
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	:	Optional[int]   ,					snake_case__	:	int   ,					snake_case__	:	Dict=14   ,					snake_case__	:	List[str]=7   ,					snake_case__	:	Any=True   ,					snake_case__	:	Tuple=True   ,					snake_case__	:	str=False   ,					snake_case__	:	Dict=True   ,					snake_case__	:	List[str]=99   ,					snake_case__	:	List[Any]=32   ,					snake_case__	:	Optional[Any]=4   ,					snake_case__	:	Optional[Any]=4   ,					snake_case__	:	List[Any]=4   ,					snake_case__	:	Dict=37   ,					snake_case__	:	Tuple="gelu"   ,					snake_case__	:	List[str]=0.1   ,					snake_case__	:	List[str]=0.1   ,					snake_case__	:	str=512   ,					snake_case__	:	Dict=0.0_2   ,					):
								"""simple docstring"""







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







							def    UpperCAmelCase__       (    self	:	Optional[int]			):
								"""simple docstring"""







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

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

								__lowerCAmelCase    =				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    UpperCAmelCase__       (    self	:	Dict			):
								"""simple docstring"""







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







							def    UpperCAmelCase__       (    self	:	Any   ,					snake_case__	:	Any   ,					snake_case__	:	List[str]   ,					snake_case__	:	List[Any]   ,					snake_case__	:	Optional[int]			):
								"""simple docstring"""







								__lowerCAmelCase    =				20
								__lowerCAmelCase    =				model_class_name(lowerCamelCase_			)

								__lowerCAmelCase    =				model.init_cache(input_ids.shape[0]   ,					lowerCamelCase_			)
								__lowerCAmelCase    =				jnp.ones((input_ids.shape[0], max_decoder_length)   ,					dtype="i4"			)

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

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

								__lowerCAmelCase    =				model(lowerCamelCase_			)

								__lowerCAmelCase    =				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	:	Optional[int]   ,					snake_case__	:	Union[str, Any]   ,					snake_case__	:	List[Any]   ,					snake_case__	:	Dict   ,					snake_case__	:	int			):
								"""simple docstring"""







								__lowerCAmelCase    =				20
								__lowerCAmelCase    =				model_class_name(lowerCamelCase_			)

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

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

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

								__lowerCAmelCase    =				model(lowerCamelCase_   ,					attention_mask=lowerCamelCase_			)

								__lowerCAmelCase    =				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       ):
							lowercase_		:    Union[str, Any] 				=	(FlaxGPTJModel, FlaxGPTJForCausalLM) if is_flax_available() else ()
							lowercase_		:    Tuple 				=	(FlaxGPTJForCausalLM,) if is_flax_available() else ()







							def    UpperCAmelCase__       (    self	:	List[str]			):
								"""simple docstring"""







								__lowerCAmelCase    =				FlaxGPTJModelTester(self			)







							def    UpperCAmelCase__       (    self	:	List[str]			):
								"""simple docstring"""







								for model_class_name in self.all_model_classes:
									__lowerCAmelCase    =				self.model_tester.prepare_config_and_inputs()
									self.model_tester.check_use_cache_forward(lowerCamelCase_   ,					lowerCamelCase_   ,					lowerCamelCase_   ,					lowerCamelCase_			)







							def    UpperCAmelCase__       (    self	:	Union[str, Any]			):
								"""simple docstring"""







								for model_class_name in self.all_model_classes:
									__lowerCAmelCase    =				self.model_tester.prepare_config_and_inputs()
									self.model_tester.check_use_cache_forward_with_attn_mask(
									    lowerCamelCase_   ,					lowerCamelCase_   ,					lowerCamelCase_   ,					lowerCamelCase_			)







							@tooslow
							def    UpperCAmelCase__       (    self	:	Any			):
								"""simple docstring"""







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

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

								__lowerCAmelCase    =				jax.jit(model.generate			)

								__lowerCAmelCase    =				jit_generate(
								    inputs["input_ids"]   ,					attention_mask=inputs["attention_mask"]   ,					pad_token_id=tokenizer.pad_token_id			).sequences

								__lowerCAmelCase    =				tokenizer.batch_decode(lowerCamelCase_   ,					skip_special_tokens=lowerCamelCase_			)

								__lowerCAmelCase    =				[
								    '''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    UpperCAmelCase__       (    self	:	List[str]			):
								"""simple docstring"""







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

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

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

										__lowerCAmelCase    =				convert_pytorch_state_dict_to_flax(pt_model.state_dict()   ,					lowerCamelCase_			)
										__lowerCAmelCase    =				fx_state

										with torch.no_grad():
											__lowerCAmelCase    =				pt_model(**lowerCamelCase_			).to_tuple()

										__lowerCAmelCase    =				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_			)
											__lowerCAmelCase    =				model_class.from_pretrained(lowerCamelCase_   ,					from_pt=lowerCamelCase_			)

										__lowerCAmelCase    =				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    UpperCAmelCase__       (    self	:	List[str]			):
								"""simple docstring"""







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

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

										__lowerCAmelCase    =				pt_model_class(lowerCamelCase_			).eval()
										__lowerCAmelCase    =				model_class(lowerCamelCase_   ,					dtype=jnp.floataa			)

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

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

										with torch.no_grad():
											__lowerCAmelCase    =				pt_model(**lowerCamelCase_			).to_tuple()

										__lowerCAmelCase    =				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_			)
											__lowerCAmelCase    =				pt_model_class.from_pretrained(lowerCamelCase_   ,					from_flax=lowerCamelCase_			)

										with torch.no_grad():
											__lowerCAmelCase    =				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    UpperCAmelCase__       (    self	:	List[Any]			):
								"""simple docstring"""







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







'''simple docstring'''

from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


UpperCamelCase__  :						List[str]					=      logging.get_logger(__name__)

UpperCamelCase__  :						str					=      {
    'camembert-base': 'https://huggingface.co/camembert-base/resolve/main/config.json',
    'umberto-commoncrawl-cased-v1': (
        'https://huggingface.co/Musixmatch/umberto-commoncrawl-cased-v1/resolve/main/config.json'
    ),
    'umberto-wikipedia-uncased-v1': (
        'https://huggingface.co/Musixmatch/umberto-wikipedia-uncased-v1/resolve/main/config.json'
    ),
}


class 					_lowercase						(   lowerCAmelCase ):
      '''simple docstring'''


      UpperCAmelCase_   :   Dict   				= '''camembert'''




      def __init__(					self       ,lowerCamelCase_=30522       ,lowerCamelCase_=768       ,lowerCamelCase_=12       ,lowerCamelCase_=12       ,lowerCamelCase_=3072       ,lowerCamelCase_="gelu"       ,lowerCamelCase_=0.1       ,lowerCamelCase_=0.1       ,lowerCamelCase_=512       ,lowerCamelCase_=2       ,lowerCamelCase_=0.02       ,lowerCamelCase_=1e-12       ,lowerCamelCase_=1       ,lowerCamelCase_=0       ,lowerCamelCase_=2       ,lowerCamelCase_="absolute"       ,lowerCamelCase_=True       ,lowerCamelCase_=None       ,**lowerCamelCase_       ,)      ->      str:



         '''simple docstring'''



         super().__init__(pad_token_id=lowerCamelCase_       ,bos_token_id=lowerCamelCase_       ,eos_token_id=lowerCamelCase_       ,**lowerCamelCase_				)

         UpperCAmelCase__				:					int 					=	vocab_size
         UpperCAmelCase__				:					List[Any] 					=	hidden_size
         UpperCAmelCase__				:					Tuple 					=	num_hidden_layers
         UpperCAmelCase__				:					List[str] 					=	num_attention_heads
         UpperCAmelCase__				:					int 					=	hidden_act
         UpperCAmelCase__				:					Tuple 					=	intermediate_size
         UpperCAmelCase__				:					List[Any] 					=	hidden_dropout_prob
         UpperCAmelCase__				:					Tuple 					=	attention_probs_dropout_prob
         UpperCAmelCase__				:					str 					=	max_position_embeddings
         UpperCAmelCase__				:					Union[str, Any] 					=	type_vocab_size
         UpperCAmelCase__				:					int 					=	initializer_range
         UpperCAmelCase__				:					Optional[Any] 					=	layer_norm_eps
         UpperCAmelCase__				:					Optional[Any] 					=	position_embedding_type
         UpperCAmelCase__				:					str 					=	use_cache
         UpperCAmelCase__				:					List[Any] 					=	classifier_dropout




class 					_lowercase						(   lowerCAmelCase ):
      '''simple docstring'''


      @property
      def 		lowerCAmelCase__       (					self				)      ->      Mapping[str, Mapping[int, str]]:



         '''simple docstring'''



         if self.task == "multiple-choice":
            UpperCAmelCase__				:					Tuple 					=	{0: '''batch''', 1: '''choice''', 2: '''sequence'''}
         else:
            UpperCAmelCase__				:					Union[str, Any] 					=	{0: '''batch''', 1: '''sequence'''}
         return OrderedDict(
             [
                 ('''input_ids''', dynamic_axis),
                 ('''attention_mask''', dynamic_axis),
             ]				)




