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				,  )						->int:
        a	:     List[Any]						      =    parent
        a	:     Tuple						      =    batch_size
        a	:     Optional[int]						      =    seq_length
        a	:     Any						      =    is_training
        a	:     List[Any]						      =    use_input_mask
        a	:     List[str]						      =    use_token_type_ids
        a	:     Tuple						      =    use_labels
        a	:     List[Any]						      =    vocab_size
        a	:     Tuple						      =    hidden_size
        a	:     str						      =    num_hidden_layers
        a	:     Dict						      =    num_attention_heads
        a	:     Dict						      =    attention_types
        a	:     Union[str, Any]						      =    intermediate_size
        a	:     Dict						      =    hidden_act
        a	:     Dict						      =    hidden_dropout_prob
        a	:     Dict						      =    attention_probs_dropout_prob
        a	:     Any						      =    max_position_embeddings
        a	:     int						      =    window_size
        a	:     Union[str, Any]						      =    initializer_range
        a	:     Union[str, Any]						      =    None
        a	:     str						      =    vocab_size - 1
        a	:     List[str]						      =    vocab_size - 1
        a	:     Union[str, Any]						      =    vocab_size - 1
   def _lowercase(     self     )						->Optional[int]:
        a	:     List[Any]						      =    ids_tensor([self.batch_size, self.seq_length]				,  self.vocab_size     )

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

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

        a	:     int						      =    model.init_cache(input_ids.shape[0]				,  lowerCAmelCase_     )
        a	:     Dict						      =    jnp.ones((input_ids.shape[0], max_decoder_length)				,  dtype='''i4'''     )

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

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

        a	:     Optional[Any]						      =    model(lowerCAmelCase_     )

        a	:     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 _lowercase(     self				,  lowerCAmelCase_				,  lowerCAmelCase_				,  lowerCAmelCase_				,  lowerCAmelCase_     )						->List[str]:
        a	:     str						      =    20
        a	:     Tuple						      =    model_class_name(lowerCAmelCase_     )

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

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

        a	:     Optional[Any]						      =    model(
            input_ids[:, :-1]				,  attention_mask=lowerCAmelCase_				,  past_key_values=lowerCAmelCase_				,  position_ids=lowerCAmelCase_				,  )
        a	:     int						      =    jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]				,  dtype='''i4'''     )
        a	:     Union[str, Any]						      =    model(
            input_ids[:, -1:]				,  past_key_values=outputs_cache.past_key_values				,  attention_mask=lowerCAmelCase_				,  position_ids=lowerCAmelCase_				,  )

        a	:     Tuple						      =    model(lowerCAmelCase_				,  attention_mask=lowerCAmelCase_     )

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

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

        a	:     Optional[int]						      =    jax.jit(model.generate     )

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

        a	:     List[Any]						      =    tokenizer.batch_decode(lowerCAmelCase_				,  skip_special_tokens=lowerCAmelCase_     )

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

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

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

                  a	:     Union[str, Any]						      =    convert_pytorch_state_dict_to_flax(pt_model.state_dict()				,  lowerCAmelCase_     )
                  a	:     str						      =    fx_state

                  with torch.no_grad():
                       a	:     List[Any]						      =    pt_model(**lowerCAmelCase_     ).to_tuple()

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

                  with tempfile.TemporaryDirectory() as tmpdirname:
                       pt_model.save_pretrained(lowerCAmelCase_     )
                       a	:     Dict						      =    model_class.from_pretrained(lowerCAmelCase_				,  from_pt=lowerCAmelCase_     )

                  a	:     str						      =    fx_model_loaded(**lowerCAmelCase_     ).to_tuple()
                  self.assertEqual(
                      len(lowerCAmelCase_     )				,  len(lowerCAmelCase_     )				,  '''Output lengths differ between Flax and PyTorch'''     )
                  for fx_output_loaded, pt_output in zip(lowerCAmelCase_				,  lowerCAmelCase_     ):
                       self.assert_almost_equals(fx_output_loaded[:, -1]				,  pt_output[:, -1].numpy()				,  4e-2     )
   @is_pt_flax_cross_test
   def _lowercase(     self     )						->Optional[int]:
        a      ,				a	:     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(lowerCAmelCase_				,  lowerCAmelCase_     )
                  a	:     Optional[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	:     Optional[Any]						      =    getattr(lowerCAmelCase_				,  lowerCAmelCase_     )

                  a	:     Any						      =    pt_model_class(lowerCAmelCase_     ).eval()
                  a	:     Optional[Any]						      =    model_class(lowerCAmelCase_				,  dtype=jnp.floataa     )

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

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

                  with torch.no_grad():
                       a	:     Any						      =    pt_model(**lowerCAmelCase_     ).to_tuple()

                  a	:     Union[str, Any]						      =    fx_model(**lowerCAmelCase_     ).to_tuple()
                  self.assertEqual(len(lowerCAmelCase_     )				,  len(lowerCAmelCase_     )				,  '''Output lengths differ between Flax and PyTorch'''     )
                  for fx_output, pt_output in zip(lowerCAmelCase_				,  lowerCAmelCase_     ):
                       self.assert_almost_equals(fx_output[:, -1]				,  pt_output[:, -1].numpy()				,  4e-2     )

                  with tempfile.TemporaryDirectory() as tmpdirname:
                       fx_model.save_pretrained(lowerCAmelCase_     )
                       a	:     Optional[int]						      =    pt_model_class.from_pretrained(lowerCAmelCase_				,  from_flax=lowerCAmelCase_     )

                  with torch.no_grad():
                       a	:     Optional[Any]						      =    pt_model_loaded(**lowerCAmelCase_     ).to_tuple()

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


   @slow
   def _lowercase(     self     )						->str:
        for model_class_name in self.all_model_classes:
             a	:     Optional[int]						      =    model_class_name.from_pretrained('''EleutherAI/gpt-neo-125M'''     )
             a	:     Optional[Any]						      =    model(np.ones((1, 1)     )     )
             self.assertIsNotNone(lowerCAmelCase_     )
from typing import TYPE_CHECKING

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


UpperCAmelCase				 =   {"""configuration_pegasus""": ["""PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP""", """PegasusConfig"""]}

try:
  if not is_sentencepiece_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase				 =   ["""PegasusTokenizer"""]

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase				 =   ["""PegasusTokenizerFast"""]

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase				 =   [
      """PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """PegasusForCausalLM""",
      """PegasusForConditionalGeneration""",
      """PegasusModel""",
      """PegasusPreTrainedModel""",
  ]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase				 =   [
      """TFPegasusForConditionalGeneration""",
      """TFPegasusModel""",
      """TFPegasusPreTrainedModel""",
  ]

try:
  if not is_flax_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase				 =   [
      """FlaxPegasusForConditionalGeneration""",
      """FlaxPegasusModel""",
      """FlaxPegasusPreTrainedModel""",
  ]


if TYPE_CHECKING:
  from .configuration_pegasus import PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP, PegasusConfig

  try:
    if not is_sentencepiece_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_pegasus import PegasusTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_pegasus_fast import PegasusTokenizerFast

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_pegasus import (
        PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST,
        PegasusForCausalLM,
        PegasusForConditionalGeneration,
        PegasusModel,
        PegasusPreTrainedModel,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_pegasus import TFPegasusForConditionalGeneration, TFPegasusModel, TFPegasusPreTrainedModel

  try:
    if not is_flax_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_flax_pegasus import (
        FlaxPegasusForConditionalGeneration,
        FlaxPegasusModel,
        FlaxPegasusPreTrainedModel,
    )

else:
  import sys

  UpperCAmelCase				 =   _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)