
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 						UpperCAmelCase	:



   def __init__(self	:      int					,		snake_case__	:      List[str]					,		snake_case__	:      List[Any]=14					,		snake_case__	:      Optional[Any]=7					,		snake_case__	:      Optional[Any]=True					,		snake_case__	:      Tuple=True					,		snake_case__	:      Dict=False					,		snake_case__	:      Optional[Any]=True					,		snake_case__	:      List[str]=99					,		snake_case__	:      Tuple=32					,		snake_case__	:      int=5					,		snake_case__	:      Optional[Any]=4					,		snake_case__	:      Tuple=37					,		snake_case__	:      Optional[int]="gelu"					,		snake_case__	:      Optional[Any]=0.1					,		snake_case__	:      Dict=0.1					,		snake_case__	:      int=5_12					,		snake_case__	:      Optional[Any]=0.02					,		)	->			Optional[int]:


          '''simple docstring'''


          snake_case :     Dict           =   parent
          snake_case :     str           =   batch_size
          snake_case :     Optional[Any]           =   seq_length
          snake_case :     List[str]           =   is_training
          snake_case :     List[Any]           =   use_input_mask
          snake_case :     Tuple           =   use_token_type_ids
          snake_case :     List[Any]           =   use_labels
          snake_case :     List[Any]           =   vocab_size
          snake_case :     List[str]           =   hidden_size
          snake_case :     Any           =   num_hidden_layers
          snake_case :     int           =   num_attention_heads
          snake_case :     Any           =   intermediate_size
          snake_case :     Optional[int]           =   hidden_act
          snake_case :     Optional[int]           =   hidden_dropout_prob
          snake_case :     Any           =   attention_probs_dropout_prob
          snake_case :     Optional[Any]           =   max_position_embeddings
          snake_case :     Optional[int]           =   initializer_range
          snake_case :     str           =   None
          snake_case :     Optional[int]           =   vocab_size - 1
          snake_case :     Tuple           =   vocab_size - 1
          snake_case :     int           =   vocab_size - 1



   def        _SCREAMING_SNAKE_CASE     (self	:      Dict				)	->			Optional[Any]:


          '''simple docstring'''


          snake_case :     Dict           =   ids_tensor([self.batch_size, self.seq_length]					,		self.vocab_size				)

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

          snake_case :     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=snake_case__					,		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        _SCREAMING_SNAKE_CASE     (self	:      int				)	->			List[str]:


          '''simple docstring'''


          snake_case :     str           =   self.prepare_config_and_inputs()
          snake_case       ,      snake_case       ,      snake_case :     List[str]           =   config_and_inputs
          snake_case :     Any           =   {"input_ids": input_ids, "attention_mask": attention_mask}
          return config, inputs_dict



   def        _SCREAMING_SNAKE_CASE     (self	:      str				)	->			List[str]:


          '''simple docstring'''


          snake_case       ,      snake_case       ,      snake_case :     Optional[int]           =   self.prepare_config_and_inputs()

          snake_case :     Any           =   floats_tensor([self.batch_size, self.seq_length, self.hidden_size]				)
          snake_case :     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        _SCREAMING_SNAKE_CASE     (self	:      Any					,		snake_case__	:      str					,		snake_case__	:      List[str]					,		snake_case__	:      Dict					,		snake_case__	:      List[Any]				)	->			Optional[Any]:


          '''simple docstring'''


          snake_case :     Union[str, Any]           =   20
          snake_case :     Any           =   model_class_name(snake_case__				)

          snake_case :     int           =   model.init_cache(input_ids.shape[0]					,		snake_case__				)
          snake_case :     Optional[Any]           =   jnp.ones((input_ids.shape[0], max_decoder_length)					,		dtype="i4"				)

          snake_case :     Optional[Any]           =   jnp.broadcast_to(
              jnp.arange(input_ids.shape[-1] - 1				)[None, :]					,		(input_ids.shape[0], input_ids.shape[-1] - 1)				)
          snake_case :     Tuple           =   model(
              input_ids[:, :-1]					,		attention_mask=snake_case__					,		past_key_values=snake_case__					,		position_ids=snake_case__					,		)

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

          snake_case :     int           =   model(snake_case__				)

          snake_case :     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        _SCREAMING_SNAKE_CASE     (self	:      int					,		snake_case__	:      List[Any]					,		snake_case__	:      Dict					,		snake_case__	:      List[Any]					,		snake_case__	:      Any				)	->			List[Any]:


          '''simple docstring'''


          snake_case :     Union[str, Any]           =   20
          snake_case :     List[str]           =   model_class_name(snake_case__				)

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

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

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

          snake_case :     Union[str, Any]           =   model(snake_case__					,		attention_mask=snake_case__				)

          snake_case :     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}"""				)






@require_flax
class 						UpperCAmelCase	(		A_ ,A_ ,unittest.TestCase				):
   A__      :	Tuple		  =  (FlaxGPTaModel, FlaxGPTaLMHeadModel) if is_flax_available() else ()
   A__      :	str		  =  (FlaxGPTaLMHeadModel,) if is_flax_available() else ()



   def        _SCREAMING_SNAKE_CASE     (self	:      Optional[int]				)	->			Any:


          '''simple docstring'''


          snake_case :     Optional[Any]           =   FlaxGPTaModelTester(self				)



   def        _SCREAMING_SNAKE_CASE     (self	:      Any				)	->			int:


          '''simple docstring'''


          for model_class_name in self.all_model_classes:
                 snake_case       ,      snake_case       ,      snake_case :     Any           =   self.model_tester.prepare_config_and_inputs()
                 self.model_tester.check_use_cache_forward(snake_case__					,		snake_case__					,		snake_case__					,		snake_case__				)



   def        _SCREAMING_SNAKE_CASE     (self	:      Optional[Any]				)	->			List[str]:


          '''simple docstring'''


          for model_class_name in self.all_model_classes:
                 snake_case       ,      snake_case       ,      snake_case :     Optional[Any]           =   self.model_tester.prepare_config_and_inputs()
                 self.model_tester.check_use_cache_forward_with_attn_mask(
                     snake_case__					,		snake_case__					,		snake_case__					,		snake_case__				)



   @slow
   def        _SCREAMING_SNAKE_CASE     (self	:      Union[str, Any]				)	->			int:


          '''simple docstring'''


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

          snake_case :     int           =   FlaxGPTaLMHeadModel.from_pretrained("gpt2"				)
          snake_case :     List[str]           =   False
          snake_case :     Optional[int]           =   model.config.eos_token_id

          snake_case :     List[str]           =   jax.jit(model.generate				)

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

          snake_case :     List[Any]           =   tokenizer.batch_decode(snake_case__					,		skip_special_tokens=snake_case__				)

          snake_case :     List[str]           =   [
              "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(snake_case__					,		snake_case__				)



   @is_pt_flax_cross_test
   def        _SCREAMING_SNAKE_CASE     (self	:      Tuple				)	->			List[Any]:


          '''simple docstring'''


          snake_case       ,      snake_case :     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
                        snake_case :     Dict           =   self._prepare_for_class(snake_case__					,		snake_case__				)
                        snake_case :     Tuple           =   {k: torch.tensor(v.tolist()				) for k, v in prepared_inputs_dict.items()}

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

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

                        snake_case :     Dict           =   convert_pytorch_state_dict_to_flax(pt_model.state_dict()					,		snake_case__				)
                        snake_case :     Optional[Any]           =   fx_state

                        with torch.no_grad():
                               snake_case :     Optional[int]           =   pt_model(**snake_case__				).to_tuple()

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

                        with tempfile.TemporaryDirectory() as tmpdirname:
                               pt_model.save_pretrained(snake_case__				)
                               snake_case :     str           =   model_class.from_pretrained(snake_case__					,		from_pt=snake_case__				)

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



   @is_pt_flax_cross_test
   def        _SCREAMING_SNAKE_CASE     (self	:      Union[str, Any]				)	->			int:


          '''simple docstring'''


          snake_case       ,      snake_case :     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
                        snake_case :     Optional[Any]           =   self._prepare_for_class(snake_case__					,		snake_case__				)
                        snake_case :     Union[str, Any]           =   {k: torch.tensor(v.tolist()				) for k, v in prepared_inputs_dict.items()}

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

                        snake_case :     Any           =   pt_model_class(snake_case__				).eval()
                        snake_case :     str           =   model_class(snake_case__					,		dtype=jnp.floataa				)

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

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

                        with torch.no_grad():
                               snake_case :     List[Any]           =   pt_model(**snake_case__				).to_tuple()

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

                        with tempfile.TemporaryDirectory() as tmpdirname:
                               fx_model.save_pretrained(snake_case__				)
                               snake_case :     Tuple           =   pt_model_class.from_pretrained(snake_case__					,		from_flax=snake_case__				)

                        with torch.no_grad():
                               snake_case :     Tuple           =   pt_model_loaded(**snake_case__				).to_tuple()

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



   @slow
   def        _SCREAMING_SNAKE_CASE     (self	:      Any				)	->			str:


          '''simple docstring'''


          for model_class_name in self.all_model_classes:
                 snake_case :     Any           =   model_class_name.from_pretrained("gpt2"					,		from_pt=snake_case__				)
                 snake_case :     List[str]           =   model(np.ones((1, 1)				)				)
                 self.assertIsNotNone(snake_case__				)







from transformers.models.cpm.tokenization_cpm import CpmTokenizer
from transformers.testing_utils import custom_tokenizers

from ..xlnet.test_modeling_xlnet import XLNetModelTest






@custom_tokenizers
class 						UpperCAmelCase	(		A_				):



   def        _SCREAMING_SNAKE_CASE     (self	:      List[str]					,		snake_case__	:      Dict					,		snake_case__	:      Tuple					,		snake_case__	:      Union[str, Any]					,		snake_case__	:      List[str]					,		snake_case__	:      List[Any]				)	->			Any:


          '''simple docstring'''


          return True



   def        _SCREAMING_SNAKE_CASE     (self	:      str				)	->			Dict:


          '''simple docstring'''


          snake_case :     Union[str, Any]           =   CpmTokenizer.from_pretrained("TsinghuaAI/CPM-Generate"				)
          snake_case :     str           =   "Hugging Face大法好，谁用谁知道。"
          snake_case :     Optional[Any]           =   "Hugging Face大法好,谁用谁知道。<unk>"
          snake_case :     int           =   "▁Hu gg ing ▁ ▂ ▁F ace ▁大法 ▁好 ▁ , ▁谁 ▁用 ▁谁 ▁知 道 ▁ 。".split()

          snake_case :     Union[str, Any]           =   tokenizer.tokenize(snake_case__				)
          self.assertListEqual(snake_case__					,		snake_case__				)

          snake_case :     Tuple           =   tokens + [tokenizer.unk_token]

          snake_case :     int           =   [1_37_89, 1_32_83, 14_21, 8, 10, 11_64, 1_36_08, 1_65_28, 63, 8, 9, 4_40, 1_08, 4_40, 1_21, 90, 8, 12, 0]
          self.assertListEqual(tokenizer.convert_tokens_to_ids(snake_case__				)					,		snake_case__				)

          snake_case :     Dict           =   tokenizer.decode(snake_case__				)
          self.assertEqual(snake_case__					,		snake_case__				)





