





'''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        __SCREAMING_SNAKE_CASE					:






							def __init__(						self  , __lowerCAmelCase  , __lowerCAmelCase=14  , __lowerCAmelCase=7  , __lowerCAmelCase=True  , __lowerCAmelCase=True  , __lowerCAmelCase=False  , __lowerCAmelCase=True  , __lowerCAmelCase=99  , __lowerCAmelCase=32  , __lowerCAmelCase=4  , __lowerCAmelCase=4  , __lowerCAmelCase=4  , __lowerCAmelCase=37  , __lowerCAmelCase="gelu"  , __lowerCAmelCase=0.1  , __lowerCAmelCase=0.1  , __lowerCAmelCase=512  , __lowerCAmelCase=0.02  , ):
														UpperCamelCase__				     =			parent
														UpperCamelCase__				     =			batch_size
														UpperCamelCase__				     =			seq_length
														UpperCamelCase__				     =			is_training
														UpperCamelCase__				     =			use_input_mask
														UpperCamelCase__				     =			use_token_type_ids
														UpperCamelCase__				     =			use_labels
														UpperCamelCase__				     =			vocab_size
														UpperCamelCase__				     =			hidden_size
														UpperCamelCase__				     =			rotary_dim
														UpperCamelCase__				     =			num_hidden_layers
														UpperCamelCase__				     =			num_attention_heads
														UpperCamelCase__				     =			intermediate_size
														UpperCamelCase__				     =			hidden_act
														UpperCamelCase__				     =			hidden_dropout_prob
														UpperCamelCase__				     =			attention_probs_dropout_prob
														UpperCamelCase__				     =			max_position_embeddings
														UpperCamelCase__				     =			initializer_range
														UpperCamelCase__				     =			None
														UpperCamelCase__				     =			vocab_size - 1
														UpperCamelCase__				     =			vocab_size - 1
														UpperCamelCase__				     =			vocab_size - 1






							def 				_lowerCamelCase		(						self  ):
														UpperCamelCase__				     =			ids_tensor([self.batch_size, self.seq_length]  , self.vocab_size  )

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

														UpperCamelCase__				     =			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 				_lowerCamelCase		(						self  ):
														UpperCamelCase__				     =			self.prepare_config_and_inputs()
														UpperCamelCase__     ,     UpperCamelCase__     ,     UpperCamelCase__				     =			config_and_inputs
														UpperCamelCase__				     =			{"""input_ids""": input_ids, """attention_mask""": attention_mask}
														return config, inputs_dict






							def 				_lowerCamelCase		(						self  , __lowerCAmelCase  , __lowerCAmelCase  , __lowerCAmelCase  , __lowerCAmelCase  ):
														UpperCamelCase__				     =			20
														UpperCamelCase__				     =			model_class_name(_lowercase  )

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

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

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

														UpperCamelCase__				     =			model(_lowercase  )

														UpperCamelCase__				     =			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  , __lowerCAmelCase  , __lowerCAmelCase  , __lowerCAmelCase  , __lowerCAmelCase  ):
														UpperCamelCase__				     =			20
														UpperCamelCase__				     =			model_class_name(_lowercase  )

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

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

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

														UpperCamelCase__				     =			model(_lowercase  , attention_mask=_lowercase  )

														UpperCamelCase__				     =			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					(     UpperCAmelCase_   ,		UpperCAmelCase_   ,		unittest.TestCase       ):
							snake_case     :      Optional[int]						     =       (FlaxGPTJModel, FlaxGPTJForCausalLM) if is_flax_available() else ()
							snake_case     :      Any						     =       (FlaxGPTJForCausalLM,) if is_flax_available() else ()






							def 				_lowerCamelCase		(						self  ):
														UpperCamelCase__				     =			FlaxGPTJModelTester(self  )






							def 				_lowerCamelCase		(						self  ):
														for model_class_name in self.all_model_classes:
																					UpperCamelCase__     ,     UpperCamelCase__     ,     UpperCamelCase__				     =			self.model_tester.prepare_config_and_inputs()
																					self.model_tester.check_use_cache_forward(_lowercase  , _lowercase  , _lowercase  , _lowercase  )






							def 				_lowerCamelCase		(						self  ):
														for model_class_name in self.all_model_classes:
																					UpperCamelCase__     ,     UpperCamelCase__     ,     UpperCamelCase__				     =			self.model_tester.prepare_config_and_inputs()
																					self.model_tester.check_use_cache_forward_with_attn_mask(
																					    _lowercase  , _lowercase  , _lowercase  , _lowercase  )






							@tooslow
							def 				_lowerCamelCase		(						self  ):
														UpperCamelCase__				     =			GPTaTokenizer.from_pretrained("""gpt2"""  , pad_token="""<|endoftext|>"""  , padding_side="""left"""  )
														UpperCamelCase__				     =			tokenizer(["""Hello this is a long string""", """Hey"""]  , return_tensors="""np"""  , padding=_lowercase  , truncation=_lowercase  )

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

														UpperCamelCase__				     =			jax.jit(model.generate  )

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

														UpperCamelCase__				     =			tokenizer.batch_decode(_lowercase  , skip_special_tokens=_lowercase  )

														UpperCamelCase__				     =			[
														    """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 				_lowerCamelCase		(						self  ):
														UpperCamelCase__     ,     UpperCamelCase__				     =			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
																												UpperCamelCase__				     =			self._prepare_for_class(_lowercase  , _lowercase  )
																												UpperCamelCase__				     =			{k: torch.tensor(v.tolist()  ) for k, v in prepared_inputs_dict.items()}

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

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

																												UpperCamelCase__				     =			convert_pytorch_state_dict_to_flax(pt_model.state_dict()  , _lowercase  )
																												UpperCamelCase__				     =			fx_state

																												with torch.no_grad():
																																			UpperCamelCase__				     =			pt_model(**_lowercase  ).to_tuple()

																												UpperCamelCase__				     =			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  )
																																			UpperCamelCase__				     =			model_class.from_pretrained(_lowercase  , from_pt=_lowercase  )

																												UpperCamelCase__				     =			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 				_lowerCamelCase		(						self  ):
														UpperCamelCase__     ,     UpperCamelCase__				     =			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
																												UpperCamelCase__				     =			self._prepare_for_class(_lowercase  , _lowercase  )
																												UpperCamelCase__				     =			{k: torch.tensor(v.tolist()  ) for k, v in prepared_inputs_dict.items()}

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

																												UpperCamelCase__				     =			pt_model_class(_lowercase  ).eval()
																												UpperCamelCase__				     =			model_class(_lowercase  , dtype=jnp.floataa  )

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

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

																												with torch.no_grad():
																																			UpperCamelCase__				     =			pt_model(**_lowercase  ).to_tuple()

																												UpperCamelCase__				     =			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  )
																																			UpperCamelCase__				     =			pt_model_class.from_pretrained(_lowercase  , from_flax=_lowercase  )

																												with torch.no_grad():
																																			UpperCamelCase__				     =			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 				_lowerCamelCase		(						self  ):
														for model_class_name in self.all_model_classes:
																					UpperCamelCase__				     =			model_class_name.from_pretrained("""EleutherAI/gpt-j-6B"""  )
																					UpperCamelCase__				     =			model(np.ones((1, 1)  )  )
																					self.assertIsNotNone(_lowercase  )











import math

from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCamelCase__											= logging.get_logger(__name__)

UpperCamelCase__											= {
    "facebook/data2vec-base-960h": "https://huggingface.co/facebook/data2vec-audio-base-960h/resolve/main/config.json",
    # See all Data2VecAudio models at https://huggingface.co/models?filter=data2vec-audio
}

class        __SCREAMING_SNAKE_CASE					(     _a       ):
							snake_case     :      Optional[Any]						     =       """data2vec-audio"""






							def __init__(						self  , __lowerCAmelCase=32  , __lowerCAmelCase=768  , __lowerCAmelCase=12  , __lowerCAmelCase=12  , __lowerCAmelCase=3072  , __lowerCAmelCase="gelu"  , __lowerCAmelCase=0.1  , __lowerCAmelCase=0.1  , __lowerCAmelCase=0.1  , __lowerCAmelCase=0.0  , __lowerCAmelCase=0.1  , __lowerCAmelCase=0.1  , __lowerCAmelCase=0.02  , __lowerCAmelCase=1E-5  , __lowerCAmelCase="gelu"  , __lowerCAmelCase=(512, 512, 512, 512, 512, 512, 512)  , __lowerCAmelCase=(5, 2, 2, 2, 2, 2, 2)  , __lowerCAmelCase=(10, 3, 3, 3, 3, 2, 2)  , __lowerCAmelCase=False  , __lowerCAmelCase=16  , __lowerCAmelCase=19  , __lowerCAmelCase=5  , __lowerCAmelCase=0.05  , __lowerCAmelCase=10  , __lowerCAmelCase=2  , __lowerCAmelCase=0.0  , __lowerCAmelCase=10  , __lowerCAmelCase=0  , __lowerCAmelCase="sum"  , __lowerCAmelCase=False  , __lowerCAmelCase=False  , __lowerCAmelCase=256  , __lowerCAmelCase=(512, 512, 512, 512, 1500)  , __lowerCAmelCase=(5, 3, 3, 1, 1)  , __lowerCAmelCase=(1, 2, 3, 1, 1)  , __lowerCAmelCase=512  , __lowerCAmelCase=0  , __lowerCAmelCase=1  , __lowerCAmelCase=2  , __lowerCAmelCase=False  , __lowerCAmelCase=3  , __lowerCAmelCase=2  , __lowerCAmelCase=3  , __lowerCAmelCase=None  , **__lowerCAmelCase  , ):
														super().__init__(**__lowerCAmelCase  , pad_token_id=__lowerCAmelCase  , bos_token_id=__lowerCAmelCase  , eos_token_id=__lowerCAmelCase  )
														UpperCamelCase__				     =			hidden_size
														UpperCamelCase__				     =			feat_extract_activation
														UpperCamelCase__				     =			list(__lowerCAmelCase  )
														UpperCamelCase__				     =			list(__lowerCAmelCase  )
														UpperCamelCase__				     =			list(__lowerCAmelCase  )
														UpperCamelCase__				     =			conv_bias
														UpperCamelCase__				     =			num_conv_pos_embeddings
														UpperCamelCase__				     =			num_conv_pos_embedding_groups
														UpperCamelCase__				     =			conv_pos_kernel_size
														UpperCamelCase__				     =			len(self.conv_dim  )
														UpperCamelCase__				     =			num_hidden_layers
														UpperCamelCase__				     =			intermediate_size
														UpperCamelCase__				     =			hidden_act
														UpperCamelCase__				     =			num_attention_heads
														UpperCamelCase__				     =			hidden_dropout
														UpperCamelCase__				     =			attention_dropout
														UpperCamelCase__				     =			activation_dropout
														UpperCamelCase__				     =			feat_proj_dropout
														UpperCamelCase__				     =			final_dropout
														UpperCamelCase__				     =			layerdrop
														UpperCamelCase__				     =			layer_norm_eps
														UpperCamelCase__				     =			initializer_range
														UpperCamelCase__				     =			vocab_size
														UpperCamelCase__				     =			use_weighted_layer_sum

														if (
														    (len(self.conv_stride  ) != self.num_feat_extract_layers)
														    or (len(self.conv_kernel  ) != self.num_feat_extract_layers)
														    or (len(self.conv_dim  ) != self.num_feat_extract_layers)
														):
																					raise ValueError(
																					    """Configuration for convolutional layers is incorrect. It is required that `len(config.conv_dim)` =="""
																					    """ `len(config.conv_stride)` == `len(config.conv_kernel)`, but is `len(config.conv_dim) ="""
																					    f""" {len(self.conv_dim  )}`, `len(config.conv_stride) = {len(self.conv_stride  )}`,"""
																					    f""" `len(config.conv_kernel) = {len(self.conv_kernel  )}`."""  )

														# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
														UpperCamelCase__				     =			mask_time_prob
														UpperCamelCase__				     =			mask_time_length
														UpperCamelCase__				     =			mask_time_min_masks
														UpperCamelCase__				     =			mask_feature_prob
														UpperCamelCase__				     =			mask_feature_length
														UpperCamelCase__				     =			mask_feature_min_masks

														# ctc loss
														UpperCamelCase__				     =			ctc_loss_reduction
														UpperCamelCase__				     =			ctc_zero_infinity

														# adapter
														UpperCamelCase__				     =			add_adapter
														UpperCamelCase__				     =			adapter_kernel_size
														UpperCamelCase__				     =			adapter_stride
														UpperCamelCase__				     =			num_adapter_layers
														UpperCamelCase__				     =			output_hidden_size or hidden_size

														# SequenceClassification-specific parameter. Feel free to ignore for other classes.
														UpperCamelCase__				     =			classifier_proj_size

														# XVector-specific parameters. Feel free to ignore for other classes.
														UpperCamelCase__				     =			list(__lowerCAmelCase  )
														UpperCamelCase__				     =			list(__lowerCAmelCase  )
														UpperCamelCase__				     =			list(__lowerCAmelCase  )
														UpperCamelCase__				     =			xvector_output_dim







							@property
							def 				_lowerCamelCase		(						self  ):
														return math.prod(self.conv_stride  )




