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 				_SCREAMING_SNAKE_CASE:
   def __init__(     self				,  lowerCAmelCase_				,  lowerCAmelCase_=14				,  lowerCAmelCase_=7				,  lowerCAmelCase_=True				,  lowerCAmelCase_=True				,  lowerCAmelCase_=False				,  lowerCAmelCase_=True				,  lowerCAmelCase_=99				,  lowerCAmelCase_=32				,  lowerCAmelCase_=4				,  lowerCAmelCase_=4				,  lowerCAmelCase_=[[["global", "local"], 2]]				,  lowerCAmelCase_=37				,  lowerCAmelCase_="gelu"				,  lowerCAmelCase_=0.1				,  lowerCAmelCase_=0.1				,  lowerCAmelCase_=5_12				,  lowerCAmelCase_=7				,  lowerCAmelCase_=0.02				,  )						->Dict:
        a	:     Dict						      =    parent
        a	:     Tuple						      =    batch_size
        a	:     List[str]						      =    seq_length
        a	:     Dict						      =    is_training
        a	:     str						      =    use_input_mask
        a	:     Any						      =    use_token_type_ids
        a	:     Optional[int]						      =    use_labels
        a	:     Union[str, Any]						      =    vocab_size
        a	:     List[str]						      =    hidden_size
        a	:     Optional[Any]						      =    num_hidden_layers
        a	:     Dict						      =    num_attention_heads
        a	:     Any						      =    attention_types
        a	:     str						      =    intermediate_size
        a	:     Union[str, Any]						      =    hidden_act
        a	:     Tuple						      =    hidden_dropout_prob
        a	:     Optional[int]						      =    attention_probs_dropout_prob
        a	:     str						      =    max_position_embeddings
        a	:     str						      =    window_size
        a	:     Dict						      =    initializer_range
        a	:     Any						      =    None
        a	:     Optional[Any]						      =    vocab_size - 1
        a	:     str						      =    vocab_size - 1
        a	:     List[Any]						      =    vocab_size - 1
   def _lowercase(     self     )						->str:
        a	:     List[Any]						      =    ids_tensor([self.batch_size, self.seq_length]				,  self.vocab_size     )

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

        a	:     Optional[int]						      =    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=_UpperCAmelCase				,  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     )						->Dict:
        a	:     Dict						      =    self.prepare_config_and_inputs()
        a      ,				a      ,				a	:     Any						      =    config_and_inputs
        a	:     List[Any]						      =    {'''input_ids''': input_ids, '''attention_mask''': attention_mask}
        return config, inputs_dict
   def _lowercase(     self				,  lowerCAmelCase_				,  lowerCAmelCase_				,  lowerCAmelCase_				,  lowerCAmelCase_     )						->str:
        a	:     List[Any]						      =    20
        a	:     List[str]						      =    model_class_name(_UpperCAmelCase     )

        a	:     Tuple						      =    model.init_cache(input_ids.shape[0]				,  _UpperCAmelCase     )
        a	:     str						      =    jnp.ones((input_ids.shape[0], max_decoder_length)				,  dtype='''i4'''     )

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

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

        a	:     Any						      =    model(_UpperCAmelCase     )

        a	:     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}"""     )


   def _lowercase(     self				,  lowerCAmelCase_				,  lowerCAmelCase_				,  lowerCAmelCase_				,  lowerCAmelCase_     )						->Optional[int]:
        a	:     str						      =    20
        a	:     List[str]						      =    model_class_name(_UpperCAmelCase     )

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

        a	:     List[Any]						      =    model.init_cache(input_ids.shape[0]				,  _UpperCAmelCase     )
        a	:     Dict						      =    jnp.broadcast_to(
            jnp.arange(input_ids.shape[-1] - 1     )[None, :]				,  (input_ids.shape[0], input_ids.shape[-1] - 1)     )

        a	:     Tuple						      =    model(
            input_ids[:, :-1]				,  attention_mask=_UpperCAmelCase				,  past_key_values=_UpperCAmelCase				,  position_ids=_UpperCAmelCase				,  )
        a	:     List[str]						      =    jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]				,  dtype='''i4'''     )
        a	:     Dict						      =    model(
            input_ids[:, -1:]				,  past_key_values=outputs_cache.past_key_values				,  attention_mask=_UpperCAmelCase				,  position_ids=_UpperCAmelCase				,  )

        a	:     List[str]						      =    model(_UpperCAmelCase				,  attention_mask=_UpperCAmelCase     )

        a	:     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 				_SCREAMING_SNAKE_CASE( lowerCamelCase__							,   lowerCamelCase__							,   unittest.TestCase						):
   UpperCamelCase_					:    List[str]			      = (FlaxGPTNeoModel, FlaxGPTNeoForCausalLM) if is_flax_available() else ()
   UpperCamelCase_					:    Union[str, Any]			      = (FlaxGPTNeoForCausalLM,) if is_flax_available() else ()
   def _lowercase(     self     )						->List[Any]:
        a	:     Optional[int]						      =    FlaxGPTNeoModelTester(self     )
   def _lowercase(     self     )						->int:
        for model_class_name in self.all_model_classes:
             a      ,				a      ,				a	:     Dict						      =    self.model_tester.prepare_config_and_inputs()
             self.model_tester.check_use_cache_forward(_UpperCAmelCase				,  _UpperCAmelCase				,  _UpperCAmelCase				,  _UpperCAmelCase     )
   def _lowercase(     self     )						->Any:
        for model_class_name in self.all_model_classes:
             a      ,				a      ,				a	:     Any						      =    self.model_tester.prepare_config_and_inputs()
             self.model_tester.check_use_cache_forward_with_attn_mask(
                 _UpperCAmelCase				,  _UpperCAmelCase				,  _UpperCAmelCase				,  _UpperCAmelCase     )
   @slow
   def _lowercase(     self     )						->Optional[int]:
        a	:     List[Any]						      =    GPTaTokenizer.from_pretrained('''gpt2'''				,  pad_token='''<|endoftext|>'''				,  padding_side='''left'''     )
        a	:     Dict						      =    tokenizer(['''Hello this is a long string''', '''Hey''']				,  return_tensors='''np'''				,  padding=_UpperCAmelCase				,  truncation=_UpperCAmelCase     )

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

        a	:     int						      =    jax.jit(model.generate     )

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

        a	:     int						      =    tokenizer.batch_decode(_UpperCAmelCase				,  skip_special_tokens=_UpperCAmelCase     )

        a	:     Dict						      =    [
            '''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(_UpperCAmelCase				,  _UpperCAmelCase     )
   @is_pt_flax_cross_test
   def _lowercase(     self     )						->Dict:
        a      ,				a	:     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
                  a	:     int						      =    self._prepare_for_class(_UpperCAmelCase				,  _UpperCAmelCase     )
                  a	:     Union[str, Any]						      =    {k: torch.tensor(v.tolist()     ) for k, v in prepared_inputs_dict.items()}

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

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

                  a	:     int						      =    convert_pytorch_state_dict_to_flax(pt_model.state_dict()				,  _UpperCAmelCase     )
                  a	:     Optional[Any]						      =    fx_state

                  with torch.no_grad():
                       a	:     List[str]						      =    pt_model(**_UpperCAmelCase     ).to_tuple()

                  a	:     str						      =    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     )
                       a	:     int						      =    model_class.from_pretrained(_UpperCAmelCase				,  from_pt=_UpperCAmelCase     )

                  a	:     Optional[int]						      =    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 _lowercase(     self     )						->List[str]:
        a      ,				a	:     Tuple						      =    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	:     List[str]						      =    self._prepare_for_class(_UpperCAmelCase				,  _UpperCAmelCase     )
                  a	:     Optional[int]						      =    {k: torch.tensor(v.tolist()     ) for k, v in prepared_inputs_dict.items()}

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

                  a	:     List[Any]						      =    pt_model_class(_UpperCAmelCase     ).eval()
                  a	:     Dict						      =    model_class(_UpperCAmelCase				,  dtype=jnp.floataa     )

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

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

                  with torch.no_grad():
                       a	:     Union[str, Any]						      =    pt_model(**_UpperCAmelCase     ).to_tuple()

                  a	:     List[Any]						      =    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     )
                       a	:     Any						      =    pt_model_class.from_pretrained(_UpperCAmelCase				,  from_flax=_UpperCAmelCase     )

                  with torch.no_grad():
                       a	:     Union[str, Any]						      =    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 _lowercase(     self     )						->List[str]:
        for model_class_name in self.all_model_classes:
             a	:     List[str]						      =    model_class_name.from_pretrained('''EleutherAI/gpt-neo-125M'''     )
             a	:     Dict						      =    model(np.ones((1, 1)     )     )
             self.assertIsNotNone(_UpperCAmelCase     )



from collections import OrderedDict
from dataclasses import fields
from typing import Any, Tuple

import numpy as np

from .import_utils import is_torch_available






def 			__lowerCAmelCase       (SCREAMING_SNAKE_CASE   )->    Union[str, Any]:




      """simple docstring"""

      if is_torch_available():
            import torch

            if isinstance(SCREAMING_SNAKE_CASE   ,   torch.Tensor   ):
                  return True

      return isinstance(SCREAMING_SNAKE_CASE   ,   np.ndarray   )







class lowerCAmelCase_		(					lowerCamelCase__			):

 '''simple docstring'''

 def 		UpperCamelCase__	(      self   ):
       snake_case_			= fields(self   )

       # Safety and consistency checks
       if not len(_UpperCAmelCase   ):
             raise ValueError(F'''{self.__class__.__name__} has no fields.'''   )

       snake_case_			= getattr(self						, class_fields[0].name   )
       snake_case_			= all(getattr(self						, field.name   ) is None for field in class_fields[1:]   )

       if other_fields_are_none and isinstance(_UpperCAmelCase						, _UpperCAmelCase   ):
             for key, value in first_field.items():
                   snake_case_			= value
       else:
             for field in class_fields:
                   snake_case_			= getattr(self						, field.name   )
                   if v is not None:
                         snake_case_			= v



 def __delitem__(      self						, *_UpperCAmelCase						, **_UpperCAmelCase   ):
       raise Exception(F'''You cannot use ``__delitem__`` on a {self.__class__.__name__} instance.'''   )



 def 		UpperCamelCase__	(      self						, *_UpperCAmelCase						, **_UpperCAmelCase   ):
       raise Exception(F'''You cannot use ``setdefault`` on a {self.__class__.__name__} instance.'''   )



 def 		UpperCamelCase__	(      self						, *_UpperCAmelCase						, **_UpperCAmelCase   ):
       raise Exception(F'''You cannot use ``pop`` on a {self.__class__.__name__} instance.'''   )



 def 		UpperCamelCase__	(      self						, *_UpperCAmelCase						, **_UpperCAmelCase   ):
       raise Exception(F'''You cannot use ``update`` on a {self.__class__.__name__} instance.'''   )



 def __getitem__(      self						, _UpperCAmelCase   ):
       if isinstance(_UpperCAmelCase						, _UpperCAmelCase   ):
             snake_case_			= dict(self.items()   )
             return inner_dict[k]
       else:
             return self.to_tuple()[k]



 def __setattr__(      self						, _UpperCAmelCase						, _UpperCAmelCase   ):
       if name in self.keys() and value is not None:
             # Don't call self.__setitem__ to avoid recursion errors
             super().__setitem__(_UpperCAmelCase						, _UpperCAmelCase   )
       super().__setattr__(_UpperCAmelCase						, _UpperCAmelCase   )



 def __setitem__(      self						, _UpperCAmelCase						, _UpperCAmelCase   ):
       # Will raise a KeyException if needed
       super().__setitem__(_UpperCAmelCase						, _UpperCAmelCase   )
       # Don't call self.__setattr__ to avoid recursion errors
       super().__setattr__(_UpperCAmelCase						, _UpperCAmelCase   )





 def 		UpperCamelCase__	(      self   ):
       return tuple(self[k] for k in self.keys()   )