




'''simple docstring'''







import tempfile
import unittest

import numpy as np

import transformers
from transformers import GPTaConfig, GPTaTokenizer, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, slow

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, 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.gpta.modeling_flax_gpta import FlaxGPTaLMHeadModel, FlaxGPTaModel

if is_torch_available():
						import torch





class      lowerCamelCase_							:




					'''simple docstring'''






					def __init__(    self,  __UpperCamelCase,  __UpperCamelCase=14,  __UpperCamelCase=7,  __UpperCamelCase=True,  __UpperCamelCase=True,  __UpperCamelCase=False,  __UpperCamelCase=True,  __UpperCamelCase=99,  __UpperCamelCase=32,  __UpperCamelCase=5,  __UpperCamelCase=4,  __UpperCamelCase=37,  __UpperCamelCase="gelu",  __UpperCamelCase=0.1,  __UpperCamelCase=0.1,  __UpperCamelCase=512,  __UpperCamelCase=0.02,  ):
							_snake_case	       =       parent
							_snake_case	       =       batch_size
							_snake_case	       =       seq_length
							_snake_case	       =       is_training
							_snake_case	       =       use_input_mask
							_snake_case	       =       use_token_type_ids
							_snake_case	       =       use_labels
							_snake_case	       =       vocab_size
							_snake_case	       =       hidden_size
							_snake_case	       =       num_hidden_layers
							_snake_case	       =       num_attention_heads
							_snake_case	       =       intermediate_size
							_snake_case	       =       hidden_act
							_snake_case	       =       hidden_dropout_prob
							_snake_case	       =       attention_probs_dropout_prob
							_snake_case	       =       max_position_embeddings
							_snake_case	       =       initializer_range
							_snake_case	       =       None
							_snake_case	       =       vocab_size - 1
							_snake_case	       =       vocab_size - 1
							_snake_case	       =       vocab_size - 1






					def 				__SCREAMING_SNAKE_CASE	(    self ):
							_snake_case	       =       ids_tensor([self.batch_size, self.seq_length],  self.vocab_size )

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

							_snake_case	       =       GPTaConfig(
							    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=_UpperCAmelCase,  bos_token_id=self.bos_token_id,  eos_token_id=self.eos_token_id,  pad_token_id=self.pad_token_id,  )

							return (config, input_ids, input_mask)






					def 				__SCREAMING_SNAKE_CASE	(    self ):
							_snake_case	       =       self.prepare_config_and_inputs()
							_snake_case	       =       config_and_inputs
							_snake_case	       =       {"""input_ids""": input_ids, """attention_mask""": attention_mask}
							return config, inputs_dict






					def 				__SCREAMING_SNAKE_CASE	(    self ):
							_snake_case	       =       self.prepare_config_and_inputs()

							_snake_case	       =       floats_tensor([self.batch_size, self.seq_length, self.hidden_size] )
							_snake_case	       =       ids_tensor([self.batch_size, self.seq_length],  vocab_size=2 )

							return (
							    config,
							    input_ids,
							    attention_mask,
							    encoder_hidden_states,
							    encoder_attention_mask,
							)






					def 				__SCREAMING_SNAKE_CASE	(    self,  __UpperCamelCase,  __UpperCamelCase,  __UpperCamelCase,  __UpperCamelCase ):
							_snake_case	       =       20
							_snake_case	       =       model_class_name(_UpperCAmelCase )

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

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

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

							_snake_case	       =       model(_UpperCAmelCase )

							_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 				__SCREAMING_SNAKE_CASE	(    self,  __UpperCamelCase,  __UpperCamelCase,  __UpperCamelCase,  __UpperCamelCase ):
							_snake_case	       =       20
							_snake_case	       =       model_class_name(_UpperCAmelCase )

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

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

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

							_snake_case	       =       model(_UpperCAmelCase,  attention_mask=_UpperCAmelCase )

							_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      lowerCamelCase_							(       _a,       _a,       unittest.TestCase							):




					'''simple docstring'''






					lowercase__    = (FlaxGPTaModel, FlaxGPTaLMHeadModel) if is_flax_available() else ()
					lowercase__    = (FlaxGPTaLMHeadModel,) if is_flax_available() else ()






					def 				__SCREAMING_SNAKE_CASE	(    self ):
							_snake_case	       =       FlaxGPTaModelTester(self )






					def 				__SCREAMING_SNAKE_CASE	(    self ):




							for model_class_name in self.all_model_classes:
									_snake_case	       =       self.model_tester.prepare_config_and_inputs()
									self.model_tester.check_use_cache_forward(_UpperCAmelCase,  _UpperCAmelCase,  _UpperCAmelCase,  _UpperCAmelCase )






					def 				__SCREAMING_SNAKE_CASE	(    self ):




							for model_class_name in self.all_model_classes:
									_snake_case	       =       self.model_tester.prepare_config_and_inputs()
									self.model_tester.check_use_cache_forward_with_attn_mask(
									    _UpperCAmelCase,  _UpperCAmelCase,  _UpperCAmelCase,  _UpperCAmelCase )






					@slow
					def 				__SCREAMING_SNAKE_CASE	(    self ):
							_snake_case	       =       GPTaTokenizer.from_pretrained("gpt2",  pad_token="</s>",  padding_side="left" )
							_snake_case	       =       tokenizer(["Hello this is a long string", "Hey"],  return_tensors="np",  padding=_UpperCAmelCase,  truncation=_UpperCAmelCase )

							_snake_case	       =       FlaxGPTaLMHeadModel.from_pretrained("gpt2" )
							_snake_case	       =       False
							_snake_case	       =       model.config.eos_token_id

							_snake_case	       =       jax.jit(model.generate )

							_snake_case	       =       jit_generate(inputs["input_ids"],  attention_mask=inputs["attention_mask"] ).sequences

							_snake_case	       =       tokenizer.batch_decode(_UpperCAmelCase,  skip_special_tokens=_UpperCAmelCase )

							_snake_case	       =       [
							    """Hello this is a long string of words. I'm going to start with the first one.\n""",
							    """Hey, I'm not sure if I'm going to be able to do""",
							]

							self.assertListEqual(_UpperCAmelCase,  _UpperCAmelCase )






					@is_pt_flax_cross_test
					def 				__SCREAMING_SNAKE_CASE	(    self ):
							_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
											_snake_case	       =       self._prepare_for_class(_UpperCAmelCase,  _UpperCAmelCase )
											_snake_case	       =       {k: torch.tensor(v.tolist() ) for k, v in prepared_inputs_dict.items()}

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

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

											_snake_case	       =       convert_pytorch_state_dict_to_flax(pt_model.state_dict(),  _UpperCAmelCase )
											_snake_case	       =       fx_state

											with torch.no_grad():
													_snake_case	       =       pt_model(**_UpperCAmelCase ).to_tuple()

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

											with tempfile.TemporaryDirectory() as tmpdirname:
													pt_model.save_pretrained(_UpperCAmelCase )
													_snake_case	       =       model_class.from_pretrained(_UpperCAmelCase,  from_pt=_UpperCAmelCase )

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






					@is_pt_flax_cross_test
					def 				__SCREAMING_SNAKE_CASE	(    self ):
							_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
											_snake_case	       =       self._prepare_for_class(_UpperCAmelCase,  _UpperCAmelCase )
											_snake_case	       =       {k: torch.tensor(v.tolist() ) for k, v in prepared_inputs_dict.items()}

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

											_snake_case	       =       pt_model_class(_UpperCAmelCase ).eval()
											_snake_case	       =       model_class(_UpperCAmelCase,  dtype=jnp.floataa )

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

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

											with torch.no_grad():
													_snake_case	       =       pt_model(**_UpperCAmelCase ).to_tuple()

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

											with tempfile.TemporaryDirectory() as tmpdirname:
													fx_model.save_pretrained(_UpperCAmelCase )
													_snake_case	       =       pt_model_class.from_pretrained(_UpperCAmelCase,  from_flax=_UpperCAmelCase )

											with torch.no_grad():
													_snake_case	       =       pt_model_loaded(**_UpperCAmelCase ).to_tuple()

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






					@slow
					def 				__SCREAMING_SNAKE_CASE	(    self ):




							for model_class_name in self.all_model_classes:
									_snake_case	       =       model_class_name.from_pretrained("gpt2",  from_pt=_UpperCAmelCase )
									_snake_case	       =       model(np.ones((1, 1) ) )
									self.assertIsNotNone(_UpperCAmelCase )











from collections import OrderedDict
from typing import Mapping

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


_lowerCamelCase					:     Optional[int]					  =  logging.get_logger(__name__)

_lowerCamelCase					:     Optional[int]					  =  {
    "squeezebert/squeezebert-uncased": (
        "https://huggingface.co/squeezebert/squeezebert-uncased/resolve/main/config.json"
    ),
    "squeezebert/squeezebert-mnli": "https://huggingface.co/squeezebert/squeezebert-mnli/resolve/main/config.json",
    "squeezebert/squeezebert-mnli-headless": (
        "https://huggingface.co/squeezebert/squeezebert-mnli-headless/resolve/main/config.json"
    ),
}
class 				__snake_case     (_a						):
				lowerCAmelCase__			=   SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP
				lowerCAmelCase__			=   "squeezebert"



				def __init__(					self					:						Optional[int]						,		_UpperCAmelCase					:						Tuple=3_0522						,		_UpperCAmelCase					:						str=768						,		_UpperCAmelCase					:						int=12						,		_UpperCAmelCase					:						Tuple=12						,		_UpperCAmelCase					:						Tuple=3072						,		_UpperCAmelCase					:						Union[str, Any]="gelu"						,		_UpperCAmelCase					:						Union[str, Any]=0.1						,		_UpperCAmelCase					:						List[Any]=0.1						,		_UpperCAmelCase					:						List[Any]=512						,		_UpperCAmelCase					:						Tuple=2						,		_UpperCAmelCase					:						Optional[Any]=0.02						,		_UpperCAmelCase					:						Any=1E-12						,		_UpperCAmelCase					:						Optional[Any]=0						,		_UpperCAmelCase					:						Union[str, Any]=768						,		_UpperCAmelCase					:						Any=4						,		_UpperCAmelCase					:						Any=4						,		_UpperCAmelCase					:						Optional[int]=4						,		_UpperCAmelCase					:						str=1						,		_UpperCAmelCase					:						Union[str, Any]=4						,		_UpperCAmelCase					:						List[str]=4						,		**_UpperCAmelCase					:						int						,		)     ->			Any:





								'''simple docstring'''




								super().__init__(pad_token_id=_UpperCAmelCase						,		**_UpperCAmelCase					)

								_lowerCAmelCase			:			Optional[Any]        =					vocab_size
								_lowerCAmelCase			:			Optional[Any]        =					hidden_size
								_lowerCAmelCase			:			Tuple        =					num_hidden_layers
								_lowerCAmelCase			:			int        =					num_attention_heads
								_lowerCAmelCase			:			List[Any]        =					hidden_act
								_lowerCAmelCase			:			List[Any]        =					intermediate_size
								_lowerCAmelCase			:			List[str]        =					hidden_dropout_prob
								_lowerCAmelCase			:			Any        =					attention_probs_dropout_prob
								_lowerCAmelCase			:			str        =					max_position_embeddings
								_lowerCAmelCase			:			int        =					type_vocab_size
								_lowerCAmelCase			:			str        =					initializer_range
								_lowerCAmelCase			:			int        =					layer_norm_eps
								_lowerCAmelCase			:			int        =					embedding_size
								_lowerCAmelCase			:			Optional[int]        =					q_groups
								_lowerCAmelCase			:			List[Any]        =					k_groups
								_lowerCAmelCase			:			List[Any]        =					v_groups
								_lowerCAmelCase			:			Dict        =					post_attention_groups
								_lowerCAmelCase			:			Optional[Any]        =					intermediate_groups
								_lowerCAmelCase			:			str        =					output_groups






class 				__snake_case     (_a						):



				@property
				def 					SCREAMING_SNAKE_CASE				(					self					:						Union[str, Any]					)     ->			Mapping[str, Mapping[int, str]]:





								'''simple docstring'''




								if self.task == "multiple-choice":
												_lowerCAmelCase			:			Optional[int]        =					{0: """batch""", 1: """choice""", 2: """sequence"""}
								else:
												_lowerCAmelCase			:			int        =					{0: """batch""", 1: """sequence"""}
								return OrderedDict(
								    [
								        ("""input_ids""", dynamic_axis),
								        ("""attention_mask""", dynamic_axis),
								        ("""token_type_ids""", dynamic_axis),
								    ]					)

