



'''simple docstring'''





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  lowerCamelCase_   :
  """simple docstring"""




  def __init__(			self				:     Any      ,     _a				:     Union[str, Any]      ,     _a				:     List[Any]=14      ,     _a				:     Any=7      ,     _a				:     List[Any]=True      ,     _a				:     List[Any]=True      ,     _a				:     List[str]=False      ,     _a				:     str=True      ,     _a				:     Optional[int]=99      ,     _a				:     Optional[Any]=32      ,     _a				:     List[str]=4      ,     _a				:     List[str]=4      ,     _a				:     int=[[["global", "local"], 2]]      ,     _a				:     int=37      ,     _a				:     int="gelu"      ,     _a				:     Any=0.1      ,     _a				:     str=0.1      ,     _a				:     List[Any]=512      ,     _a				:     Dict=7      ,     _a				:     Any=0.02      ,     ) -> Dict:
    __lowerCamelCase	:	int   	=			parent
    __lowerCamelCase	:	Union[str, Any]   	=			batch_size
    __lowerCamelCase	:	List[Any]   	=			seq_length
    __lowerCamelCase	:	Any   	=			is_training
    __lowerCamelCase	:	Union[str, Any]   	=			use_input_mask
    __lowerCamelCase	:	Optional[Any]   	=			use_token_type_ids
    __lowerCamelCase	:	Tuple   	=			use_labels
    __lowerCamelCase	:	Optional[int]   	=			vocab_size
    __lowerCamelCase	:	Dict   	=			hidden_size
    __lowerCamelCase	:	Tuple   	=			num_hidden_layers
    __lowerCamelCase	:	List[str]   	=			num_attention_heads
    __lowerCamelCase	:	Optional[int]   	=			attention_types
    __lowerCamelCase	:	Dict   	=			intermediate_size
    __lowerCamelCase	:	Tuple   	=			hidden_act
    __lowerCamelCase	:	Tuple   	=			hidden_dropout_prob
    __lowerCamelCase	:	Dict   	=			attention_probs_dropout_prob
    __lowerCamelCase	:	List[Any]   	=			max_position_embeddings
    __lowerCamelCase	:	Dict   	=			window_size
    __lowerCamelCase	:	Dict   	=			initializer_range
    __lowerCamelCase	:	List[str]   	=			None
    __lowerCamelCase	:	Optional[int]   	=			vocab_size - 1
    __lowerCamelCase	:	Dict   	=			vocab_size - 1
    __lowerCamelCase	:	int   	=			vocab_size - 1




  def 						_lowercase					(			self				:     List[Any]       ) -> Union[str, Any]:
    __lowerCamelCase	:	str   	=			ids_tensor([self.batch_size, self.seq_length]      ,     self.vocab_size       )

    __lowerCamelCase	:	Any   	=			None
    if self.use_input_mask:
      __lowerCamelCase	:	Optional[Any]   	=			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=_a      ,     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				:     str       ) -> int:
    __lowerCamelCase	:	List[str]   	=			self.prepare_config_and_inputs()
    __lowerCamelCase	,__lowerCamelCase	,__lowerCamelCase	:	str   	=			config_and_inputs
    __lowerCamelCase	:	List[str]   	=			{'input_ids': input_ids, 'attention_mask': attention_mask}
    return config, inputs_dict




  def 						_lowercase					(			self				:     Tuple      ,     _a				:     int      ,     _a				:     Dict      ,     _a				:     str      ,     _a				:     List[Any]       ) -> Optional[Any]:
    __lowerCamelCase	:	Any   	=			20
    __lowerCamelCase	:	Optional[Any]   	=			model_class_name(_a       )

    __lowerCamelCase	:	Tuple   	=			model.init_cache(input_ids.shape[0]      ,     _a       )
    __lowerCamelCase	:	Optional[Any]   	=			jnp.ones((input_ids.shape[0], max_decoder_length)      ,     dtype='i4'       )

    __lowerCamelCase	:	Dict   	=			jnp.broadcast_to(
        jnp.arange(input_ids.shape[-1] - 1       )[None, :]      ,     (input_ids.shape[0], input_ids.shape[-1] - 1)       )
    __lowerCamelCase	:	int   	=			model(
        input_ids[:, :-1]      ,     attention_mask=_a      ,     past_key_values=_a      ,     position_ids=_a      ,     )

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

    __lowerCamelCase	:	Any   	=			model(_a       )

    __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 						_lowercase					(			self				:     Tuple      ,     _a				:     Dict      ,     _a				:     List[Any]      ,     _a				:     Optional[int]      ,     _a				:     Union[str, Any]       ) -> List[Any]:
    __lowerCamelCase	:	Tuple   	=			20
    __lowerCamelCase	:	Union[str, Any]   	=			model_class_name(_a       )

    __lowerCamelCase	:	Optional[int]   	=			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]      ,     _a       )
    __lowerCamelCase	:	List[str]   	=			jnp.broadcast_to(
        jnp.arange(input_ids.shape[-1] - 1       )[None, :]      ,     (input_ids.shape[0], input_ids.shape[-1] - 1)       )

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

    __lowerCamelCase	:	Union[str, Any]   	=			model(_a      ,     attention_mask=_a       )

    __lowerCamelCase	:	Tuple   	=			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  lowerCamelCase_   (    SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       unittest.TestCase					):
  """simple docstring"""




  a_							 =(FlaxGPTNeoModel, FlaxGPTNeoForCausalLM) if is_flax_available() else ()
  a_							 =(FlaxGPTNeoForCausalLM,) if is_flax_available() else ()




  def 						_lowercase					(			self				:     List[str]       ) -> Tuple:
    __lowerCamelCase	:	Any   	=			FlaxGPTNeoModelTester(self       )




  def 						_lowercase					(			self				:     List[Any]       ) -> Dict:
    for model_class_name in self.all_model_classes:
      __lowerCamelCase	,__lowerCamelCase	,__lowerCamelCase	:	str   	=			self.model_tester.prepare_config_and_inputs()
      self.model_tester.check_use_cache_forward(_a      ,     _a      ,     _a      ,     _a       )




  def 						_lowercase					(			self				:     Dict       ) -> Optional[Any]:
    for model_class_name in self.all_model_classes:
      __lowerCamelCase	,__lowerCamelCase	,__lowerCamelCase	:	List[Any]   	=			self.model_tester.prepare_config_and_inputs()
      self.model_tester.check_use_cache_forward_with_attn_mask(
          _a      ,     _a      ,     _a      ,     _a       )




  @slow
  def 						_lowercase					(			self				:     List[str]       ) -> Optional[int]:
    __lowerCamelCase	:	Union[str, Any]   	=			GPTaTokenizer.from_pretrained('gpt2'      ,     pad_token='<|endoftext|>'      ,     padding_side='left'       )
    __lowerCamelCase	:	Optional[Any]   	=			tokenizer(['Hello this is a long string', 'Hey']      ,     return_tensors='np'      ,     padding=_a      ,     truncation=_a       )

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

    __lowerCamelCase	:	str   	=			jax.jit(model.generate       )

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

    __lowerCamelCase	:	Tuple   	=			tokenizer.batch_decode(_a      ,     skip_special_tokens=_a       )

    __lowerCamelCase	:	Union[str, Any]   	=			[
        '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 						_lowercase					(			self				:     Dict       ) -> str:
    __lowerCamelCase	,__lowerCamelCase	:	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
        __lowerCamelCase	:	Optional[Any]   	=			self._prepare_for_class(_a      ,     _a       )
        __lowerCamelCase	:	Optional[Any]   	=			{k: torch.tensor(v.tolist()       ) for k, v in prepared_inputs_dict.items()}

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

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

        __lowerCamelCase	:	List[Any]   	=			convert_pytorch_state_dict_to_flax(pt_model.state_dict()      ,     _a       )
        __lowerCamelCase	:	List[str]   	=			fx_state

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

        __lowerCamelCase	:	List[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:
          pt_model.save_pretrained(_a       )
          __lowerCamelCase	:	Union[str, Any]   	=			model_class.from_pretrained(_a      ,     from_pt=_a       )

        __lowerCamelCase	:	Tuple   	=			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 						_lowercase					(			self				:     Optional[Any]       ) -> List[Any]:
    __lowerCamelCase	,__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	:	Union[str, Any]   	=			self._prepare_for_class(_a      ,     _a       )
        __lowerCamelCase	:	List[str]   	=			{k: torch.tensor(v.tolist()       ) for k, v in prepared_inputs_dict.items()}

        # load corresponding PyTorch class
        __lowerCamelCase	:	Optional[int]   	=			model_class.__name__[4:]  # Skip the "Flax" at the beginning
        __lowerCamelCase	:	Tuple   	=			getattr(_a      ,     _a       )

        __lowerCamelCase	:	Optional[Any]   	=			pt_model_class(_a       ).eval()
        __lowerCamelCase	:	Union[str, Any]   	=			model_class(_a      ,     dtype=jnp.floataa       )

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

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

        with torch.no_grad():
          __lowerCamelCase	:	Optional[Any]   	=			pt_model(**_a       ).to_tuple()

        __lowerCamelCase	:	Dict   	=			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       )
          __lowerCamelCase	:	Any   	=			pt_model_class.from_pretrained(_a      ,     from_flax=_a       )

        with torch.no_grad():
          __lowerCamelCase	:	List[str]   	=			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       )




  @slow
  def 						_lowercase					(			self				:     Tuple       ) -> str:
    for model_class_name in self.all_model_classes:
      __lowerCamelCase	:	Optional[Any]   	=			model_class_name.from_pretrained('EleutherAI/gpt-neo-125M'       )
      __lowerCamelCase	:	List[str]   	=			model(np.ones((1, 1)       )       )
      self.assertIsNotNone(_a       )











'''simple docstring'''





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


_UpperCamelCase							    =						{
    'configuration_imagegpt': ['IMAGEGPT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ImageGPTConfig', 'ImageGPTOnnxConfig']
}

try:
   if not is_vision_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _UpperCamelCase							    =						['ImageGPTFeatureExtractor']
   _UpperCamelCase							    =						['ImageGPTImageProcessor']

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _UpperCamelCase							    =						[
       'IMAGEGPT_PRETRAINED_MODEL_ARCHIVE_LIST',
       'ImageGPTForCausalImageModeling',
       'ImageGPTForImageClassification',
       'ImageGPTModel',
       'ImageGPTPreTrainedModel',
       'load_tf_weights_in_imagegpt',
   ]


if TYPE_CHECKING:
   from .configuration_imagegpt import IMAGEGPT_PRETRAINED_CONFIG_ARCHIVE_MAP, ImageGPTConfig, ImageGPTOnnxConfig

   try:
      if not is_vision_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .feature_extraction_imagegpt import ImageGPTFeatureExtractor
      from .image_processing_imagegpt import ImageGPTImageProcessor

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_imagegpt import (
          IMAGEGPT_PRETRAINED_MODEL_ARCHIVE_LIST,
          ImageGPTForCausalImageModeling,
          ImageGPTForImageClassification,
          ImageGPTModel,
          ImageGPTPreTrainedModel,
          load_tf_weights_in_imagegpt,
      )

else:
   import sys

   _UpperCamelCase							    =						_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)






