






"""simple docstring"""





from typing import TYPE_CHECKING

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


A__       :			Optional[int]=						{"""configuration_levit""": ["""LEVIT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """LevitConfig""", """LevitOnnxConfig"""]}

try:
  if not is_vision_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  A__       :			Any=						["""LevitFeatureExtractor"""]
  A__       :			str=						["""LevitImageProcessor"""]

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  A__       :			List[Any]=						[
      """LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """LevitForImageClassification""",
      """LevitForImageClassificationWithTeacher""",
      """LevitModel""",
      """LevitPreTrainedModel""",
  ]


if TYPE_CHECKING:
  from .configuration_levit import LEVIT_PRETRAINED_CONFIG_ARCHIVE_MAP, LevitConfig, LevitOnnxConfig

  try:
    if not is_vision_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .feature_extraction_levit import LevitFeatureExtractor
    from .image_processing_levit import LevitImageProcessor

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_levit import (
        LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
        LevitForImageClassification,
        LevitForImageClassificationWithTeacher,
        LevitModel,
        LevitPreTrainedModel,
    )
else:
  import sys

  A__       :			Union[str, Any]=						_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)












"""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     :

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

   def     SCREAMING_SNAKE_CASE__			(       self   )		->							Dict:
       UpperCamelCase__								=     ids_tensor([self.batch_size, self.seq_length]			,					self.vocab_size   )

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

       UpperCamelCase__								=     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=snake_case_			,					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     SCREAMING_SNAKE_CASE__			(       self   )		->							Tuple:
       UpperCamelCase__								=     self.prepare_config_and_inputs()
       UpperCamelCase__							,    UpperCamelCase__							,    UpperCamelCase__								=     config_and_inputs
       UpperCamelCase__								=     {'input_ids': input_ids, 'attention_mask': attention_mask}
       return config, inputs_dict

   def     SCREAMING_SNAKE_CASE__			(       self			,					snake_case_			,					snake_case_			,					snake_case_			,					snake_case_   )		->							Union[str, Any]:
       UpperCamelCase__								=     20
       UpperCamelCase__								=     model_class_name(snake_case_   )

       UpperCamelCase__								=     model.init_cache(input_ids.shape[0]			,					snake_case_   )
       UpperCamelCase__								=     jnp.ones((input_ids.shape[0], max_decoder_length)			,					dtype='i4'   )

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

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

       UpperCamelCase__								=     model(snake_case_   )

       UpperCamelCase__								=     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			,					snake_case_			,					snake_case_			,					snake_case_			,					snake_case_   )		->							List[str]:
       UpperCamelCase__								=     20
       UpperCamelCase__								=     model_class_name(snake_case_   )

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

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

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

       UpperCamelCase__								=     model(snake_case_			,					attention_mask=snake_case_   )

       UpperCamelCase__								=     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     (	_a				,						_a				,						unittest.TestCase	):
   a   :					int			    =(FlaxGPTNeoModel, FlaxGPTNeoForCausalLM) if is_flax_available() else ()
   a   :					Optional[Any]			    =(FlaxGPTNeoForCausalLM,) if is_flax_available() else ()

   def     SCREAMING_SNAKE_CASE__			(       self   )		->							Dict:
       UpperCamelCase__								=     FlaxGPTNeoModelTester(self   )

   def     SCREAMING_SNAKE_CASE__			(       self   )		->							Tuple:
       for model_class_name in self.all_model_classes:
           UpperCamelCase__							,    UpperCamelCase__							,    UpperCamelCase__								=     self.model_tester.prepare_config_and_inputs()
           self.model_tester.check_use_cache_forward(snake_case_			,					snake_case_			,					snake_case_			,					snake_case_   )

   def     SCREAMING_SNAKE_CASE__			(       self   )		->							Optional[int]:
       for model_class_name in self.all_model_classes:
           UpperCamelCase__							,    UpperCamelCase__							,    UpperCamelCase__								=     self.model_tester.prepare_config_and_inputs()
           self.model_tester.check_use_cache_forward_with_attn_mask(
               snake_case_			,					snake_case_			,					snake_case_			,					snake_case_   )

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

       UpperCamelCase__								=     FlaxGPTNeoForCausalLM.from_pretrained('EleutherAI/gpt-neo-125M'   )
       UpperCamelCase__								=     False
       UpperCamelCase__								=     model.config.eos_token_id

       UpperCamelCase__								=     jax.jit(model.generate   )

       UpperCamelCase__								=     jit_generate(
           inputs['input_ids']			,					attention_mask=inputs['attention_mask']			,					pad_token_id=tokenizer.pad_token_id   ).sequences

       UpperCamelCase__								=     tokenizer.batch_decode(snake_case_			,					skip_special_tokens=snake_case_   )

       UpperCamelCase__								=     [
           '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(snake_case_			,					snake_case_   )

   @is_pt_flax_cross_test
   def     SCREAMING_SNAKE_CASE__			(       self   )		->							Optional[Any]:
       UpperCamelCase__							,    UpperCamelCase__								=     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__								=     self._prepare_for_class(snake_case_			,					snake_case_   )
               UpperCamelCase__								=     {k: torch.tensor(v.tolist()   ) for k, v in prepared_inputs_dict.items()}

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

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

               UpperCamelCase__								=     convert_pytorch_state_dict_to_flax(pt_model.state_dict()			,					snake_case_   )
               UpperCamelCase__								=     fx_state

               with torch.no_grad():
                   UpperCamelCase__								=     pt_model(**snake_case_   ).to_tuple()

               UpperCamelCase__								=     fx_model(**snake_case_   ).to_tuple()
               self.assertEqual(len(snake_case_   )			,					len(snake_case_   )			,					'Output lengths differ between Flax and PyTorch'   )
               for fx_output, pt_output in zip(snake_case_			,					snake_case_   ):
                   self.assert_almost_equals(fx_output[:, -1]			,					pt_output[:, -1].numpy()			,					4E-2   )

               with tempfile.TemporaryDirectory() as tmpdirname:
                   pt_model.save_pretrained(snake_case_   )
                   UpperCamelCase__								=     model_class.from_pretrained(snake_case_			,					from_pt=snake_case_   )

               UpperCamelCase__								=     fx_model_loaded(**snake_case_   ).to_tuple()
               self.assertEqual(
                   len(snake_case_   )			,					len(snake_case_   )			,					'Output lengths differ between Flax and PyTorch'   )
               for fx_output_loaded, pt_output in zip(snake_case_			,					snake_case_   ):
                   self.assert_almost_equals(fx_output_loaded[:, -1]			,					pt_output[:, -1].numpy()			,					4E-2   )

   @is_pt_flax_cross_test
   def     SCREAMING_SNAKE_CASE__			(       self   )		->							List[Any]:
       UpperCamelCase__							,    UpperCamelCase__								=     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__								=     self._prepare_for_class(snake_case_			,					snake_case_   )
               UpperCamelCase__								=     {k: torch.tensor(v.tolist()   ) for k, v in prepared_inputs_dict.items()}

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

               UpperCamelCase__								=     pt_model_class(snake_case_   ).eval()
               UpperCamelCase__								=     model_class(snake_case_			,					dtype=jnp.floataa   )

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

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

               with torch.no_grad():
                   UpperCamelCase__								=     pt_model(**snake_case_   ).to_tuple()

               UpperCamelCase__								=     fx_model(**snake_case_   ).to_tuple()
               self.assertEqual(len(snake_case_   )			,					len(snake_case_   )			,					'Output lengths differ between Flax and PyTorch'   )
               for fx_output, pt_output in zip(snake_case_			,					snake_case_   ):
                   self.assert_almost_equals(fx_output[:, -1]			,					pt_output[:, -1].numpy()			,					4E-2   )

               with tempfile.TemporaryDirectory() as tmpdirname:
                   fx_model.save_pretrained(snake_case_   )
                   UpperCamelCase__								=     pt_model_class.from_pretrained(snake_case_			,					from_flax=snake_case_   )

               with torch.no_grad():
                   UpperCamelCase__								=     pt_model_loaded(**snake_case_   ).to_tuple()

               self.assertEqual(
                   len(snake_case_   )			,					len(snake_case_   )			,					'Output lengths differ between Flax and PyTorch'   )
               for fx_output, pt_output in zip(snake_case_			,					snake_case_   ):
                   self.assert_almost_equals(fx_output[:, -1]			,					pt_output[:, -1].numpy()			,					4E-2   )







   @slow
   def     SCREAMING_SNAKE_CASE__			(       self   )		->							Optional[int]:
       for model_class_name in self.all_model_classes:
           UpperCamelCase__								=     model_class_name.from_pretrained('EleutherAI/gpt-neo-125M'   )
           UpperCamelCase__								=     model(np.ones((1, 1)   )   )
           self.assertIsNotNone(snake_case_   )




