





import tempfile
import unittest

import numpy as np

import transformers
from transformers import GPTaTokenizer, GPTNeoConfig, 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, 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.gpt_neo.modeling_flax_gpt_neo import FlaxGPTNeoForCausalLM, FlaxGPTNeoModel

if is_torch_available():
				import torch







class 	_UpperCamelCase	:







					'''simple docstring'''

					def __init__(    self     :	List[Any]			,       __lowercase     :	Optional[Any]			,       __lowercase     :	Optional[int]=14			,       __lowercase     :	Optional[Any]=7			,       __lowercase     :	List[Any]=True			,       __lowercase     :	Optional[int]=True			,       __lowercase     :	Dict=False			,       __lowercase     :	Tuple=True			,       __lowercase     :	Optional[Any]=99			,       __lowercase     :	str=32			,       __lowercase     :	Tuple=4			,       __lowercase     :	Union[str, Any]=4			,       __lowercase     :	Dict=[[["global", "local"], 2]]			,       __lowercase     :	List[str]=37			,       __lowercase     :	Union[str, Any]="gelu"			,       __lowercase     :	int=0.1			,       __lowercase     :	int=0.1			,       __lowercase     :	List[str]=5_12			,       __lowercase     :	Optional[Any]=7			,       __lowercase     :	Any=0.02			,       ):






						'''simple docstring'''







						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_		    =  num_hidden_layers
						UpperCAmelCase_		    =  num_attention_heads
						UpperCAmelCase_		    =  attention_types
						UpperCAmelCase_		    =  intermediate_size
						UpperCAmelCase_		    =  hidden_act
						UpperCAmelCase_		    =  hidden_dropout_prob
						UpperCAmelCase_		    =  attention_probs_dropout_prob
						UpperCAmelCase_		    =  max_position_embeddings
						UpperCAmelCase_		    =  window_size
						UpperCAmelCase_		    =  initializer_range
						UpperCAmelCase_		    =  None
						UpperCAmelCase_		    =  vocab_size - 1
						UpperCAmelCase_		    =  vocab_size - 1
						UpperCAmelCase_		    =  vocab_size - 1





					def 							SCREAMING_SNAKE_CASE				(    self     :	Optional[Any]		):






						'''simple docstring'''







						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_		    =  GPTNeoConfig(
						    vocab_size=self.vocab_size			,       hidden_size=self.hidden_size			,       num_layers=self.num_hidden_layers			,       num_heads=self.num_attention_heads			,       max_position_embeddings=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			,       window_size=self.window_size			,       attention_types=self.attention_types			,       )

						return (config, input_ids, input_mask)





					def 							SCREAMING_SNAKE_CASE				(    self     :	Dict		):






						'''simple docstring'''







						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 							SCREAMING_SNAKE_CASE				(    self     :	List[Any]			,       __lowercase     :	Optional[int]			,       __lowercase     :	str			,       __lowercase     :	int			,       __lowercase     :	Union[str, Any]		):






						'''simple docstring'''







						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 							SCREAMING_SNAKE_CASE				(    self     :	Any			,       __lowercase     :	int			,       __lowercase     :	List[str]			,       __lowercase     :	Optional[Any]			,       __lowercase     :	Any		):






						'''simple docstring'''







						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 	_UpperCamelCase	(							A_    ,							A_    ,							unittest.TestCase	):







					'''simple docstring'''

					lowerCamelCase  :  int           =							(FlaxGPTNeoModel, FlaxGPTNeoForCausalLM) if is_flax_available() else ()
					lowerCamelCase  :  Dict           =							(FlaxGPTNeoForCausalLM,) if is_flax_available() else ()





					def 							SCREAMING_SNAKE_CASE				(    self     :	Optional[int]		):






						'''simple docstring'''







						UpperCAmelCase_		    =  FlaxGPTNeoModelTester(self		)





					def 							SCREAMING_SNAKE_CASE				(    self     :	Optional[Any]		):






						'''simple docstring'''







						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 							SCREAMING_SNAKE_CASE				(    self     :	int		):






						'''simple docstring'''







						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		)





					@slow
					def 							SCREAMING_SNAKE_CASE				(    self     :	Tuple		):






						'''simple docstring'''







						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_		    =  FlaxGPTNeoForCausalLM.from_pretrained("""EleutherAI/gpt-neo-125M"""		)
						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 							SCREAMING_SNAKE_CASE				(    self     :	Union[str, Any]		):






						'''simple docstring'''







						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 							SCREAMING_SNAKE_CASE				(    self     :	int		):






						'''simple docstring'''







						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		)





					@slow
					def 							SCREAMING_SNAKE_CASE				(    self     :	str		):






						'''simple docstring'''







						for model_class_name in self.all_model_classes:
							UpperCAmelCase_		    =  model_class_name.from_pretrained("""EleutherAI/gpt-neo-125M"""		)
							UpperCAmelCase_		    =  model(np.ones((1, 1)		)		)
							self.assertIsNotNone(__lowercase		)









import inspect
from typing import Callable, List, Optional, Set, Tuple, Union

import torch
from packaging import version
from safetensors.torch import storage_ptr, storage_size
from torch import nn

from .utils import logging


UpperCamelCase__ :			Optional[int]       	=			[nn.LayerNorm]

UpperCamelCase__ :			int       	=			logging.get_logger(__name__)

UpperCamelCase__ :			Dict       	=			version.parse(version.parse(torch.__version__).base_version)

UpperCamelCase__ :			str       	=			parsed_torch_version_base >= version.parse("""2.0""")
UpperCamelCase__ :			List[Any]       	=			parsed_torch_version_base >= version.parse("""1.12""")
UpperCamelCase__ :			List[Any]       	=			parsed_torch_version_base >= version.parse("""1.11""")
UpperCamelCase__ :			Any       	=			parsed_torch_version_base < version.parse("""1.11""")




def  A_(      A					,   A					,   A					,   A					,   self ):
	from torch import _softmax_backward_data

	if is_torch_less_than_1_11:
		return _softmax_backward_data(A					,   A					,   parent.dim					,   self )
	else:
		return _softmax_backward_data(A					,   A					,   parent.dim					,   self.dtype )




def  A_(      A					,   A					,   A = 0 ):
	UpperCAmelCase_		    =  index.to(layer.weight.device )
	UpperCAmelCase_		    =  layer.weight.index_select(A					,   A ).clone().detach()
	if layer.bias is not None:
		if dim == 1:
			UpperCAmelCase_		    =  layer.bias.clone().detach()
		else:
			UpperCAmelCase_		    =  layer.bias[index].clone().detach()
	UpperCAmelCase_		    =  list(layer.weight.size() )
	UpperCAmelCase_		    =  len(A )
	UpperCAmelCase_		    =  nn.Linear(new_size[1]					,   new_size[0]					,   bias=layer.bias is not None ).to(layer.weight.device )
	UpperCAmelCase_		    =  False
	new_layer.weight.copy_(W.contiguous() )
	UpperCAmelCase_		    =  True
	if layer.bias is not None:
		UpperCAmelCase_		    =  False
		new_layer.bias.copy_(b.contiguous() )
		UpperCAmelCase_		    =  True
	return new_layer







class 	_UpperCamelCase	(							nn.Module	):







					'''simple docstring'''

					def __init__(    self     :	Union[str, Any]			,       __lowercase     :	List[Any]			,       __lowercase     :	List[Any]		):






						'''simple docstring'''







						super().__init__()
						UpperCAmelCase_		    =  nf
						UpperCAmelCase_		    =  nn.Parameter(torch.empty(__lowercase			,       __lowercase		)		)
						UpperCAmelCase_		    =  nn.Parameter(torch.zeros(__lowercase		)		)
						nn.init.normal_(self.weight			,       std=0.02		)





					def 							SCREAMING_SNAKE_CASE				(    self     :	str			,       __lowercase     :	Tuple		):






						'''simple docstring'''







						UpperCAmelCase_		    =  x.size()[:-1] + (self.nf,)
						UpperCAmelCase_		    =  torch.addmm(self.bias			,       x.view(-1			,       x.size(-1		)		)			,       self.weight		)
						UpperCAmelCase_		    =  x.view(__lowercase		)
						return x




def  A_(      A					,   A					,   A = 1 ):
	UpperCAmelCase_		    =  index.to(layer.weight.device )
	UpperCAmelCase_		    =  layer.weight.index_select(A					,   A ).clone().detach()
	if dim == 0:
		UpperCAmelCase_		    =  layer.bias.clone().detach()
	else:
		UpperCAmelCase_		    =  layer.bias[index].clone().detach()
	UpperCAmelCase_		    =  list(layer.weight.size() )
	UpperCAmelCase_		    =  len(A )
	UpperCAmelCase_		    =  ConvaD(new_size[1]					,   new_size[0] ).to(layer.weight.device )
	UpperCAmelCase_		    =  False
	new_layer.weight.copy_(W.contiguous() )
	UpperCAmelCase_		    =  True
	UpperCAmelCase_		    =  False
	new_layer.bias.copy_(b.contiguous() )
	UpperCAmelCase_		    =  True
	return new_layer




def  A_(      A					,   A					,   A = None ):
	if isinstance(A					,   nn.Linear ):
		return prune_linear_layer(A					,   A					,   dim=0 if dim is None else dim )
	elif isinstance(A					,   A ):
		return prune_convad_layer(A					,   A					,   dim=1 if dim is None else dim )
	else:
		raise ValueError(f"""Can't prune layer of class {layer.__class__}""" )




def  A_(      A					,   A					,   A					,   *A ):
	assert len(A ) > 0, f"""{input_tensors} has to be a tuple/list of tensors"""

	# inspect.signature exist since python 3.5 and is a python method -> no problem with backward compatibility
	UpperCAmelCase_		    =  len(inspect.signature(A ).parameters )
	if num_args_in_forward_chunk_fn != len(A ):
		raise ValueError(
		    f"""forward_chunk_fn expects {num_args_in_forward_chunk_fn} arguments, but only {len(A )} input """
		    """tensors are given""" )

	if chunk_size > 0:
		UpperCAmelCase_		    =  input_tensors[0].shape[chunk_dim]
		for input_tensor in input_tensors:
			if input_tensor.shape[chunk_dim] != tensor_shape:
				raise ValueError(
				    f"""All input tenors have to be of the same shape: {tensor_shape}, """
				    f"""found shape {input_tensor.shape[chunk_dim]}""" )

		if input_tensors[0].shape[chunk_dim] % chunk_size != 0:
			raise ValueError(
			    f"""The dimension to be chunked {input_tensors[0].shape[chunk_dim]} has to be a multiple of the chunk """
			    f"""size {chunk_size}""" )

		UpperCAmelCase_		    =  input_tensors[0].shape[chunk_dim] // chunk_size

		# chunk input tensor into tuples
		UpperCAmelCase_		    =  tuple(input_tensor.chunk(A					,   dim=A ) for input_tensor in input_tensors )
		# apply forward fn to every tuple
		UpperCAmelCase_		    =  tuple(forward_fn(*A ) for input_tensors_chunk in zip(*A ) )
		# concatenate output at same dimension
		return torch.cat(A					,   dim=A )

	return forward_fn(*A )




def  A_(      A					,   A					,   A					,   A ):
	UpperCAmelCase_		    =  torch.ones(A					,   A )
	UpperCAmelCase_		    =  set(A ) - already_pruned_heads  # Convert to set and remove already pruned heads
	for head in heads:
		# Compute how many pruned heads are before the head and move the index accordingly
		UpperCAmelCase_		    =  head - sum(1 if h < head else 0 for h in already_pruned_heads )
		UpperCAmelCase_		    =  0
	UpperCAmelCase_		    =  mask.view(-1 ).contiguous().eq(1 )
	UpperCAmelCase_		    =  torch.arange(len(A ) )[mask].long()
	return heads, index




def  A_(      *A					,   A = None ):
	return torch.meshgrid(*A					,   indexing=A )




def  A_(      A ):
	return tensor.device, storage_ptr(A ), storage_size(A )


