


import tempfile
import unittest

import numpy as np

import transformers
from transformers import GPTaTokenizer, GPTJConfig, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, tooslow

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.gptj.modeling_flax_gptj import FlaxGPTJForCausalLM, FlaxGPTJModel

if is_torch_available():
      import torch





class   SCREAMING_SNAKE_CASE_			:




       '''simple docstring'''



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



       def 				_lowerCAmelCase							(						self				:				str							)					->Optional[int]:
          lowerCamelCase_						:   Optional[Any]		= ids_tensor([self.batch_size, self.seq_length]		,				self.vocab_size							)

          lowerCamelCase_						:   Dict		= None
          if self.use_input_mask:
             lowerCamelCase_						:   List[str]		= random_attention_mask([self.batch_size, self.seq_length]							)

          lowerCamelCase_						:   Tuple		= GPTJConfig(
              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		,				rotary_dim=self.rotary_dim		,				)

          return (config, input_ids, input_mask)



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



       def 				_lowerCAmelCase							(						self				:				Any		,				__a				:				Any		,				__a				:				Tuple		,				__a				:				List[str]		,				__a				:				Union[str, Any]							)					->Optional[int]:
          lowerCamelCase_						:   List[Any]		= 20
          lowerCamelCase_						:   Tuple		= model_class_name(__a							)

          lowerCamelCase_						:   Tuple		= model.init_cache(input_ids.shape[0]		,				__a							)
          lowerCamelCase_						:   str		= jnp.ones((input_ids.shape[0], max_decoder_length)		,				dtype="""i4"""							)

          lowerCamelCase_						:   Dict		= jnp.broadcast_to(
              jnp.arange(input_ids.shape[-1] - 1							)[None, :]		,				(input_ids.shape[0], input_ids.shape[-1] - 1)							)
          lowerCamelCase_						:   Tuple		= model(
              input_ids[:, :-1]		,				attention_mask=__a		,				past_key_values=__a		,				position_ids=__a		,				)

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

          lowerCamelCase_						:   Union[str, Any]		= model(__a							)

          lowerCamelCase_						:   Optional[int]		= 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				:				Tuple		,				__a				:				str		,				__a				:				Optional[Any]		,				__a				:				str		,				__a				:				Optional[Any]							)					->Optional[Any]:
          lowerCamelCase_						:   Tuple		= 20
          lowerCamelCase_						:   List[Any]		= model_class_name(__a							)

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

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

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

          lowerCamelCase_						:   Union[str, Any]		= model(__a		,				attention_mask=__a							)

          lowerCamelCase_						:   Optional[int]		= 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_			(a__						,       a__						,       unittest.TestCase						):




       '''simple docstring'''



       _a   							=							(FlaxGPTJModel, FlaxGPTJForCausalLM) if is_flax_available() else ()
       _a   							=							(FlaxGPTJForCausalLM,) if is_flax_available() else ()



       def 				_lowerCAmelCase							(						self				:				Optional[int]							)					->List[str]:
          lowerCamelCase_						:   str		= FlaxGPTJModelTester(self							)



       def 				_lowerCAmelCase							(						self				:				Dict							)					->int:
          for model_class_name in self.all_model_classes:
             lowerCamelCase_,	lowerCamelCase_,	lowerCamelCase_						:   int		= self.model_tester.prepare_config_and_inputs()
             self.model_tester.check_use_cache_forward(__a		,				__a		,				__a		,				__a							)



       def 				_lowerCAmelCase							(						self				:				Tuple							)					->Tuple:
          for model_class_name in self.all_model_classes:
             lowerCamelCase_,	lowerCamelCase_,	lowerCamelCase_						:   List[Any]		= self.model_tester.prepare_config_and_inputs()
             self.model_tester.check_use_cache_forward_with_attn_mask(
                 __a		,				__a		,				__a		,				__a							)



       @tooslow
       def 				_lowerCAmelCase							(						self				:				Optional[int]							)					->Optional[Any]:
          lowerCamelCase_						:   List[str]		= GPTaTokenizer.from_pretrained("""gpt2"""		,				pad_token="""<|endoftext|>"""		,				padding_side="""left"""							)
          lowerCamelCase_						:   Optional[Any]		= tokenizer(["""Hello this is a long string""", """Hey"""]		,				return_tensors="""np"""		,				padding=__a		,				truncation=__a							)

          lowerCamelCase_						:   Any		= FlaxGPTJForCausalLM.from_pretrained("""EleutherAI/gpt-j-6B"""							)
          lowerCamelCase_						:   Optional[Any]		= False
          lowerCamelCase_						:   Tuple		= model.config.eos_token_id

          lowerCamelCase_						:   Union[str, Any]		= jax.jit(model.generate							)

          lowerCamelCase_						:   int		= jit_generate(
              inputs["""input_ids"""]		,				attention_mask=inputs["""attention_mask"""]		,				pad_token_id=tokenizer.pad_token_id							).sequences

          lowerCamelCase_						:   Optional[int]		= tokenizer.batch_decode(__a		,				skip_special_tokens=__a							)

          lowerCamelCase_						:   Optional[Any]		= [
              """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(__a		,				__a							)



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

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

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

                lowerCamelCase_						:   str		= convert_pytorch_state_dict_to_flax(pt_model.state_dict()		,				__a							)
                lowerCamelCase_						:   Tuple		= fx_state

                with torch.no_grad():
                   lowerCamelCase_						:   Union[str, Any]		= pt_model(**__a							).to_tuple()

                lowerCamelCase_						:   Dict		= 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							)
                   lowerCamelCase_						:   Dict		= model_class.from_pretrained(__a		,				from_pt=__a							)

                lowerCamelCase_						:   Any		= 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 				_lowerCAmelCase							(						self				:				Optional[int]							)					->int:
          lowerCamelCase_,	lowerCamelCase_						:   List[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
                lowerCamelCase_						:   Tuple		= self._prepare_for_class(__a		,				__a							)
                lowerCamelCase_						:   Any		= {k: torch.tensor(v.tolist()							) for k, v in prepared_inputs_dict.items()}

                # load corresponding PyTorch class
                lowerCamelCase_						:   int		= model_class.__name__[4:]  # Skip the "Flax" at the beginning
                lowerCamelCase_						:   str		= getattr(__a		,				__a							)

                lowerCamelCase_						:   Optional[Any]		= pt_model_class(__a							).eval()
                lowerCamelCase_						:   List[Any]		= model_class(__a		,				dtype=jnp.floataa							)

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

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

                with torch.no_grad():
                   lowerCamelCase_						:   Union[str, Any]		= pt_model(**__a							).to_tuple()

                lowerCamelCase_						:   List[Any]		= 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							)
                   lowerCamelCase_						:   str		= pt_model_class.from_pretrained(__a		,				from_flax=__a							)

                with torch.no_grad():
                   lowerCamelCase_						:   str		= 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							)




       @tooslow
       def 				_lowerCAmelCase							(						self				:				Dict							)					->Tuple:
          for model_class_name in self.all_model_classes:
             lowerCamelCase_						:   Dict		= model_class_name.from_pretrained("""EleutherAI/gpt-j-6B"""							)
             lowerCamelCase_						:   str		= model(np.ones((1, 1)							)							)
             self.assertIsNotNone(__a							)









class   SCREAMING_SNAKE_CASE_			:




       '''simple docstring'''



       def __init__(						self				:				Tuple		,				__a				:				int							)					->Optional[int]:
          lowerCamelCase_						:   Optional[Any]		= n
          lowerCamelCase_						:   Dict		= [None] * self.n
          lowerCamelCase_						:   int		= 0  # index of the first element
          lowerCamelCase_						:   str		= 0
          lowerCamelCase_						:   Optional[Any]		= 0



       def __len__(						self				:				List[str]							)					->int:
          return self.size



       def 				_lowerCAmelCase							(						self				:				str							)					->bool:
          return self.size == 0



       def 				_lowerCAmelCase							(						self				:				str							)					->Any:
          return False if self.is_empty() else self.array[self.front]



       def 				_lowerCAmelCase							(						self				:				Union[str, Any]		,				__a				:				int							)					->List[Any]:
          if self.size >= self.n:
             raise Exception("""QUEUE IS FULL"""							)

          lowerCamelCase_						:   Tuple		= data
          lowerCamelCase_						:   Tuple		= (self.rear + 1) % self.n
          self.size += 1
          return self




       def 				_lowerCAmelCase							(						self				:				Tuple							)					->int:
          if self.size == 0:
             raise Exception("""UNDERFLOW"""							)

          lowerCamelCase_						:   str		= self.array[self.front]
          lowerCamelCase_						:   str		= None
          lowerCamelCase_						:   Optional[Any]		= (self.front + 1) % self.n
          self.size -= 1
          return temp





