'''

   def __init__(      self					:	Optional[int],			*_lowerCamelCase					:	int,			**_lowerCamelCase					:	str    ):

     '''simple docstring'''
     warnings.warn(
         '''The class ViltFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'''
         ''' use ViltImageProcessor instead.''',			_lowerCamelCase,			)
     super().__init__(*_lowerCamelCase,			**_lowerCamelCase    )




