



'''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   SCREAMING_SNAKE_CASE__				:
 """simple docstring"""


 def __init__(		self					,   A					,   A=14					,   A=7					,   A=True					,   A=True					,   A=False					,   A=True					,   A=99					,   A=32					,   A=5					,   A=4					,   A=37					,   A="gelu"					,   A=0.1					,   A=0.1					,   A=5_12					,   A=0.02					,   ) ->							Tuple:
    A:      int														=   parent
    A:      List[Any]														=   batch_size
    A:      str														=   seq_length
    A:      Dict														=   is_training
    A:      Union[str, Any]														=   use_input_mask
    A:      Optional[Any]														=   use_token_type_ids
    A:      str														=   use_labels
    A:      Any														=   vocab_size
    A:      Union[str, Any]														=   hidden_size
    A:      Tuple														=   num_hidden_layers
    A:      Any														=   num_attention_heads
    A:      List[Any]														=   intermediate_size
    A:      Any														=   hidden_act
    A:      Tuple														=   hidden_dropout_prob
    A:      Union[str, Any]														=   attention_probs_dropout_prob
    A:      str														=   max_position_embeddings
    A:      int														=   initializer_range
    A:      List[str]														=   None
    A:      Any														=   vocab_size - 1
    A:      Dict														=   vocab_size - 1
    A:      Union[str, Any]														=   vocab_size - 1


 def      a__ (		self			) ->							str:
    A:      Optional[Any]														=   ids_tensor([self.batch_size, self.seq_length]					,   self.vocab_size			)

    A:      Tuple														=   None
    if self.use_input_mask:
       A:      Optional[Any]														=   random_attention_mask([self.batch_size, self.seq_length]			)

    A:      List[str]														=   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=A					,   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			) ->							int:
    A:      Any														=   self.prepare_config_and_inputs()
    A:      Tuple														=   config_and_inputs
    A:      str														=   {"""input_ids""": input_ids, """attention_mask""": attention_mask}
    return config, inputs_dict


 def      a__ (		self			) ->							Optional[Any]:
    A:      Tuple														=   self.prepare_config_and_inputs()

    A:      Tuple														=   floats_tensor([self.batch_size, self.seq_length, self.hidden_size]			)
    A:      Optional[int]														=   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					,   A					,   A					,   A					,   A			) ->							str:
    A:      Any														=   20
    A:      Union[str, Any]														=   model_class_name(A			)

    A:      Dict														=   model.init_cache(input_ids.shape[0]					,   A			)
    A:      Union[str, Any]														=   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=A					,   past_key_values=A					,   position_ids=A					,   )

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

    A:      Optional[int]														=   model(A			)

    A:      List[str]														=   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					,   A					,   A					,   A					,   A			) ->							List[Any]:
    A:      int														=   20
    A:      Dict														=   model_class_name(A			)

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

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

    A:      Union[str, Any]														=   model(
        input_ids[:, :-1]					,   attention_mask=A					,   past_key_values=A					,   position_ids=A					,   )
    A:      Tuple														=   jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]					,   dtype="""i4"""			)
    A:      Tuple														=   model(
        input_ids[:, -1:]					,   past_key_values=outputs_cache.past_key_values					,   attention_mask=A					,   position_ids=A					,   )

    A:      List[str]														=   model(A					,   attention_mask=A			)

    A:      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}'			)







@require_flax
class   SCREAMING_SNAKE_CASE__				(   snake_case_     ,			snake_case_     ,			unittest.TestCase					):
 """simple docstring"""


 A__    :    Optional[Any]						=				(FlaxGPTaModel, FlaxGPTaLMHeadModel) if is_flax_available() else ()
 A__    :    Any						=				(FlaxGPTaLMHeadModel,) if is_flax_available() else ()


 def      a__ (		self			) ->							List[str]:
    A:      Dict														=   FlaxGPTaModelTester(self			)


 def      a__ (		self			) ->							Optional[Any]:
    for model_class_name in self.all_model_classes:
       A:      Optional[Any]														=   self.model_tester.prepare_config_and_inputs()
       self.model_tester.check_use_cache_forward(A					,   A					,   A					,   A			)


 def      a__ (		self			) ->							Any:
    for model_class_name in self.all_model_classes:
       A:      Union[str, Any]														=   self.model_tester.prepare_config_and_inputs()
       self.model_tester.check_use_cache_forward_with_attn_mask(
           A					,   A					,   A					,   A			)


 @slow
 def      a__ (		self			) ->							List[Any]:
    A:      str														=   GPTaTokenizer.from_pretrained("""gpt2"""					,   pad_token="""</s>"""					,   padding_side="""left"""			)
    A:      Any														=   tokenizer(["""Hello this is a long string""", """Hey"""]					,   return_tensors="""np"""					,   padding=A					,   truncation=A			)

    A:      str														=   FlaxGPTaLMHeadModel.from_pretrained("""gpt2"""			)
    A:      List[str]														=   False
    A:      Union[str, Any]														=   model.config.eos_token_id

    A:      List[Any]														=   jax.jit(model.generate			)

    A:      Optional[Any]														=   jit_generate(inputs["""input_ids"""]					,   attention_mask=inputs["""attention_mask"""]			).sequences

    A:      int														=   tokenizer.batch_decode(A					,   skip_special_tokens=A			)

    A:      Union[str, Any]														=   [
        """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(A					,   A			)


 @is_pt_flax_cross_test
 def      a__ (		self			) ->							str:
    A:      int														=   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(A					,   A			)
          A:      Tuple														=   {k: torch.tensor(v.tolist()			) for k, v in prepared_inputs_dict.items()}

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

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

          A:      int														=   convert_pytorch_state_dict_to_flax(pt_model.state_dict()					,   A			)
          A:      str														=   fx_state

          with torch.no_grad():
             A:      Dict														=   pt_model(**A			).to_tuple()

          A:      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			)
             A:      List[str]														=   model_class.from_pretrained(A					,   from_pt=A			)

          A:      List[str]														=   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      a__ (		self			) ->							str:
    A:      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
          A:      int														=   self._prepare_for_class(A					,   A			)
          A:      List[str]														=   {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(A					,   A			)

          A:      str														=   pt_model_class(A			).eval()
          A:      List[Any]														=   model_class(A					,   dtype=jnp.floataa			)

          A:      Any														=   load_flax_weights_in_pytorch_model(A					,   fx_model.params			)
          A:      Any														=   pt_inputs["""input_ids"""].shape
          A:      int														=   np.random.randint(0					,   seq_length - 1					,   size=(batch_size,)			)
          for batch_idx, start_index in enumerate(A			):
             A:      Optional[Any]														=   0
             A:      Any														=   1
             A:      Dict														=   0
             A:      Optional[int]														=   1

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

          with torch.no_grad():
             A:      Dict														=   pt_model(**A			).to_tuple()

          A:      Optional[int]														=   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			)
             A:      Any														=   pt_model_class.from_pretrained(A					,   from_flax=A			)

          with torch.no_grad():
             A:      Optional[Any]														=   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      a__ (		self			) ->							Optional[Any]:
    for model_class_name in self.all_model_classes:
       A:      Any														=   model_class_name.from_pretrained("""gpt2"""					,   from_pt=A			)
       A:      Any														=   model(np.ones((1, 1)			)			)
       self.assertIsNotNone(A			)










'''simple docstring'''
from ..utils import DummyObject, requires_backends


class   SCREAMING_SNAKE_CASE__				(   metaclass=snake_case_					):
	"""simple docstring"""


	A__    :    Optional[int]						=				['''torch''', '''transformers''', '''invisible_watermark''']


	def __init__(		self					,   *A					,   **A			) ->							Tuple:
				requires_backends(self					,   ["""torch""", """transformers""", """invisible_watermark"""]			)


	@classmethod
	def      a__ (		cls					,   *A					,   **A			) ->							Optional[int]:
				requires_backends(cls					,   ["""torch""", """transformers""", """invisible_watermark"""]			)





	@classmethod
	def      a__ (		cls					,   *A					,   **A			) ->							List[Any]:
				requires_backends(cls					,   ["""torch""", """transformers""", """invisible_watermark"""]			)







class   SCREAMING_SNAKE_CASE__				(   metaclass=snake_case_					):
	"""simple docstring"""


	A__    :    Optional[int]						=				['''torch''', '''transformers''', '''invisible_watermark''']


	def __init__(		self					,   *A					,   **A			) ->							List[str]:
				requires_backends(self					,   ["""torch""", """transformers""", """invisible_watermark"""]			)


	@classmethod
	def      a__ (		cls					,   *A					,   **A			) ->							Union[str, Any]:
				requires_backends(cls					,   ["""torch""", """transformers""", """invisible_watermark"""]			)





	@classmethod
	def      a__ (		cls					,   *A					,   **A			) ->							Union[str, Any]:
				requires_backends(cls					,   ["""torch""", """transformers""", """invisible_watermark"""]			)





