'''simple docstring'''

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 							lowercase_    :







    def __init__(	self       ,     UpperCamelCase__       ,     UpperCamelCase__=1_4       ,     UpperCamelCase__=7       ,     UpperCamelCase__=True       ,     UpperCamelCase__=True       ,     UpperCamelCase__=False       ,     UpperCamelCase__=True       ,     UpperCamelCase__=9_9       ,     UpperCamelCase__=3_2       ,     UpperCamelCase__=5       ,     UpperCamelCase__=4       ,     UpperCamelCase__=3_7       ,     UpperCamelCase__="gelu"       ,     UpperCamelCase__=0.1       ,     UpperCamelCase__=0.1       ,     UpperCamelCase__=5_1_2       ,     UpperCamelCase__=0.02       ,     )				->       Optional[Any]:





     """simple docstring"""

     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_				       =					intermediate_size
     UpperCAmelCase_				       =					hidden_act
     UpperCAmelCase_				       =					hidden_dropout_prob
     UpperCAmelCase_				       =					attention_probs_dropout_prob
     UpperCAmelCase_				       =					max_position_embeddings
     UpperCAmelCase_				       =					initializer_range
     UpperCAmelCase_				       =					None
     UpperCAmelCase_				       =					vocab_size - 1
     UpperCAmelCase_				       =					vocab_size - 1
     UpperCAmelCase_				       =					vocab_size - 1







    def   lowerCamelCase_    (	self				)				->       Dict:





     """simple docstring"""

     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_				       =					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=UpperCamelCase__       ,     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   lowerCamelCase_    (	self				)				->       List[Any]:





     """simple docstring"""

     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   lowerCamelCase_    (	self				)				->       Any:





     """simple docstring"""

     UpperCAmelCase_       ,       UpperCAmelCase_       ,       UpperCAmelCase_				       =					self.prepare_config_and_inputs()

     UpperCAmelCase_				       =					floats_tensor([self.batch_size, self.seq_length, self.hidden_size]				)
     UpperCAmelCase_				       =					ids_tensor([self.batch_size, self.seq_length]       ,     vocab_size=2				)

     return (
         config,
         input_ids,
         attention_mask,
         encoder_hidden_states,
         encoder_attention_mask,
     )







    def   lowerCamelCase_    (	self       ,     UpperCamelCase__       ,     UpperCamelCase__       ,     UpperCamelCase__       ,     UpperCamelCase__				)				->       Optional[int]:





     """simple docstring"""

     UpperCAmelCase_				       =					2_0
     UpperCAmelCase_				       =					model_class_name(UpperCamelCase__				)

     UpperCAmelCase_				       =					model.init_cache(input_ids.shape[0]       ,     UpperCamelCase__				)
     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=UpperCamelCase__       ,     past_key_values=UpperCamelCase__       ,     position_ids=UpperCamelCase__       ,     )

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

     UpperCAmelCase_				       =					model(UpperCamelCase__				)

     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   lowerCamelCase_    (	self       ,     UpperCamelCase__       ,     UpperCamelCase__       ,     UpperCamelCase__       ,     UpperCamelCase__				)				->       str:





     """simple docstring"""

     UpperCAmelCase_				       =					2_0
     UpperCAmelCase_				       =					model_class_name(UpperCamelCase__				)

     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]       ,     UpperCamelCase__				)
     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=UpperCamelCase__       ,     past_key_values=UpperCamelCase__       ,     position_ids=UpperCamelCase__       ,     )
     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=UpperCamelCase__       ,     position_ids=UpperCamelCase__       ,     )

     UpperCAmelCase_				       =					model(UpperCamelCase__       ,     attention_mask=UpperCamelCase__				)

     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 							lowercase_    (     _A ,  _A ,  unittest.TestCase						):
    a_             =   (FlaxGPTaModel, FlaxGPTaLMHeadModel) if is_flax_available() else ()
    a_             =   (FlaxGPTaLMHeadModel,) if is_flax_available() else ()







    def   lowerCamelCase_    (	self				)				->       Any:





     """simple docstring"""

     UpperCAmelCase_				       =					FlaxGPTaModelTester(self				)







    def   lowerCamelCase_    (	self				)				->       Any:





     """simple docstring"""

     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(UpperCamelCase__       ,     UpperCamelCase__       ,     UpperCamelCase__       ,     UpperCamelCase__				)







    def   lowerCamelCase_    (	self				)				->       Any:





     """simple docstring"""

     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(
          UpperCamelCase__       ,     UpperCamelCase__       ,     UpperCamelCase__       ,     UpperCamelCase__				)







    @slow
    def   lowerCamelCase_    (	self				)				->       List[Any]:





     """simple docstring"""

     UpperCAmelCase_				       =					GPTaTokenizer.from_pretrained("gpt2"       ,     pad_token="</s>"       ,     padding_side="left"				)
     UpperCAmelCase_				       =					tokenizer(["Hello this is a long string", "Hey"]       ,     return_tensors="np"       ,     padding=UpperCamelCase__       ,     truncation=UpperCamelCase__				)

     UpperCAmelCase_				       =					FlaxGPTaLMHeadModel.from_pretrained("gpt2"				)
     UpperCAmelCase_				       =					False
     UpperCAmelCase_				       =					model.config.eos_token_id

     UpperCAmelCase_				       =					jax.jit(model.generate				)

     UpperCAmelCase_				       =					jit_generate(inputs["input_ids"]       ,     attention_mask=inputs["attention_mask"]				).sequences

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

     UpperCAmelCase_				       =					[
         "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(UpperCamelCase__       ,     UpperCamelCase__				)







    @is_pt_flax_cross_test
    def   lowerCamelCase_    (	self				)				->       Tuple:





     """simple docstring"""

     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(UpperCamelCase__       ,     UpperCamelCase__				)
       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(UpperCamelCase__       ,     UpperCamelCase__				)

       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(UpperCamelCase__				):
        UpperCAmelCase_				       =					0
        UpperCAmelCase_				       =					1
        UpperCAmelCase_				       =					0
        UpperCAmelCase_				       =					1
       UpperCAmelCase_				       =					pt_model_class(UpperCamelCase__				).eval()
       UpperCAmelCase_				       =					model_class(UpperCamelCase__       ,     dtype=jnp.floataa				)

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

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

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

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

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







    @is_pt_flax_cross_test
    def   lowerCamelCase_    (	self				)				->       int:





     """simple docstring"""

     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(UpperCamelCase__       ,     UpperCamelCase__				)
       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(UpperCamelCase__       ,     UpperCamelCase__				)

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

       UpperCAmelCase_				       =					load_flax_weights_in_pytorch_model(UpperCamelCase__       ,     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(UpperCamelCase__				):
        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(**UpperCamelCase__				).to_tuple()

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

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

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

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







    @slow
    def   lowerCamelCase_    (	self				)				->       List[Any]:





     """simple docstring"""

     for model_class_name in self.all_model_classes:
      UpperCAmelCase_				       =					model_class_name.from_pretrained("gpt2"       ,     from_pt=UpperCamelCase__				)
      UpperCAmelCase_				       =					model(np.ones((1, 1)				)				)
      self.assertIsNotNone(UpperCamelCase__				)

'''simple docstring'''

from ..utils import DummyObject, requires_backends
class 							lowercase_    (     metaclass=_A						):
    a_             =   ["""torch""", """librosa"""]







    def __init__(	self       ,     *UpperCamelCase__       ,     **UpperCamelCase__				)				->       int:





     """simple docstring"""

     requires_backends(self       ,     ["torch", "librosa"]				)







    @classmethod
    def   lowerCamelCase_    (	cls       ,     *UpperCamelCase__       ,     **UpperCamelCase__				)				->       Union[str, Any]:





     """simple docstring"""

     requires_backends(cls       ,     ["torch", "librosa"]				)







    @classmethod
    def   lowerCamelCase_    (	cls       ,     *UpperCamelCase__       ,     **UpperCamelCase__				)				->       Dict:





     """simple docstring"""

     requires_backends(cls       ,     ["torch", "librosa"]				)


class 							lowercase_    (     metaclass=_A						):
    a_             =   ["""torch""", """librosa"""]







    def __init__(	self       ,     *UpperCamelCase__       ,     **UpperCamelCase__				)				->       str:





     """simple docstring"""

     requires_backends(self       ,     ["torch", "librosa"]				)







    @classmethod
    def   lowerCamelCase_    (	cls       ,     *UpperCamelCase__       ,     **UpperCamelCase__				)				->       Union[str, Any]:





     """simple docstring"""

     requires_backends(cls       ,     ["torch", "librosa"]				)







    @classmethod
    def   lowerCamelCase_    (	cls       ,     *UpperCamelCase__       ,     **UpperCamelCase__				)				->       int:





     """simple docstring"""

     requires_backends(cls       ,     ["torch", "librosa"]				)
