

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				,    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=512				,    lowerCamelCase=7				,    lowerCamelCase=0.02				,    )		->			str:
        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_					= attention_types
        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_					= window_size
        snake_case_					= initializer_range
        snake_case_					= None
        snake_case_					= vocab_size - 1
        snake_case_					= vocab_size - 1
        snake_case_					= vocab_size - 1







      def       lowerCAmelCase_       (	self  )		->			Any:
        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_					= 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       lowerCAmelCase_       (	self  )		->			str:
        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       lowerCAmelCase_       (	self				,    lowerCamelCase				,    lowerCamelCase				,    lowerCamelCase				,    lowerCamelCase  )		->			Union[str, Any]:
        snake_case_					= 20
        snake_case_					= model_class_name(lowerCamelCase  )

        snake_case_					= model.init_cache(input_ids.shape[0]				,    lowerCamelCase  )
        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=lowerCamelCase				,    past_key_values=lowerCamelCase				,    position_ids=lowerCamelCase				,    )

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

        snake_case_					= model(lowerCamelCase  )

        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       lowerCAmelCase_       (	self				,    lowerCamelCase				,    lowerCamelCase				,    lowerCamelCase				,    lowerCamelCase  )		->			Any:
        snake_case_					= 20
        snake_case_					= model_class_name(lowerCamelCase  )

        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]				,    lowerCamelCase  )
        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=lowerCamelCase				,    past_key_values=lowerCamelCase				,    position_ids=lowerCamelCase				,    )
        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=lowerCamelCase				,    position_ids=lowerCamelCase				,    )

        snake_case_					= model(lowerCamelCase				,    attention_mask=lowerCamelCase  )

        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 					__lowerCamelCase							(      __snake_case    ,				__snake_case    ,				unittest.TestCase      ):
      lowerCamelCase_      : Dict  			=   (FlaxGPTNeoModel, FlaxGPTNeoForCausalLM) if is_flax_available() else ()
      lowerCamelCase_      : Optional[Any]  			=   (FlaxGPTNeoForCausalLM,) if is_flax_available() else ()







      def       lowerCAmelCase_       (	self  )		->			List[Any]:
        snake_case_					= FlaxGPTNeoModelTester(self  )







      def       lowerCAmelCase_       (	self  )		->			int:
        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(lowerCamelCase				,    lowerCamelCase				,    lowerCamelCase				,    lowerCamelCase  )







      def       lowerCAmelCase_       (	self  )		->			Union[str, Any]:
        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(
              lowerCamelCase				,    lowerCamelCase				,    lowerCamelCase				,    lowerCamelCase  )







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

        snake_case_					= FlaxGPTNeoForCausalLM.from_pretrained("""EleutherAI/gpt-neo-125M"""  )
        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"""]				,    pad_token_id=tokenizer.pad_token_id  ).sequences

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

        snake_case_					= [
            """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       lowerCAmelCase_       (	self  )		->			Optional[Any]:
        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(lowerCamelCase				,    lowerCamelCase  )
            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(lowerCamelCase				,    lowerCamelCase  )

            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(lowerCamelCase  ):
              snake_case_					= 0
              snake_case_					= 1
              snake_case_					= 0
              snake_case_					= 1
            snake_case_					= pt_model_class(lowerCamelCase  ).eval()
            snake_case_					= model_class(lowerCamelCase				,    dtype=jnp.floataa  )

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

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

            snake_case_					= 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  )
              snake_case_					= model_class.from_pretrained(lowerCamelCase				,    from_pt=lowerCamelCase  )

            snake_case_					= 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       lowerCAmelCase_       (	self  )		->			Any:
        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(lowerCamelCase				,    lowerCamelCase  )
            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(lowerCamelCase				,    lowerCamelCase  )

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

            snake_case_					= load_flax_weights_in_pytorch_model(lowerCamelCase				,    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(lowerCamelCase  ):
              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(**lowerCamelCase  ).to_tuple()

            snake_case_					= 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  )
              snake_case_					= pt_model_class.from_pretrained(lowerCamelCase				,    from_flax=lowerCamelCase  )

            with torch.no_grad():
              snake_case_					= 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       lowerCAmelCase_       (	self  )		->			Optional[int]:
        for model_class_name in self.all_model_classes:
          snake_case_					= model_class_name.from_pretrained("""EleutherAI/gpt-neo-125M"""  )
          snake_case_					= model(np.ones((1, 1)  )  )
          self.assertIsNotNone(lowerCamelCase  )


def   UpperCamelCase(   lowercase_				)      ->							str:





  '''simple docstring'''





  return "".join(chr(ord(lowercase_				) + 32				) if """A""" <= char <= """Z""" else char for char in word				)


if __name__ == "__main__":
    from doctest import testmod

    testmod()