




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   ,_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE=14   ,_SCREAMING_SNAKE_CASE=7   ,_SCREAMING_SNAKE_CASE=True   ,_SCREAMING_SNAKE_CASE=True   ,_SCREAMING_SNAKE_CASE=False   ,_SCREAMING_SNAKE_CASE=True   ,_SCREAMING_SNAKE_CASE=99   ,_SCREAMING_SNAKE_CASE=32   ,_SCREAMING_SNAKE_CASE=5   ,_SCREAMING_SNAKE_CASE=4   ,_SCREAMING_SNAKE_CASE=37   ,_SCREAMING_SNAKE_CASE="gelu"   ,_SCREAMING_SNAKE_CASE=0.1   ,_SCREAMING_SNAKE_CASE=0.1   ,_SCREAMING_SNAKE_CASE=512   ,_SCREAMING_SNAKE_CASE=0.02   ,) ->		Union[str, Any]:
         UpperCAmelCase_			:							Any   =			parent
         UpperCAmelCase_			:							Any   =			batch_size
         UpperCAmelCase_			:							str   =			seq_length
         UpperCAmelCase_			:							Union[str, Any]   =			is_training
         UpperCAmelCase_			:							Optional[int]   =			use_input_mask
         UpperCAmelCase_			:							Any   =			use_token_type_ids
         UpperCAmelCase_			:							Any   =			use_labels
         UpperCAmelCase_			:							Optional[int]   =			vocab_size
         UpperCAmelCase_			:							Tuple   =			hidden_size
         UpperCAmelCase_			:							Tuple   =			num_hidden_layers
         UpperCAmelCase_			:							Any   =			num_attention_heads
         UpperCAmelCase_			:							Union[str, Any]   =			intermediate_size
         UpperCAmelCase_			:							List[Any]   =			hidden_act
         UpperCAmelCase_			:							Tuple   =			hidden_dropout_prob
         UpperCAmelCase_			:							Optional[int]   =			attention_probs_dropout_prob
         UpperCAmelCase_			:							Union[str, Any]   =			max_position_embeddings
         UpperCAmelCase_			:							List[str]   =			initializer_range
         UpperCAmelCase_			:							Dict   =			None
         UpperCAmelCase_			:							Union[str, Any]   =			vocab_size - 1
         UpperCAmelCase_			:							int   =			vocab_size - 1
         UpperCAmelCase_			:							List[str]   =			vocab_size - 1





    def 							a__      (      self  ) ->		Any:
         UpperCAmelCase_			:							Tuple   =			ids_tensor([self.batch_size, self.seq_length]   ,self.vocab_size  )

         UpperCAmelCase_			:							Any   =			None
         if self.use_input_mask:
              UpperCAmelCase_			:							Union[str, Any]   =			random_attention_mask([self.batch_size, self.seq_length]  )

         UpperCAmelCase_			:							Optional[Any]   =			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 							a__      (      self  ) ->		str:
         UpperCAmelCase_			:							Tuple   =			self.prepare_config_and_inputs()
         UpperCAmelCase_,    UpperCAmelCase_,    UpperCAmelCase_			:							Union[str, Any]   =			config_and_inputs
         UpperCAmelCase_			:							int   =			{'''input_ids''': input_ids, '''attention_mask''': attention_mask}
         return config, inputs_dict





    def 							a__      (      self  ) ->		str:
         UpperCAmelCase_,    UpperCAmelCase_,    UpperCAmelCase_			:							Union[str, Any]   =			self.prepare_config_and_inputs()

         UpperCAmelCase_			:							List[str]   =			floats_tensor([self.batch_size, self.seq_length, self.hidden_size]  )
         UpperCAmelCase_			:							List[str]   =			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   ,_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE  ) ->		Dict:
         UpperCAmelCase_			:							Tuple   =			20
         UpperCAmelCase_			:							Tuple   =			model_class_name(__UpperCamelCase  )

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

         UpperCAmelCase_			:							Union[str, Any]   =			jnp.broadcast_to(
             jnp.arange(input_ids.shape[-1] - 1  )[None, :]   ,(input_ids.shape[0], input_ids.shape[-1] - 1)  )
         UpperCAmelCase_			:							Any   =			model(
             input_ids[:, :-1]   ,attention_mask=__UpperCamelCase   ,past_key_values=__UpperCamelCase   ,position_ids=__UpperCamelCase   ,)

         UpperCAmelCase_			:							Optional[Any]   =			jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]   ,dtype='''i4'''  )
         UpperCAmelCase_			:							Dict   =			model(
             input_ids[:, -1:]   ,attention_mask=__UpperCamelCase   ,past_key_values=outputs_cache.past_key_values   ,position_ids=__UpperCamelCase   ,)

         UpperCAmelCase_			:							Optional[int]   =			model(__UpperCamelCase  )

         UpperCAmelCase_			:							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}'''  )





    def 							a__      (      self   ,_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE  ) ->		Dict:
         UpperCAmelCase_			:							Tuple   =			20
         UpperCAmelCase_			:							Optional[Any]   =			model_class_name(__UpperCamelCase  )

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

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

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

         UpperCAmelCase_			:							List[str]   =			model(__UpperCamelCase   ,attention_mask=__UpperCamelCase  )

         UpperCAmelCase_			:							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   __a(     UpperCAmelCase_				,						UpperCAmelCase_				,						unittest.TestCase   ):






    """simple docstring"""





    lowerCAmelCase  			=      (FlaxGPTaModel, FlaxGPTaLMHeadModel) if is_flax_available() else ()
    lowerCAmelCase  			=      (FlaxGPTaLMHeadModel,) if is_flax_available() else ()





    def 							a__      (      self  ) ->		Optional[Any]:
         UpperCAmelCase_			:							Dict   =			FlaxGPTaModelTester(self  )





    def 							a__      (      self  ) ->		Tuple:
         for model_class_name in self.all_model_classes:
              UpperCAmelCase_,    UpperCAmelCase_,    UpperCAmelCase_			:							Union[str, Any]   =			self.model_tester.prepare_config_and_inputs()
              self.model_tester.check_use_cache_forward(__UpperCamelCase   ,__UpperCamelCase   ,__UpperCamelCase   ,__UpperCamelCase  )





    def 							a__      (      self  ) ->		Dict:
         for model_class_name in self.all_model_classes:
              UpperCAmelCase_,    UpperCAmelCase_,    UpperCAmelCase_			:							str   =			self.model_tester.prepare_config_and_inputs()
              self.model_tester.check_use_cache_forward_with_attn_mask(
                  __UpperCamelCase   ,__UpperCamelCase   ,__UpperCamelCase   ,__UpperCamelCase  )





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

         UpperCAmelCase_			:							Dict   =			FlaxGPTaLMHeadModel.from_pretrained('''gpt2'''  )
         UpperCAmelCase_			:							Dict   =			False
         UpperCAmelCase_			:							List[Any]   =			model.config.eos_token_id

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

         UpperCAmelCase_			:							List[Any]   =			jit_generate(inputs['''input_ids''']   ,attention_mask=inputs['''attention_mask''']  ).sequences

         UpperCAmelCase_			:							str   =			tokenizer.batch_decode(__UpperCamelCase   ,skip_special_tokens=__UpperCamelCase  )

         UpperCAmelCase_			:							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(__UpperCamelCase   ,__UpperCamelCase  )





    @is_pt_flax_cross_test
    def 							a__      (      self  ) ->		int:
         UpperCAmelCase_,    UpperCAmelCase_			:							Optional[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
                   UpperCAmelCase_			:							Union[str, Any]   =			self._prepare_for_class(__UpperCamelCase   ,__UpperCamelCase  )
                   UpperCAmelCase_			:							List[str]   =			{k: torch.tensor(v.tolist()  ) for k, v in prepared_inputs_dict.items()}

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

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

                   UpperCAmelCase_			:							List[Any]   =			convert_pytorch_state_dict_to_flax(pt_model.state_dict()   ,__UpperCamelCase  )
                   UpperCAmelCase_			:							List[str]   =			fx_state

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

                   UpperCAmelCase_			:							Union[str, Any]   =			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_			:							List[Any]   =			model_class.from_pretrained(__UpperCamelCase   ,from_pt=__UpperCamelCase  )

                   UpperCAmelCase_			:							Optional[int]   =			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 							a__      (      self  ) ->		Optional[int]:
         UpperCAmelCase_,    UpperCAmelCase_			:							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
                   UpperCAmelCase_			:							int   =			self._prepare_for_class(__UpperCamelCase   ,__UpperCamelCase  )
                   UpperCAmelCase_			:							Tuple   =			{k: torch.tensor(v.tolist()  ) for k, v in prepared_inputs_dict.items()}

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

                   UpperCAmelCase_			:							Optional[int]   =			pt_model_class(__UpperCamelCase  ).eval()
                   UpperCAmelCase_			:							Tuple   =			model_class(__UpperCamelCase   ,dtype=jnp.floataa  )

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

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

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

                   UpperCAmelCase_			:							Any   =			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_			:							Optional[int]   =			pt_model_class.from_pretrained(__UpperCamelCase   ,from_flax=__UpperCamelCase  )

                   with torch.no_grad():
                        UpperCAmelCase_			:							Any   =			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 							a__      (      self  ) ->		Tuple:
         for model_class_name in self.all_model_classes:
              UpperCAmelCase_			:							str   =			model_class_name.from_pretrained('''gpt2'''   ,from_pt=__UpperCamelCase  )
              UpperCAmelCase_			:							int   =			model(np.ones((1, 1)  )  )
              self.assertIsNotNone(__UpperCamelCase  )





'''simple docstring'''







def 		_lowercase		(	lowercase_  ):



		'''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()





