




'''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      A       :







							def __init__(    self			:    Union[str, Any]				,     lowerCAmelCase_			:    Any				,     lowerCAmelCase_			:    Tuple=14				,     lowerCAmelCase_			:    str=7				,     lowerCAmelCase_			:    Optional[Any]=True				,     lowerCAmelCase_			:    Dict=True				,     lowerCAmelCase_			:    Any=False				,     lowerCAmelCase_			:    List[str]=True				,     lowerCAmelCase_			:    List[str]=99				,     lowerCAmelCase_			:    Tuple=32				,     lowerCAmelCase_			:    Optional[int]=4				,     lowerCAmelCase_			:    Optional[Any]=4				,     lowerCAmelCase_			:    Union[str, Any]=4				,     lowerCAmelCase_			:    Optional[int]=37				,     lowerCAmelCase_			:    List[Any]="gelu"				,     lowerCAmelCase_			:    List[str]=0.1				,     lowerCAmelCase_			:    str=0.1				,     lowerCAmelCase_			:    Any=5_12				,     lowerCAmelCase_			:    Union[str, Any]=0.0_2				,     )     ->				Dict:


														"""simple docstring"""


														_a      	=							parent
														_a      	=							batch_size
														_a      	=							seq_length
														_a      	=							is_training
														_a      	=							use_input_mask
														_a      	=							use_token_type_ids
														_a      	=							use_labels
														_a      	=							vocab_size
														_a      	=							hidden_size
														_a      	=							rotary_dim
														_a      	=							num_hidden_layers
														_a      	=							num_attention_heads
														_a      	=							intermediate_size
														_a      	=							hidden_act
														_a      	=							hidden_dropout_prob
														_a      	=							attention_probs_dropout_prob
														_a      	=							max_position_embeddings
														_a      	=							initializer_range
														_a      	=							None
														_a      	=							vocab_size - 1
														_a      	=							vocab_size - 1
														_a      	=							vocab_size - 1







							def   __lowerCAmelCase     (    self			:    str   )     ->				str:


														"""simple docstring"""


														_a      	=							ids_tensor([self.batch_size, self.seq_length]				,     self.vocab_size   )

														_a      	=							None
														if self.use_input_mask:
																					_a      	=							random_attention_mask([self.batch_size, self.seq_length]   )

														_a      	=							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=lowerCAmelCase_				,     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[Any]   )     ->				Optional[Any]:


														"""simple docstring"""


														_a      	=							self.prepare_config_and_inputs()
														_a	,				_a	,				_a      	=							config_and_inputs
														_a      	=							{'''input_ids''': input_ids, '''attention_mask''': attention_mask}
														return config, inputs_dict







							def   __lowerCAmelCase     (    self			:    Optional[Any]				,     lowerCAmelCase_			:    Optional[int]				,     lowerCAmelCase_			:    Tuple				,     lowerCAmelCase_			:    Any				,     lowerCAmelCase_			:    Any   )     ->				Dict:


														"""simple docstring"""


														_a      	=							20
														_a      	=							model_class_name(lowerCAmelCase_   )

														_a      	=							model.init_cache(input_ids.shape[0]				,     lowerCAmelCase_   )
														_a      	=							jnp.ones((input_ids.shape[0], max_decoder_length)				,     dtype='''i4'''   )

														_a      	=							jnp.broadcast_to(
														    jnp.arange(input_ids.shape[-1] - 1   )[None, :]				,     (input_ids.shape[0], input_ids.shape[-1] - 1)   )
														_a      	=							model(
														    input_ids[:, :-1]				,     attention_mask=lowerCAmelCase_				,     past_key_values=lowerCAmelCase_				,     position_ids=lowerCAmelCase_				,     )

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

														_a      	=							model(lowerCAmelCase_   )

														_a      	=							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			:    int				,     lowerCAmelCase_			:    str				,     lowerCAmelCase_			:    Dict				,     lowerCAmelCase_			:    List[Any]				,     lowerCAmelCase_			:    List[str]   )     ->				List[Any]:


														"""simple docstring"""


														_a      	=							20
														_a      	=							model_class_name(lowerCAmelCase_   )

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

														_a      	=							model.init_cache(input_ids.shape[0]				,     lowerCAmelCase_   )
														_a      	=							jnp.broadcast_to(
														    jnp.arange(input_ids.shape[-1] - 1   )[None, :]				,     (input_ids.shape[0], input_ids.shape[-1] - 1)   )

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

														_a      	=							model(lowerCAmelCase_				,     attention_mask=lowerCAmelCase_   )

														_a      	=							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       (						_a	,_a	,unittest.TestCase       ):
							lowercase_							     =							(FlaxGPTJModel, FlaxGPTJForCausalLM) if is_flax_available() else ()
							lowercase_							     =							(FlaxGPTJForCausalLM,) if is_flax_available() else ()







							def   __lowerCAmelCase     (    self			:    Any   )     ->				int:


														"""simple docstring"""


														_a      	=							FlaxGPTJModelTester(self   )







							def   __lowerCAmelCase     (    self			:    int   )     ->				List[str]:


														"""simple docstring"""


														for model_class_name in self.all_model_classes:
																					_a	,				_a	,				_a      	=							self.model_tester.prepare_config_and_inputs()
																					self.model_tester.check_use_cache_forward(lowerCAmelCase_				,     lowerCAmelCase_				,     lowerCAmelCase_				,     lowerCAmelCase_   )







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


														"""simple docstring"""


														for model_class_name in self.all_model_classes:
																					_a	,				_a	,				_a      	=							self.model_tester.prepare_config_and_inputs()
																					self.model_tester.check_use_cache_forward_with_attn_mask(
																					    lowerCAmelCase_				,     lowerCAmelCase_				,     lowerCAmelCase_				,     lowerCAmelCase_   )







							@tooslow
							def   __lowerCAmelCase     (    self			:    Optional[int]   )     ->				Tuple:


														"""simple docstring"""


														_a      	=							GPTaTokenizer.from_pretrained('''gpt2'''				,     pad_token='''<|endoftext|>'''				,     padding_side='''left'''   )
														_a      	=							tokenizer(['''Hello this is a long string''', '''Hey''']				,     return_tensors='''np'''				,     padding=lowerCAmelCase_				,     truncation=lowerCAmelCase_   )

														_a      	=							FlaxGPTJForCausalLM.from_pretrained('''EleutherAI/gpt-j-6B'''   )
														_a      	=							False
														_a      	=							model.config.eos_token_id

														_a      	=							jax.jit(model.generate   )

														_a      	=							jit_generate(
														    inputs['''input_ids''']				,     attention_mask=inputs['''attention_mask''']				,     pad_token_id=tokenizer.pad_token_id   ).sequences

														_a      	=							tokenizer.batch_decode(lowerCAmelCase_				,     skip_special_tokens=lowerCAmelCase_   )

														_a      	=							[
														    '''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[int]   )     ->				Optional[int]:


														"""simple docstring"""


														_a	,				_a      	=							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
																												_a      	=							self._prepare_for_class(lowerCAmelCase_				,     lowerCAmelCase_   )
																												_a      	=							{k: torch.tensor(v.tolist()   ) for k, v in prepared_inputs_dict.items()}

																												# load corresponding PyTorch class
																												_a      	=							model_class.__name__[4:]  # Skip the "Flax" at the beginning
																												_a      	=							getattr(lowerCAmelCase_				,     lowerCAmelCase_   )

																												_a	,				_a      	=							pt_inputs['''input_ids'''].shape
																												_a      	=							np.random.randint(0				,     seq_length - 1				,     size=(batch_size,)   )
																												for batch_idx, start_index in enumerate(lowerCAmelCase_   ):
																																			_a      	=							0
																																			_a      	=							1
																																			_a      	=							0
																																			_a      	=							1
																												_a      	=							pt_model_class(lowerCAmelCase_   ).eval()
																												_a      	=							model_class(lowerCAmelCase_				,     dtype=jnp.floataa   )

																												_a      	=							convert_pytorch_state_dict_to_flax(pt_model.state_dict()				,     lowerCAmelCase_   )
																												_a      	=							fx_state

																												with torch.no_grad():
																																			_a      	=							pt_model(**lowerCAmelCase_   ).to_tuple()

																												_a      	=							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_   )
																																			_a      	=							model_class.from_pretrained(lowerCAmelCase_				,     from_pt=lowerCAmelCase_   )

																												_a      	=							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			:    Optional[int]   )     ->				int:


														"""simple docstring"""


														_a	,				_a      	=							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
																												_a      	=							self._prepare_for_class(lowerCAmelCase_				,     lowerCAmelCase_   )
																												_a      	=							{k: torch.tensor(v.tolist()   ) for k, v in prepared_inputs_dict.items()}

																												# load corresponding PyTorch class
																												_a      	=							model_class.__name__[4:]  # Skip the "Flax" at the beginning
																												_a      	=							getattr(lowerCAmelCase_				,     lowerCAmelCase_   )

																												_a      	=							pt_model_class(lowerCAmelCase_   ).eval()
																												_a      	=							model_class(lowerCAmelCase_				,     dtype=jnp.floataa   )

																												_a      	=							load_flax_weights_in_pytorch_model(lowerCAmelCase_				,     fx_model.params   )
																												_a	,				_a      	=							pt_inputs['''input_ids'''].shape
																												_a      	=							np.random.randint(0				,     seq_length - 1				,     size=(batch_size,)   )
																												for batch_idx, start_index in enumerate(lowerCAmelCase_   ):
																																			_a      	=							0
																																			_a      	=							1
																																			_a      	=							0
																																			_a      	=							1

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

																												with torch.no_grad():
																																			_a      	=							pt_model(**lowerCAmelCase_   ).to_tuple()

																												_a      	=							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_   )
																																			_a      	=							pt_model_class.from_pretrained(lowerCAmelCase_				,     from_flax=lowerCAmelCase_   )

																												with torch.no_grad():
																																			_a      	=							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   )







							@tooslow
							def   __lowerCAmelCase     (    self			:    Optional[int]   )     ->				List[str]:


														"""simple docstring"""


														for model_class_name in self.all_model_classes:
																					_a      	=							model_class_name.from_pretrained('''EleutherAI/gpt-j-6B'''   )
																					_a      	=							model(np.ones((1, 1)   )   )
																					self.assertIsNotNone(lowerCAmelCase_   )






'''simple docstring'''





import json
import os
import unittest

from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin


@require_tokenizers
class 					snake_case			(UpperCamelCase		,   unittest.TestCase ):
       lowerCAmelCase__	:str     =       MgpstrTokenizer
       lowerCAmelCase__	:Tuple     =       False
       lowerCAmelCase__	:Any     =       {}
       lowerCAmelCase__	:List[Any]     =       False



       def 				_a   (					self   )	->				Union[str, Any]:
           super().setUp()

           # fmt: off
           lowercase__									=    ["[GO]", "[s]", "0", "1", "2", "3", "4", "5", "6", "7", "8", "9", "a", "b", "c", "d", "e", "f", "g", "h", "i", "j", "k", "l", "m", "n", "o", "p", "q", "r", "s", "t", "u", "v", "w", "x", "y", "z"]
           # fmt: on
           lowercase__									=    dict(zip(UpperCAmelCase_  ,range(len(UpperCAmelCase_   )   )   )   )

           lowercase__									=    os.path.join(self.tmpdirname  ,VOCAB_FILES_NAMES["vocab_file"]   )
           with open(self.vocab_file  ,"w"  ,encoding="utf-8"   ) as fp:
               fp.write(json.dumps(UpperCAmelCase_   ) + "\n"   )



       def 				_a   (					self  ,**UpperCAmelCase_   )	->				Any:
           return MgpstrTokenizer.from_pretrained(self.tmpdirname  ,**UpperCAmelCase_   )



       def 				_a   (					self  ,UpperCAmelCase_   )	->				Dict:
           lowercase__									=    "tester"
           lowercase__									=    "tester"
           return input_text, output_text



       @unittest.skip("MGP-STR always lower cases letters."   )
       def 				_a   (					self   )	->				Dict:
           pass



       def 				_a   (					self   )	->				Optional[Any]:
           lowercase__									=    self.get_tokenizers(do_lower_case=UpperCAmelCase_   )
           for tokenizer in tokenizers:
               with self.subTest(F'''{tokenizer.__class__.__name__}'''   ):
                   lowercase__									=    "[SPECIAL_TOKEN]"

                   tokenizer.add_special_tokens({"cls_token": special_token}   )
                   lowercase__									=    tokenizer.encode([special_token]  ,add_special_tokens=UpperCAmelCase_   )
                   self.assertEqual(len(UpperCAmelCase_   )  ,1   )

                   lowercase__									=    tokenizer.decode(UpperCAmelCase_  ,skip_special_tokens=UpperCAmelCase_   )
                   self.assertTrue(special_token not in decoded   )



       def 				_a   (					self   )	->				str:
           lowercase__									=    self.get_tokenizers()
           for tokenizer in tokenizers:
               with self.subTest(F'''{tokenizer.__class__.__name__}'''   ):
                   lowercase__							,							lowercase__									=    self.get_input_output_texts(UpperCAmelCase_   )

                   lowercase__									=    tokenizer.tokenize(UpperCAmelCase_   )
                   lowercase__									=    tokenizer.convert_tokens_to_ids(UpperCAmelCase_   )
                   lowercase__									=    tokenizer.encode(UpperCAmelCase_  ,add_special_tokens=UpperCAmelCase_   )
                   self.assertListEqual(UpperCAmelCase_  ,UpperCAmelCase_   )

                   lowercase__									=    tokenizer.convert_ids_to_tokens(UpperCAmelCase_   )
                   self.assertNotEqual(len(UpperCAmelCase_   )  ,0   )
                   lowercase__									=    tokenizer.decode(UpperCAmelCase_   )
                   self.assertIsInstance(UpperCAmelCase_  ,UpperCAmelCase_   )

                   self.assertEqual(text_a.replace(" "  ,""   )  ,UpperCAmelCase_   )



       @unittest.skip("MGP-STR tokenizer only handles one sequence."   )
       def 				_a   (					self   )	->				Optional[int]:
           pass







       @unittest.skip("inputs cannot be pretokenized in MgpstrTokenizer"   )
       def 				_a   (					self   )	->				Any:
           pass




