


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 						A__      :







    """simple docstring"""

    def __init__( self    ,      __snake_case    ,      __snake_case=1_4    ,      __snake_case=7    ,      __snake_case=True    ,      __snake_case=True    ,      __snake_case=False    ,      __snake_case=True    ,      __snake_case=9_9    ,      __snake_case=3_2    ,      __snake_case=5    ,      __snake_case=4    ,      __snake_case=3_7    ,      __snake_case="gelu"    ,      __snake_case=0.1    ,      __snake_case=0.1    ,      __snake_case=5_1_2    ,      __snake_case=0.02    ,      ):
         snake_case					=       parent
         snake_case					=       batch_size
         snake_case					=       seq_length
         snake_case					=       is_training
         snake_case					=       use_input_mask
         snake_case					=       use_token_type_ids
         snake_case					=       use_labels
         snake_case					=       vocab_size
         snake_case					=       hidden_size
         snake_case					=       num_hidden_layers
         snake_case					=       num_attention_heads
         snake_case					=       intermediate_size
         snake_case					=       hidden_act
         snake_case					=       hidden_dropout_prob
         snake_case					=       attention_probs_dropout_prob
         snake_case					=       max_position_embeddings
         snake_case					=       initializer_range
         snake_case					=       None
         snake_case					=       vocab_size - 1
         snake_case					=       vocab_size - 1
         snake_case					=       vocab_size - 1
    def 	a_    ( self					):
         snake_case					=       ids_tensor([self.batch_size, self.seq_length]    ,      self.vocab_size					)

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

         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=__snake_case    ,      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					):
         snake_case					=       self.prepare_config_and_inputs()
         snake_case				,						snake_case				,						snake_case					=       config_and_inputs
         snake_case					=       {'''input_ids''': input_ids, '''attention_mask''': attention_mask}
         return config, inputs_dict
    def 	a_    ( self					):
         snake_case				,						snake_case				,						snake_case					=       self.prepare_config_and_inputs()

         snake_case					=       floats_tensor([self.batch_size, self.seq_length, self.hidden_size]					)
         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    ,      __snake_case    ,      __snake_case    ,      __snake_case    ,      __snake_case					):
         snake_case					=       2_0
         snake_case					=       model_class_name(__snake_case					)

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

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

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

         snake_case					=       model(__snake_case					)

         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    ,      __snake_case    ,      __snake_case    ,      __snake_case    ,      __snake_case					):
         snake_case					=       2_0
         snake_case					=       model_class_name(__snake_case					)

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

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

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

         snake_case					=       model(__snake_case    ,      attention_mask=__snake_case					)

         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 						A__      (					snake_case__ ,      snake_case__ ,      unittest.TestCase			):







    """simple docstring"""

    __magic_name__    						=	(FlaxGPTaModel, FlaxGPTaLMHeadModel) if is_flax_available() else ()
    __magic_name__    						=	(FlaxGPTaLMHeadModel,) if is_flax_available() else ()
    def 	a_    ( self					):
         snake_case					=       FlaxGPTaModelTester(self					)
    def 	a_    ( self					):
         for model_class_name in self.all_model_classes:
              snake_case				,						snake_case				,						snake_case					=       self.model_tester.prepare_config_and_inputs()
              self.model_tester.check_use_cache_forward(__snake_case    ,      __snake_case    ,      __snake_case    ,      __snake_case					)
    def 	a_    ( self					):
         for model_class_name in self.all_model_classes:
              snake_case				,						snake_case				,						snake_case					=       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 	a_    ( self					):
         snake_case					=       GPTaTokenizer.from_pretrained('''gpt2'''    ,      pad_token='''</s>'''    ,      padding_side='''left'''					)
         snake_case					=       tokenizer(['''Hello this is a long string''', '''Hey''']    ,      return_tensors='''np'''    ,      padding=__snake_case    ,      truncation=__snake_case					)

         snake_case					=       FlaxGPTaLMHeadModel.from_pretrained('''gpt2'''					)
         snake_case					=       False
         snake_case					=       model.config.eos_token_id

         snake_case					=       jax.jit(model.generate					)

         snake_case					=       jit_generate(inputs['''input_ids''']    ,      attention_mask=inputs['''attention_mask''']					).sequences

         snake_case					=       tokenizer.batch_decode(__snake_case    ,      skip_special_tokens=__snake_case					)

         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(__snake_case    ,      __snake_case					)
    @is_pt_flax_cross_test
    def 	a_    ( self					):
         snake_case				,						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
                   snake_case					=       self._prepare_for_class(__snake_case    ,      __snake_case					)
                   snake_case					=       {k: torch.tensor(v.tolist()					) for k, v in prepared_inputs_dict.items()}

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

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

                   snake_case					=       convert_pytorch_state_dict_to_flax(pt_model.state_dict()    ,      __snake_case					)
                   snake_case					=       fx_state

                   with torch.no_grad():
                        snake_case					=       pt_model(**__snake_case					).to_tuple()

                   snake_case					=       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					)
                        snake_case					=       model_class.from_pretrained(__snake_case    ,      from_pt=__snake_case					)

                   snake_case					=       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 	a_    ( self					):
         snake_case				,						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
                   snake_case					=       self._prepare_for_class(__snake_case    ,      __snake_case					)
                   snake_case					=       {k: torch.tensor(v.tolist()					) for k, v in prepared_inputs_dict.items()}

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

                   snake_case					=       pt_model_class(__snake_case					).eval()
                   snake_case					=       model_class(__snake_case    ,      dtype=jnp.floataa					)

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

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

                   with torch.no_grad():
                        snake_case					=       pt_model(**__snake_case					).to_tuple()

                   snake_case					=       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					)
                        snake_case					=       pt_model_class.from_pretrained(__snake_case    ,      from_flax=__snake_case					)

                   with torch.no_grad():
                        snake_case					=       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 	a_    ( self					):
         for model_class_name in self.all_model_classes:
              snake_case					=       model_class_name.from_pretrained('''gpt2'''    ,      from_pt=__snake_case					)
              snake_case					=       model(np.ones((1, 1)					)					)
              self.assertIsNotNone(__snake_case					)








from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


_SCREAMING_SNAKE_CASE    :				Optional[int]       =					{
    "configuration_layoutlm": ["LAYOUTLM_PRETRAINED_CONFIG_ARCHIVE_MAP", "LayoutLMConfig", "LayoutLMOnnxConfig"],
    "tokenization_layoutlm": ["LayoutLMTokenizer"],
}

try:
 if not is_tokenizers_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _SCREAMING_SNAKE_CASE    :				str       =					["LayoutLMTokenizerFast"]

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _SCREAMING_SNAKE_CASE    :				List[str]       =					[
     "LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST",
     "LayoutLMForMaskedLM",
     "LayoutLMForSequenceClassification",
     "LayoutLMForTokenClassification",
     "LayoutLMForQuestionAnswering",
     "LayoutLMModel",
     "LayoutLMPreTrainedModel",
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _SCREAMING_SNAKE_CASE    :				Optional[int]       =					[
     "TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST",
     "TFLayoutLMForMaskedLM",
     "TFLayoutLMForSequenceClassification",
     "TFLayoutLMForTokenClassification",
     "TFLayoutLMForQuestionAnswering",
     "TFLayoutLMMainLayer",
     "TFLayoutLMModel",
     "TFLayoutLMPreTrainedModel",
 ]


if TYPE_CHECKING:
 from .configuration_layoutlm import LAYOUTLM_PRETRAINED_CONFIG_ARCHIVE_MAP, LayoutLMConfig, LayoutLMOnnxConfig
 from .tokenization_layoutlm import LayoutLMTokenizer

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_layoutlm_fast import LayoutLMTokenizerFast

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_layoutlm import (
      LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
      LayoutLMForMaskedLM,
      LayoutLMForQuestionAnswering,
      LayoutLMForSequenceClassification,
      LayoutLMForTokenClassification,
      LayoutLMModel,
      LayoutLMPreTrainedModel,
  )
 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_layoutlm import (
      TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFLayoutLMForMaskedLM,
      TFLayoutLMForQuestionAnswering,
      TFLayoutLMForSequenceClassification,
      TFLayoutLMForTokenClassification,
      TFLayoutLMMainLayer,
      TFLayoutLMModel,
      TFLayoutLMPreTrainedModel,
  )

else:
 import sys

 _SCREAMING_SNAKE_CASE    :				Optional[int]       =					_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)




