





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 					__snake_case	:


    '''simple docstring'''







    def __init__(       self   ,				A_   ,				A_=14   ,				A_=7   ,				A_=True   ,				A_=True   ,				A_=False   ,				A_=True   ,				A_=99   ,				A_=32   ,				A_=4   ,				A_=4   ,				A_=4   ,				A_=37   ,				A_="gelu"   ,				A_=0.1   ,				A_=0.1   ,				A_=5_12   ,				A_=0.02   ,				):





        '''simple docstring'''

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



    def  lowercase_					(       self     ):





        '''simple docstring'''

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

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

        SCREAMING_SNAKE_CASE__											=			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  lowercase_					(       self     ):





        '''simple docstring'''

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



    def  lowercase_					(       self   ,				A_   ,				A_   ,				A_   ,				A_     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			20
        SCREAMING_SNAKE_CASE__											=			model_class_name(A_     )

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

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

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

        SCREAMING_SNAKE_CASE__											=			model(A_     )

        SCREAMING_SNAKE_CASE__											=			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  lowercase_					(       self   ,				A_   ,				A_   ,				A_   ,				A_     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			20
        SCREAMING_SNAKE_CASE__											=			model_class_name(A_     )

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

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

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

        SCREAMING_SNAKE_CASE__											=			model(A_   ,				attention_mask=A_     )

        SCREAMING_SNAKE_CASE__											=			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 					__snake_case	(  __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     unittest.TestCase    ):


    '''simple docstring'''







    lowerCamelCase__    :		Optional[int]				=	(FlaxGPTJModel, FlaxGPTJForCausalLM) if is_flax_available() else ()
    lowerCamelCase__    :		Tuple				=	(FlaxGPTJForCausalLM,) if is_flax_available() else ()



    def  lowercase_					(       self     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			FlaxGPTJModelTester(self     )



    def  lowercase_					(       self     ):





        '''simple docstring'''

        for model_class_name in self.all_model_classes:
            SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__											=			self.model_tester.prepare_config_and_inputs()
            self.model_tester.check_use_cache_forward(A_   ,				A_   ,				A_   ,				A_     )



    def  lowercase_					(       self     ):





        '''simple docstring'''

        for model_class_name in self.all_model_classes:
            SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__											=			self.model_tester.prepare_config_and_inputs()
            self.model_tester.check_use_cache_forward_with_attn_mask(
                A_   ,				A_   ,				A_   ,				A_     )



    @tooslow
    def  lowercase_					(       self     ):





        '''simple docstring'''

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

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

        SCREAMING_SNAKE_CASE__											=			jax.jit(model.generate     )

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

        SCREAMING_SNAKE_CASE__											=			tokenizer.batch_decode(A_   ,				skip_special_tokens=A_     )

        SCREAMING_SNAKE_CASE__											=			[
            '''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  lowercase_					(       self     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__											=			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__											=			self._prepare_for_class(A_   ,				A_     )
                SCREAMING_SNAKE_CASE__											=			{k: torch.tensor(v.tolist()     ) for k, v in prepared_inputs_dict.items()}

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

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

                SCREAMING_SNAKE_CASE__											=			convert_pytorch_state_dict_to_flax(pt_model.state_dict()   ,				A_     )
                SCREAMING_SNAKE_CASE__											=			fx_state

                with torch.no_grad():
                    SCREAMING_SNAKE_CASE__											=			pt_model(**A_     ).to_tuple()

                SCREAMING_SNAKE_CASE__											=			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_     )
                    SCREAMING_SNAKE_CASE__											=			model_class.from_pretrained(A_   ,				from_pt=A_     )

                SCREAMING_SNAKE_CASE__											=			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  lowercase_					(       self     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__											=			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__											=			self._prepare_for_class(A_   ,				A_     )
                SCREAMING_SNAKE_CASE__											=			{k: torch.tensor(v.tolist()     ) for k, v in prepared_inputs_dict.items()}

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

                SCREAMING_SNAKE_CASE__											=			pt_model_class(A_     ).eval()
                SCREAMING_SNAKE_CASE__											=			model_class(A_   ,				dtype=jnp.floataa     )

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

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

                with torch.no_grad():
                    SCREAMING_SNAKE_CASE__											=			pt_model(**A_     ).to_tuple()

                SCREAMING_SNAKE_CASE__											=			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_     )
                    SCREAMING_SNAKE_CASE__											=			pt_model_class.from_pretrained(A_   ,				from_flax=A_     )

                with torch.no_grad():
                    SCREAMING_SNAKE_CASE__											=			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  lowercase_					(       self     ):





        '''simple docstring'''

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



"""simple docstring"""





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__UpperCamelCase    : List[str]				 =						{'''configuration_xglm''': ['''XGLM_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''XGLMConfig''']}

try:
							if not is_sentencepiece_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__UpperCamelCase    : Union[str, Any]				 =						['''XGLMTokenizer''']

try:
							if not is_tokenizers_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__UpperCamelCase    : str				 =						['''XGLMTokenizerFast''']

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__UpperCamelCase    : Optional[int]				 =						[
							    '''XGLM_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''XGLMForCausalLM''',
							    '''XGLMModel''',
							    '''XGLMPreTrainedModel''',
							]


try:
							if not is_flax_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__UpperCamelCase    : str				 =						[
							    '''FlaxXGLMForCausalLM''',
							    '''FlaxXGLMModel''',
							    '''FlaxXGLMPreTrainedModel''',
							]


try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__UpperCamelCase    : str				 =						[
							    '''TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''TFXGLMForCausalLM''',
							    '''TFXGLMModel''',
							    '''TFXGLMPreTrainedModel''',
							]


if TYPE_CHECKING:
							from .configuration_xglm import XGLM_PRETRAINED_CONFIG_ARCHIVE_MAP, XGLMConfig

							try:
														if not is_sentencepiece_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_xglm import XGLMTokenizer

							try:
														if not is_tokenizers_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_xglm_fast import XGLMTokenizerFast

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_xglm import XGLM_PRETRAINED_MODEL_ARCHIVE_LIST, XGLMForCausalLM, XGLMModel, XGLMPreTrainedModel

							try:
														if not is_flax_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_flax_xglm import FlaxXGLMForCausalLM, FlaxXGLMModel, FlaxXGLMPreTrainedModel

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_xglm import (
														    TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST,
														    TFXGLMForCausalLM,
														    TFXGLMModel,
														    TFXGLMPreTrainedModel,
														)


else:
							import sys

							__UpperCamelCase    : Optional[int]				 =						_LazyModule(__name__, globals()['''__file__'''], _import_structure)




