





"""simple docstring"""


import tempfile
import unittest

import numpy as np

import transformers
from transformers import GPTaTokenizer, GPTJConfig, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, tooslow

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.gptj.modeling_flax_gptj import FlaxGPTJForCausalLM, FlaxGPTJModel

if is_torch_available():
  import torch



class      __SCREAMING_SNAKE_CASE    :







  '''simple docstring'''


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

  def    __SCREAMING_SNAKE_CASE				(		self   :       Tuple	)		->				List[str]:
        _UpperCamelCase      :	Tuple						=       ids_tensor([self.batch_size, self.seq_length]					,   self.vocab_size	)

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

        _UpperCamelCase      :	Dict						=       GPTJConfig(
            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=__a					,   bos_token_id=self.bos_token_id					,   eos_token_id=self.eos_token_id					,   pad_token_id=self.pad_token_id					,   rotary_dim=self.rotary_dim					,   )

        return (config, input_ids, input_mask)

  def    __SCREAMING_SNAKE_CASE				(		self   :       Dict	)		->				Optional[int]:
        _UpperCamelCase      :	List[Any]						=       self.prepare_config_and_inputs()
        _UpperCamelCase,  _UpperCamelCase,  _UpperCamelCase      :	List[Any]						=       config_and_inputs
        _UpperCamelCase      :	str						=       {"input_ids": input_ids, "attention_mask": attention_mask}
        return config, inputs_dict

  def    __SCREAMING_SNAKE_CASE				(		self   :       Any					,   __a   :       Any					,   __a   :       Dict					,   __a   :       List[Any]					,   __a   :       Tuple	)		->				Tuple:
        _UpperCamelCase      :	Dict						=       20
        _UpperCamelCase      :	Any						=       model_class_name(__a	)

        _UpperCamelCase      :	Tuple						=       model.init_cache(input_ids.shape[0]					,   __a	)
        _UpperCamelCase      :	List[str]						=       jnp.ones((input_ids.shape[0], max_decoder_length)					,   dtype="i4"	)

        _UpperCamelCase      :	List[str]						=       jnp.broadcast_to(
            jnp.arange(input_ids.shape[-1] - 1	)[None, :]					,   (input_ids.shape[0], input_ids.shape[-1] - 1)	)
        _UpperCamelCase      :	int						=       model(
            input_ids[:, :-1]					,   attention_mask=__a					,   past_key_values=__a					,   position_ids=__a					,   )

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

        _UpperCamelCase      :	str						=       model(__a	)

        _UpperCamelCase      :	Optional[Any]						=       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[Any]					,   __a   :       Union[str, Any]					,   __a   :       Any					,   __a   :       int					,   __a   :       Optional[Any]	)		->				Optional[int]:
        _UpperCamelCase      :	Optional[Any]						=       20
        _UpperCamelCase      :	Optional[Any]						=       model_class_name(__a	)

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

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

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

        _UpperCamelCase      :	Dict						=       model(__a					,   attention_mask=__a	)

        _UpperCamelCase      :	List[Any]						=       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    (						lowercase__		,  lowercase__		,  unittest.TestCase   ):







  '''simple docstring'''


  SCREAMING_SNAKE_CASE__							:Union[str, Any]	     =					(FlaxGPTJModel, FlaxGPTJForCausalLM) if is_flax_available() else ()
  SCREAMING_SNAKE_CASE__							:Dict	     =					(FlaxGPTJForCausalLM,) if is_flax_available() else ()

  def    __SCREAMING_SNAKE_CASE				(		self   :       Dict	)		->				str:
        _UpperCamelCase      :	List[str]						=       FlaxGPTJModelTester(self	)

  def    __SCREAMING_SNAKE_CASE				(		self   :       int	)		->				Union[str, Any]:
        for model_class_name in self.all_model_classes:
              _UpperCamelCase,  _UpperCamelCase,  _UpperCamelCase      :	str						=       self.model_tester.prepare_config_and_inputs()
              self.model_tester.check_use_cache_forward(__a					,   __a					,   __a					,   __a	)

  def    __SCREAMING_SNAKE_CASE				(		self   :       str	)		->				Dict:
        for model_class_name in self.all_model_classes:
              _UpperCamelCase,  _UpperCamelCase,  _UpperCamelCase      :	Dict						=       self.model_tester.prepare_config_and_inputs()
              self.model_tester.check_use_cache_forward_with_attn_mask(
                  __a					,   __a					,   __a					,   __a	)

  @tooslow
  def    __SCREAMING_SNAKE_CASE				(		self   :       Tuple	)		->				Any:
        _UpperCamelCase      :	Optional[Any]						=       GPTaTokenizer.from_pretrained("gpt2"					,   pad_token="<|endoftext|>"					,   padding_side="left"	)
        _UpperCamelCase      :	List[str]						=       tokenizer(["Hello this is a long string", "Hey"]					,   return_tensors="np"					,   padding=__a					,   truncation=__a	)

        _UpperCamelCase      :	List[str]						=       FlaxGPTJForCausalLM.from_pretrained("EleutherAI/gpt-j-6B"	)
        _UpperCamelCase      :	List[Any]						=       False
        _UpperCamelCase      :	Dict						=       model.config.eos_token_id

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

        _UpperCamelCase      :	Union[str, Any]						=       jit_generate(
            inputs["input_ids"]					,   attention_mask=inputs["attention_mask"]					,   pad_token_id=tokenizer.pad_token_id	).sequences

        _UpperCamelCase      :	List[Any]						=       tokenizer.batch_decode(__a					,   skip_special_tokens=__a	)

        _UpperCamelCase      :	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(__a					,   __a	)

  @is_pt_flax_cross_test
  def    __SCREAMING_SNAKE_CASE				(		self   :       Optional[int]	)		->				List[str]:
        _UpperCamelCase,  _UpperCamelCase      :	Optional[int]						=       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
                    _UpperCamelCase      :	str						=       self._prepare_for_class(__a					,   __a	)
                    _UpperCamelCase      :	Dict						=       {k: torch.tensor(v.tolist()	) for k, v in prepared_inputs_dict.items()}

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

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

                    _UpperCamelCase      :	str						=       convert_pytorch_state_dict_to_flax(pt_model.state_dict()					,   __a	)
                    _UpperCamelCase      :	List[str]						=       fx_state

                    with torch.no_grad():
                          _UpperCamelCase      :	Any						=       pt_model(**__a	).to_tuple()

                    _UpperCamelCase      :	Union[str, Any]						=       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	)
                          _UpperCamelCase      :	Optional[Any]						=       model_class.from_pretrained(__a					,   from_pt=__a	)

                    _UpperCamelCase      :	int						=       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    __SCREAMING_SNAKE_CASE				(		self   :       str	)		->				Union[str, Any]:
        _UpperCamelCase,  _UpperCamelCase      :	Dict						=       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
                    _UpperCamelCase      :	Optional[int]						=       self._prepare_for_class(__a					,   __a	)
                    _UpperCamelCase      :	Union[str, Any]						=       {k: torch.tensor(v.tolist()	) for k, v in prepared_inputs_dict.items()}

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

                    _UpperCamelCase      :	str						=       pt_model_class(__a	).eval()
                    _UpperCamelCase      :	Any						=       model_class(__a					,   dtype=jnp.floataa	)

                    _UpperCamelCase      :	Optional[int]						=       load_flax_weights_in_pytorch_model(__a					,   fx_model.params	)
                    _UpperCamelCase,  _UpperCamelCase      :	List[Any]						=       pt_inputs["input_ids"].shape
                    _UpperCamelCase      :	Any						=       np.random.randint(0					,   seq_length - 1					,   size=(batch_size,)	)
                    for batch_idx, start_index in enumerate(__a	):
                          _UpperCamelCase      :	Any						=       0
                          _UpperCamelCase      :	Dict						=       1
                          _UpperCamelCase      :	List[Any]						=       0
                          _UpperCamelCase      :	Optional[Any]						=       1

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

                    with torch.no_grad():
                          _UpperCamelCase      :	Any						=       pt_model(**__a	).to_tuple()

                    _UpperCamelCase      :	str						=       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	)
                          _UpperCamelCase      :	Optional[int]						=       pt_model_class.from_pretrained(__a					,   from_flax=__a	)

                    with torch.no_grad():
                          _UpperCamelCase      :	int						=       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	)


  @tooslow
  def    __SCREAMING_SNAKE_CASE				(		self   :       Dict	)		->				Dict:
        for model_class_name in self.all_model_classes:
              _UpperCamelCase      :	Optional[Any]						=       model_class_name.from_pretrained("EleutherAI/gpt-j-6B"	)
              _UpperCamelCase      :	str						=       model(np.ones((1, 1)	)	)
              self.assertIsNotNone(__a	)











"""simple docstring"""


import importlib
import os
import sys


# This is required to make the module import works (when the python process is running from the root of the repo)
sys.path.append(".")




def 					lowercase__    (					lowercase_			)					->		int:




      """simple docstring"""

      _UpperCamelCase      :	Any						=       test_file.split(os.path.sep			)
      if components[0:2] != ["tests", "models"]:
            raise ValueError(
                "`test_file` should start with `tests/models/` (with `/` being the OS specific path separator). Got "
                F'''{test_file} instead.'''			)
      _UpperCamelCase      :	str						=       components[-1]
      if not test_fn.endswith("py"			):
            raise ValueError(F'''`test_file` should be a python file. Got {test_fn} instead.'''			)
      if not test_fn.startswith("test_modeling_"			):
            raise ValueError(
                F'''`test_file` should point to a file name of the form `test_modeling_*.py`. Got {test_fn} instead.'''			)

      _UpperCamelCase      :	Dict						=       components[:-1] + [test_fn.replace(".py"		,""			)]
      _UpperCamelCase      :	List[str]						=       ".".join(lowercase_			)

      return test_module_path




def 					lowercase__    (					lowercase_			)					->		List[Any]:




      """simple docstring"""

      _UpperCamelCase      :	Optional[Any]						=       get_module_path(lowercase_			)
      _UpperCamelCase      :	str						=       importlib.import_module(lowercase_			)

      return test_module




def 					lowercase__    (					lowercase_			)					->		Optional[Any]:




      """simple docstring"""

      _UpperCamelCase      :	Union[str, Any]						=       []
      _UpperCamelCase      :	List[Any]						=       get_test_module(lowercase_			)
      for attr in dir(lowercase_			):
            if attr.endswith("ModelTester"			):
                  tester_classes.append(getattr(lowercase_		,lowercase_			)			)

    # sort with class names
      return sorted(lowercase_		,key=lambda lowercase_			: x.__name__			)




def 					lowercase__    (					lowercase_			)					->		Tuple:




      """simple docstring"""

      _UpperCamelCase      :	Optional[Any]						=       []
      _UpperCamelCase      :	Any						=       get_test_module(lowercase_			)
      for attr in dir(lowercase_			):
            _UpperCamelCase      :	int						=       getattr(lowercase_		,lowercase_			)
            # (TF/Flax)ModelTesterMixin is also an attribute in specific model test module. Let's exclude them by checking
            # `all_model_classes` is not empty (which also excludes other special classes).
            _UpperCamelCase      :	Optional[Any]						=       getattr(lowercase_		,"all_model_classes"		,[]			)
            if len(lowercase_			) > 0:
                  test_classes.append(lowercase_			)

    # sort with class names
      return sorted(lowercase_		,key=lambda lowercase_			: x.__name__			)




def 					lowercase__    (					lowercase_			)					->		Any:




      """simple docstring"""

      _UpperCamelCase      :	Dict						=       get_test_classes(lowercase_			)
      _UpperCamelCase      :	int						=       set()
      for test_class in test_classes:
            model_classes.update(test_class.all_model_classes			)

      # sort with class names
      return sorted(lowercase_		,key=lambda lowercase_			: x.__name__			)




def 					lowercase__    (					lowercase_			)					->		Optional[Any]:




      """simple docstring"""

      _UpperCamelCase      :	List[str]						=       test_class()
      if hasattr(lowercase_		,"setUp"			):
            test.setUp()

      _UpperCamelCase      :	Tuple						=       None
      if hasattr(lowercase_		,"model_tester"			):
            # `(TF/Flax)ModelTesterMixin` has this attribute default to `None`. Let's skip this case.
            if test.model_tester is not None:
                  _UpperCamelCase      :	Tuple						=       test.model_tester.__class__

      return model_tester




def 					lowercase__    (					lowercase_		,lowercase_			)					->		Union[str, Any]:




      """simple docstring"""

      _UpperCamelCase      :	str						=       get_test_classes(lowercase_			)

      _UpperCamelCase      :	Dict						=       []
      for test_class in test_classes:
            if model_class in test_class.all_model_classes:
                  target_test_classes.append(lowercase_			)

    # sort with class names
      return sorted(lowercase_		,key=lambda lowercase_			: x.__name__			)




def 					lowercase__    (					lowercase_		,lowercase_			)					->		Dict:




      """simple docstring"""

      _UpperCamelCase      :	Any						=       get_test_classes_for_model(lowercase_		,lowercase_			)

      _UpperCamelCase      :	List[Any]						=       []
      for test_class in test_classes:
            _UpperCamelCase      :	List[Any]						=       get_model_tester_from_test_class(lowercase_			)
            if tester_class is not None:
                  tester_classes.append(lowercase_			)

    # sort with class names
      return sorted(lowercase_		,key=lambda lowercase_			: x.__name__			)




def 					lowercase__    (					lowercase_			)					->		Union[str, Any]:




      """simple docstring"""

      _UpperCamelCase      :	Any						=       get_test_classes(lowercase_			)
      _UpperCamelCase      :	Tuple						=       {test_class: get_model_tester_from_test_class(lowercase_			) for test_class in test_classes}
      return test_tester_mapping




def 					lowercase__    (					lowercase_			)					->		Union[str, Any]:




      """simple docstring"""

      _UpperCamelCase      :	List[Any]						=       get_model_classes(lowercase_			)
      _UpperCamelCase      :	Optional[int]						=       {
          model_class: get_test_classes_for_model(lowercase_		,lowercase_			) for model_class in model_classes
      }
      return model_test_mapping




def 					lowercase__    (					lowercase_			)					->		Union[str, Any]:




      """simple docstring"""

      _UpperCamelCase      :	Optional[Any]						=       get_model_classes(lowercase_			)
      _UpperCamelCase      :	Tuple						=       {
          model_class: get_tester_classes_for_model(lowercase_		,lowercase_			) for model_class in model_classes
      }
      return model_to_tester_mapping




def 					lowercase__    (					lowercase_			)					->		Optional[int]:




      """simple docstring"""

      if isinstance(lowercase_		,lowercase_			):
            return o
      elif isinstance(lowercase_		,lowercase_			):
            return o.__name__
      elif isinstance(lowercase_		,(list, tuple)			):
            return [to_json(lowercase_			) for x in o]
      elif isinstance(lowercase_		,lowercase_			):
            return {to_json(lowercase_			): to_json(lowercase_			) for k, v in o.items()}
      else:
            return o
