'''simple docstring'''





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 				UpperCAmelCase :




    '''simple docstring'''







    def __init__(       self    ,							lowercase__    ,							lowercase__=14    ,							lowercase__=7    ,							lowercase__=True    ,							lowercase__=True    ,							lowercase__=False    ,							lowercase__=True    ,							lowercase__=99    ,							lowercase__=32    ,							lowercase__=4    ,							lowercase__=4    ,							lowercase__=4    ,							lowercase__=37    ,							lowercase__="gelu"    ,							lowercase__=0.1    ,							lowercase__=0.1    ,							lowercase__=512    ,							lowercase__=0.0_2    ,							)							-> str:
         SCREAMING_SNAKE_CASE					:			Any    	=       parent
         SCREAMING_SNAKE_CASE					:			Any    	=       batch_size
         SCREAMING_SNAKE_CASE					:			List[Any]    	=       seq_length
         SCREAMING_SNAKE_CASE					:			int    	=       is_training
         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       use_input_mask
         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       use_token_type_ids
         SCREAMING_SNAKE_CASE					:			str    	=       use_labels
         SCREAMING_SNAKE_CASE					:			Any    	=       vocab_size
         SCREAMING_SNAKE_CASE					:			Dict    	=       hidden_size
         SCREAMING_SNAKE_CASE					:			Tuple    	=       rotary_dim
         SCREAMING_SNAKE_CASE					:			int    	=       num_hidden_layers
         SCREAMING_SNAKE_CASE					:			List[str]    	=       num_attention_heads
         SCREAMING_SNAKE_CASE					:			List[Any]    	=       intermediate_size
         SCREAMING_SNAKE_CASE					:			List[str]    	=       hidden_act
         SCREAMING_SNAKE_CASE					:			int    	=       hidden_dropout_prob
         SCREAMING_SNAKE_CASE					:			str    	=       attention_probs_dropout_prob
         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       max_position_embeddings
         SCREAMING_SNAKE_CASE					:			List[str]    	=       initializer_range
         SCREAMING_SNAKE_CASE					:			List[Any]    	=       None
         SCREAMING_SNAKE_CASE					:			List[Any]    	=       vocab_size - 1
         SCREAMING_SNAKE_CASE					:			List[Any]    	=       vocab_size - 1
         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       vocab_size - 1






    def       _UpperCamelCase    (       self )							-> Dict:
         SCREAMING_SNAKE_CASE					:			str    	=       ids_tensor([self.batch_size, self.seq_length]    ,							self.vocab_size )

         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       None
         if self.use_input_mask:
              SCREAMING_SNAKE_CASE					:			List[str]    	=       random_attention_mask([self.batch_size, self.seq_length] )

         SCREAMING_SNAKE_CASE					:			int    	=       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=lowercase__    ,							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       _UpperCamelCase    (       self )							-> Any:
         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       self.prepare_config_and_inputs()
         SCREAMING_SNAKE_CASE					:			Tuple    	=       config_and_inputs
         SCREAMING_SNAKE_CASE					:			List[str]    	=       {'input_ids': input_ids, 'attention_mask': attention_mask}
         return config, inputs_dict






    def       _UpperCamelCase    (       self    ,							lowercase__    ,							lowercase__    ,							lowercase__    ,							lowercase__ )							-> str:
         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       20
         SCREAMING_SNAKE_CASE					:			int    	=       model_class_name(lowercase__ )

         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       model.init_cache(input_ids.shape[0]    ,							lowercase__ )
         SCREAMING_SNAKE_CASE					:			str    	=       jnp.ones((input_ids.shape[0], max_decoder_length)    ,							dtype='i4' )

         SCREAMING_SNAKE_CASE					:			Any    	=       jnp.broadcast_to(
             jnp.arange(input_ids.shape[-1] - 1 )[None, :]    ,							(input_ids.shape[0], input_ids.shape[-1] - 1) )
         SCREAMING_SNAKE_CASE					:			List[Any]    	=       model(
             input_ids[:, :-1]    ,							attention_mask=lowercase__    ,							past_key_values=lowercase__    ,							position_ids=lowercase__    ,							)

         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]    ,							dtype='i4' )
         SCREAMING_SNAKE_CASE					:			str    	=       model(
             input_ids[:, -1:]    ,							attention_mask=lowercase__    ,							past_key_values=outputs_cache.past_key_values    ,							position_ids=lowercase__    ,							)

         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       model(lowercase__ )

         SCREAMING_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       _UpperCamelCase    (       self    ,							lowercase__    ,							lowercase__    ,							lowercase__    ,							lowercase__ )							-> int:
         SCREAMING_SNAKE_CASE					:			List[str]    	=       20
         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       model_class_name(lowercase__ )

         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       jnp.concatenate(
             [attention_mask, jnp.zeros((attention_mask.shape[0], max_decoder_length - attention_mask.shape[1]) )]    ,							axis=-1    ,							)

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

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

         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       model(lowercase__    ,							attention_mask=lowercase__ )

         SCREAMING_SNAKE_CASE					:			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 				UpperCAmelCase (       _SCREAMING_SNAKE_CASE   ,    _SCREAMING_SNAKE_CASE   ,    unittest.TestCase ):




    '''simple docstring'''







    snake_case__							:   List[Any]							=						(FlaxGPTJModel, FlaxGPTJForCausalLM) if is_flax_available() else ()
    snake_case__							:   Any							=						(FlaxGPTJForCausalLM,) if is_flax_available() else ()






    def       _UpperCamelCase    (       self )							-> List[Any]:
         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       FlaxGPTJModelTester(self )






    def       _UpperCamelCase    (       self )							-> Union[str, Any]:
         for model_class_name in self.all_model_classes:
              SCREAMING_SNAKE_CASE					:			Optional[int]    	=       self.model_tester.prepare_config_and_inputs()
              self.model_tester.check_use_cache_forward(lowercase__    ,							lowercase__    ,							lowercase__    ,							lowercase__ )






    def       _UpperCamelCase    (       self )							-> str:
         for model_class_name in self.all_model_classes:
              SCREAMING_SNAKE_CASE					:			List[str]    	=       self.model_tester.prepare_config_and_inputs()
              self.model_tester.check_use_cache_forward_with_attn_mask(
                  lowercase__    ,							lowercase__    ,							lowercase__    ,							lowercase__ )






    @tooslow
    def       _UpperCamelCase    (       self )							-> Optional[Any]:
         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       GPTaTokenizer.from_pretrained('gpt2'    ,							pad_token='<|endoftext|>'    ,							padding_side='left' )
         SCREAMING_SNAKE_CASE					:			Tuple    	=       tokenizer(['Hello this is a long string', 'Hey']    ,							return_tensors='np'    ,							padding=lowercase__    ,							truncation=lowercase__ )

         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       FlaxGPTJForCausalLM.from_pretrained('EleutherAI/gpt-j-6B' )
         SCREAMING_SNAKE_CASE					:			str    	=       False
         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       model.config.eos_token_id

         SCREAMING_SNAKE_CASE					:			Any    	=       jax.jit(model.generate )

         SCREAMING_SNAKE_CASE					:			Any    	=       jit_generate(
             inputs['input_ids']    ,							attention_mask=inputs['attention_mask']    ,							pad_token_id=tokenizer.pad_token_id ).sequences

         SCREAMING_SNAKE_CASE					:			int    	=       tokenizer.batch_decode(lowercase__    ,							skip_special_tokens=lowercase__ )

         SCREAMING_SNAKE_CASE					:			str    	=       [
             '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(lowercase__    ,							lowercase__ )






    @is_pt_flax_cross_test
    def       _UpperCamelCase    (       self )							-> Any:
         SCREAMING_SNAKE_CASE					:			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
                   SCREAMING_SNAKE_CASE					:			Optional[int]    	=       self._prepare_for_class(lowercase__    ,							lowercase__ )
                   SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       {k: torch.tensor(v.tolist() ) for k, v in prepared_inputs_dict.items()}

                   # load corresponding PyTorch class
                   SCREAMING_SNAKE_CASE					:			Tuple    	=       model_class.__name__[4:]  # Skip the "Flax" at the beginning
                   SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       getattr(lowercase__    ,							lowercase__ )

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

                   SCREAMING_SNAKE_CASE					:			Dict    	=       convert_pytorch_state_dict_to_flax(pt_model.state_dict()    ,							lowercase__ )
                   SCREAMING_SNAKE_CASE					:			int    	=       fx_state

                   with torch.no_grad():
                        SCREAMING_SNAKE_CASE					:			List[Any]    	=       pt_model(**lowercase__ ).to_tuple()

                   SCREAMING_SNAKE_CASE					:			str    	=       fx_model(**lowercase__ ).to_tuple()
                   self.assertEqual(len(lowercase__ )    ,							len(lowercase__ )    ,							'Output lengths differ between Flax and PyTorch' )
                   for fx_output, pt_output in zip(lowercase__    ,							lowercase__ ):
                        self.assert_almost_equals(fx_output[:, -1]    ,							pt_output[:, -1].numpy()    ,							4E-2 )

                   with tempfile.TemporaryDirectory() as tmpdirname:
                        pt_model.save_pretrained(lowercase__ )
                        SCREAMING_SNAKE_CASE					:			List[Any]    	=       model_class.from_pretrained(lowercase__    ,							from_pt=lowercase__ )

                   SCREAMING_SNAKE_CASE					:			Optional[int]    	=       fx_model_loaded(**lowercase__ ).to_tuple()
                   self.assertEqual(
                       len(lowercase__ )    ,							len(lowercase__ )    ,							'Output lengths differ between Flax and PyTorch' )
                   for fx_output_loaded, pt_output in zip(lowercase__    ,							lowercase__ ):
                        self.assert_almost_equals(fx_output_loaded[:, -1]    ,							pt_output[:, -1].numpy()    ,							4E-2 )






    @is_pt_flax_cross_test
    def       _UpperCamelCase    (       self )							-> Any:
         SCREAMING_SNAKE_CASE					:			Optional[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
                   SCREAMING_SNAKE_CASE					:			Any    	=       self._prepare_for_class(lowercase__    ,							lowercase__ )
                   SCREAMING_SNAKE_CASE					:			str    	=       {k: torch.tensor(v.tolist() ) for k, v in prepared_inputs_dict.items()}

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

                   SCREAMING_SNAKE_CASE					:			int    	=       pt_model_class(lowercase__ ).eval()
                   SCREAMING_SNAKE_CASE					:			Dict    	=       model_class(lowercase__    ,							dtype=jnp.floataa )

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

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

                   with torch.no_grad():
                        SCREAMING_SNAKE_CASE					:			Optional[int]    	=       pt_model(**lowercase__ ).to_tuple()

                   SCREAMING_SNAKE_CASE					:			int    	=       fx_model(**lowercase__ ).to_tuple()
                   self.assertEqual(len(lowercase__ )    ,							len(lowercase__ )    ,							'Output lengths differ between Flax and PyTorch' )
                   for fx_output, pt_output in zip(lowercase__    ,							lowercase__ ):
                        self.assert_almost_equals(fx_output[:, -1]    ,							pt_output[:, -1].numpy()    ,							4E-2 )

                   with tempfile.TemporaryDirectory() as tmpdirname:
                        fx_model.save_pretrained(lowercase__ )
                        SCREAMING_SNAKE_CASE					:			Any    	=       pt_model_class.from_pretrained(lowercase__    ,							from_flax=lowercase__ )

                   with torch.no_grad():
                        SCREAMING_SNAKE_CASE					:			Tuple    	=       pt_model_loaded(**lowercase__ ).to_tuple()

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






    @tooslow
    def       _UpperCamelCase    (       self )							-> Union[str, Any]:
         for model_class_name in self.all_model_classes:
              SCREAMING_SNAKE_CASE					:			int    	=       model_class_name.from_pretrained('EleutherAI/gpt-j-6B' )
              SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       model(np.ones((1, 1) ) )
              self.assertIsNotNone(lowercase__ )




'''simple docstring'''





import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch


if is_torch_available():
 import torch

 from transformers.activations import gelu_new, gelu_python, get_activation






@require_torch
class 				UpperCAmelCase (       unittest.TestCase ):




    '''simple docstring'''







    def       _UpperCamelCase    (       self )							-> int:
         SCREAMING_SNAKE_CASE					:			str    	=       torch.tensor([-100, -1, -0.1, 0, 0.1, 1.0, 100] )
         SCREAMING_SNAKE_CASE					:			str    	=       get_activation('gelu' )
         self.assertTrue(torch.allclose(gelu_python(lowercase__ )    ,							torch_builtin(lowercase__ ) ) )
         self.assertFalse(torch.allclose(gelu_python(lowercase__ )    ,							gelu_new(lowercase__ ) ) )






    def       _UpperCamelCase    (       self )							-> Optional[Any]:
         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       torch.tensor([-100, -1, -0.1, 0, 0.1, 1.0, 100] )
         SCREAMING_SNAKE_CASE					:			str    	=       get_activation('gelu' )
         SCREAMING_SNAKE_CASE					:			Dict    	=       get_activation('gelu_10' )

         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       torch_builtin(lowercase__ )
         SCREAMING_SNAKE_CASE					:			str    	=       geluaa(lowercase__ )

         SCREAMING_SNAKE_CASE					:			List[Any]    	=       torch.where(y_gelu_aa < 1_0.0    ,							1    ,							0 )

         self.assertTrue(torch.max(lowercase__ ).item() == 1_0.0 )
         self.assertTrue(torch.allclose(y_gelu * clipped_mask    ,							y_gelu_aa * clipped_mask ) )






    def       _UpperCamelCase    (       self )							-> Optional[Any]:
         get_activation('gelu' )
         get_activation('gelu_10' )
         get_activation('gelu_fast' )
         get_activation('gelu_new' )
         get_activation('gelu_python' )
         get_activation('gelu_pytorch_tanh' )
         get_activation('linear' )
         get_activation('mish' )
         get_activation('quick_gelu' )
         get_activation('relu' )
         get_activation('sigmoid' )
         get_activation('silu' )
         get_activation('swish' )
         get_activation('tanh' )
         with self.assertRaises(lowercase__ ):
              get_activation('bogus' )
         with self.assertRaises(lowercase__ ):
              get_activation(lowercase__ )






    def       _UpperCamelCase    (       self )							-> int:
         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       get_activation('gelu' )
         SCREAMING_SNAKE_CASE					:			str    	=       1
         SCREAMING_SNAKE_CASE					:			List[str]    	=       get_activation('gelu' )
         self.assertEqual(acta.a    ,							1 )
         with self.assertRaises(lowercase__ ):
              SCREAMING_SNAKE_CASE					:			Dict    	=       acta.a



