





"""simple docstring"""





import argparse
import json
from collections import OrderedDict

import torch
from huggingface_hub import cached_download, hf_hub_url

from transformers import AutoImageProcessor, CvtConfig, CvtForImageClassification


def       a__					(					_SCREAMING_SNAKE_CASE    ):




		"""simple docstring"""


		UpperCamelCase   =	[]
		embed.append(
		    (
		        F"cvt.encoder.stages.{idx}.embedding.convolution_embeddings.projection.weight",
		        F"stage{idx}.patch_embed.proj.weight",
		    )    )
		embed.append(
		    (
		        F"cvt.encoder.stages.{idx}.embedding.convolution_embeddings.projection.bias",
		        F"stage{idx}.patch_embed.proj.bias",
		    )    )
		embed.append(
		    (
		        F"cvt.encoder.stages.{idx}.embedding.convolution_embeddings.normalization.weight",
		        F"stage{idx}.patch_embed.norm.weight",
		    )    )
		embed.append(
		    (
		        F"cvt.encoder.stages.{idx}.embedding.convolution_embeddings.normalization.bias",
		        F"stage{idx}.patch_embed.norm.bias",
		    )    )
		return embed


def       a__					(					_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE    ):




		"""simple docstring"""


		UpperCamelCase   =	[]
		attention_weights.append(
		    (
		        F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.convolution.weight",
		        F"stage{idx}.blocks.{cnt}.attn.conv_proj_q.conv.weight",
		    )    )
		attention_weights.append(
		    (
		        F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.weight",
		        F"stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.weight",
		    )    )
		attention_weights.append(
		    (
		        F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.bias",
		        F"stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.bias",
		    )    )
		attention_weights.append(
		    (
		        F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.running_mean",
		        F"stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.running_mean",
		    )    )
		attention_weights.append(
		    (
		        F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.running_var",
		        F"stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.running_var",
		    )    )
		attention_weights.append(
		    (
		        F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.num_batches_tracked",
		        F"stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.num_batches_tracked",
		    )    )
		attention_weights.append(
		    (
		        F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.convolution.weight",
		        F"stage{idx}.blocks.{cnt}.attn.conv_proj_k.conv.weight",
		    )    )
		attention_weights.append(
		    (
		        F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.weight",
		        F"stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.weight",
		    )    )
		attention_weights.append(
		    (
		        F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.bias",
		        F"stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.bias",
		    )    )
		attention_weights.append(
		    (
		        F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.running_mean",
		        F"stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.running_mean",
		    )    )
		attention_weights.append(
		    (
		        F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.running_var",
		        F"stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.running_var",
		    )    )
		attention_weights.append(
		    (
		        F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.num_batches_tracked",
		        F"stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.num_batches_tracked",
		    )    )
		attention_weights.append(
		    (
		        F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.convolution.weight",
		        F"stage{idx}.blocks.{cnt}.attn.conv_proj_v.conv.weight",
		    )    )
		attention_weights.append(
		    (
		        F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.weight",
		        F"stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.weight",
		    )    )
		attention_weights.append(
		    (
		        F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.bias",
		        F"stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.bias",
		    )    )
		attention_weights.append(
		    (
		        F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.running_mean",
		        F"stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.running_mean",
		    )    )
		attention_weights.append(
		    (
		        F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.running_var",
		        F"stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.running_var",
		    )    )
		attention_weights.append(
		    (
		        F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.num_batches_tracked",
		        F"stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.num_batches_tracked",
		    )    )
		attention_weights.append(
		    (
		        F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_query.weight",
		        F"stage{idx}.blocks.{cnt}.attn.proj_q.weight",
		    )    )
		attention_weights.append(
		    (
		        F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_query.bias",
		        F"stage{idx}.blocks.{cnt}.attn.proj_q.bias",
		    )    )
		attention_weights.append(
		    (
		        F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_key.weight",
		        F"stage{idx}.blocks.{cnt}.attn.proj_k.weight",
		    )    )
		attention_weights.append(
		    (
		        F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_key.bias",
		        F"stage{idx}.blocks.{cnt}.attn.proj_k.bias",
		    )    )
		attention_weights.append(
		    (
		        F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_value.weight",
		        F"stage{idx}.blocks.{cnt}.attn.proj_v.weight",
		    )    )
		attention_weights.append(
		    (
		        F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_value.bias",
		        F"stage{idx}.blocks.{cnt}.attn.proj_v.bias",
		    )    )
		attention_weights.append(
		    (
		        F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.output.dense.weight",
		        F"stage{idx}.blocks.{cnt}.attn.proj.weight",
		    )    )
		attention_weights.append(
		    (
		        F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.output.dense.bias",
		        F"stage{idx}.blocks.{cnt}.attn.proj.bias",
		    )    )
		attention_weights.append(
		    (F"cvt.encoder.stages.{idx}.layers.{cnt}.intermediate.dense.weight", F"stage{idx}.blocks.{cnt}.mlp.fc1.weight")    )
		attention_weights.append(
		    (F"cvt.encoder.stages.{idx}.layers.{cnt}.intermediate.dense.bias", F"stage{idx}.blocks.{cnt}.mlp.fc1.bias")    )
		attention_weights.append(
		    (F"cvt.encoder.stages.{idx}.layers.{cnt}.output.dense.weight", F"stage{idx}.blocks.{cnt}.mlp.fc2.weight")    )
		attention_weights.append(
		    (F"cvt.encoder.stages.{idx}.layers.{cnt}.output.dense.bias", F"stage{idx}.blocks.{cnt}.mlp.fc2.bias")    )
		attention_weights.append(
		    (F"cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_before.weight", F"stage{idx}.blocks.{cnt}.norm1.weight")    )
		attention_weights.append(
		    (F"cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_before.bias", F"stage{idx}.blocks.{cnt}.norm1.bias")    )
		attention_weights.append(
		    (F"cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_after.weight", F"stage{idx}.blocks.{cnt}.norm2.weight")    )
		attention_weights.append(
		    (F"cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_after.bias", F"stage{idx}.blocks.{cnt}.norm2.bias")    )
		return attention_weights


def       a__					(					_SCREAMING_SNAKE_CASE    ):




		"""simple docstring"""


		UpperCamelCase   =	[]
		token.append((F"cvt.encoder.stages.{idx}.cls_token", "stage2.cls_token")    )
		return token


def       a__					(					):




		"""simple docstring"""


		UpperCamelCase   =	[]
		head.append(("layernorm.weight", "norm.weight")    )
		head.append(("layernorm.bias", "norm.bias")    )
		head.append(("classifier.weight", "head.weight")    )
		head.append(("classifier.bias", "head.bias")    )
		return head


def       a__					(					_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE    ):




		"""simple docstring"""


		UpperCamelCase   =	"imagenet-1k-id2label.json"
		UpperCamelCase   =	1_000

		UpperCamelCase   =	"huggingface/label-files"
		UpperCamelCase   =	num_labels
		UpperCamelCase   =	json.load(open(cached_download(hf_hub_url(_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	repo_type="dataset"    )    )  ,	"r"    )    )
		UpperCamelCase   =	{int(_SCREAMING_SNAKE_CASE    ): v for k, v in idalabel.items()}

		UpperCamelCase   =	idalabel
		UpperCamelCase   =	{v: k for k, v in idalabel.items()}

		UpperCamelCase   =	UpperCamelCase   =	CvtConfig(num_labels=_SCREAMING_SNAKE_CASE  ,	idalabel=_SCREAMING_SNAKE_CASE  ,	labelaid=_SCREAMING_SNAKE_CASE    )

		# For depth size 13 (13 = 1+2+10)
		if cvt_model.rsplit("/"  ,	1    )[-1][4:6] == "13":
				UpperCamelCase   =	[1, 2, 10]

		# For depth size 21 (21 = 1+4+16)
		elif cvt_model.rsplit("/"  ,	1    )[-1][4:6] == "21":
				UpperCamelCase   =	[1, 4, 16]

		# For wide cvt (similar to wide-resnet) depth size 24 (w24 = 2 + 2 20)
		else:
				UpperCamelCase   =	[2, 2, 20]
				UpperCamelCase   =	[3, 12, 16]
				UpperCamelCase   =	[192, 768, 1_024]

		UpperCamelCase   =	CvtForImageClassification(_SCREAMING_SNAKE_CASE    )
		UpperCamelCase   =	AutoImageProcessor.from_pretrained("facebook/convnext-base-224-22k-1k"    )
		UpperCamelCase   =	image_size
		UpperCamelCase   =	torch.load(_SCREAMING_SNAKE_CASE  ,	map_location=torch.device("cpu"    )    )

		UpperCamelCase   =	OrderedDict()
		UpperCamelCase   =	[]

		for idx in range(len(config.depth    )    ):
				if config.cls_token[idx]:
						UpperCamelCase   =	list_of_state_dict + cls_token(_SCREAMING_SNAKE_CASE    )
				UpperCamelCase   =	list_of_state_dict + embeddings(_SCREAMING_SNAKE_CASE    )
				for cnt in range(config.depth[idx]    ):
						UpperCamelCase   =	list_of_state_dict + attention(_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE    )

		UpperCamelCase   =	list_of_state_dict + final()
		for gg in list_of_state_dict:
				print(_SCREAMING_SNAKE_CASE    )
		for i in range(len(_SCREAMING_SNAKE_CASE    )    ):
				UpperCamelCase   =	original_weights[list_of_state_dict[i][1]]

		model.load_state_dict(_SCREAMING_SNAKE_CASE    )
		model.save_pretrained(_SCREAMING_SNAKE_CASE    )
		image_processor.save_pretrained(_SCREAMING_SNAKE_CASE    )


# Download the weights from zoo: https://1drv.ms/u/s!AhIXJn_J-blW9RzF3rMW7SsLHa8h?e=blQ0Al

if __name__ == "__main__":
						lowerCAmelCase__     			=			argparse.ArgumentParser()
						parser.add_argument(
						    '''--cvt_model''',
						    default='''cvt-w24''',
						    type=str,
						    help='''Name of the cvt model you\'d like to convert.''',
						)
						parser.add_argument(
						    '''--image_size''',
						    default=384,
						    type=int,
						    help='''Input Image Size''',
						)
						parser.add_argument(
						    '''--cvt_file_name''',
						    default=r'''cvtmodels\CvT-w24-384x384-IN-22k.pth''',
						    type=str,
						    help='''Input Image Size''',
						)
						parser.add_argument(
						    '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model directory.'''
						)

						lowerCAmelCase__     			=			parser.parse_args()
						convert_cvt_checkpoint(args.cvt_model, args.image_size, args.cvt_file_name, args.pytorch_dump_folder_path)













"""simple docstring"""





import unittest

from parameterized import parameterized

from transformers import AutoTokenizer, GPTNeoXConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
						import torch

						from transformers import (
						    GPTNeoXForCausalLM,
						    GPTNeoXForQuestionAnswering,
						    GPTNeoXForSequenceClassification,
						    GPTNeoXForTokenClassification,
						    GPTNeoXModel,
						)





class        _lowerCamelCase      :


						def __init__(self   ,							__a   ,							__a=13   ,							__a=7   ,							__a=True   ,							__a=True   ,							__a=True   ,							__a=True   ,							__a=99   ,							__a=64   ,							__a=5   ,							__a=4   ,							__a=37   ,							__a="gelu"   ,							__a=0.1   ,							__a=0.1   ,							__a=5_12   ,							__a=16   ,							__a=2   ,							__a=0.02   ,							__a=3   ,							__a=4   ,							__a=None   ,							) ->   Optional[int]:
								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   =	intermediate_size
								UpperCamelCase   =	hidden_act
								UpperCamelCase   =	hidden_dropout_prob
								UpperCamelCase   =	attention_probs_dropout_prob
								UpperCamelCase   =	max_position_embeddings
								UpperCamelCase   =	type_vocab_size
								UpperCamelCase   =	type_sequence_label_size
								UpperCamelCase   =	initializer_range
								UpperCamelCase   =	num_labels
								UpperCamelCase   =	num_choices
								UpperCamelCase   =	scope
								UpperCamelCase   =	vocab_size - 1


						def     snake_case_   (self		) ->   List[Any]:
								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   =	None
								if self.use_labels:
										UpperCamelCase   =	ids_tensor([self.batch_size, self.seq_length]   ,							self.num_labels		)

								UpperCamelCase   =	self.get_config()

								return config, input_ids, input_mask, token_labels


						def     snake_case_   (self		) ->   int:
								return GPTNeoXConfig(
								    vocab_size=self.vocab_size   ,							hidden_size=self.hidden_size   ,							num_hidden_layers=self.num_hidden_layers   ,							num_attention_heads=self.num_attention_heads   ,							intermediate_size=self.intermediate_size   ,							hidden_act=self.hidden_act   ,							hidden_dropout_prob=self.hidden_dropout_prob   ,							attention_probs_dropout_prob=self.attention_probs_dropout_prob   ,							max_position_embeddings=self.max_position_embeddings   ,							type_vocab_size=self.type_vocab_size   ,							is_decoder=__a   ,							initializer_range=self.initializer_range   ,							pad_token_id=self.pad_token_id   ,							)


						def     snake_case_   (self		) ->   int:
								UpperCamelCase   ,				UpperCamelCase   ,				UpperCamelCase   ,				UpperCamelCase   =	self.prepare_config_and_inputs()

								UpperCamelCase   =	True

								return config, input_ids, input_mask, token_labels


						def     snake_case_   (self   ,							__a   ,							__a   ,							__a		) ->   Optional[int]:
								UpperCamelCase   =	GPTNeoXModel(config=__a		)
								model.to(__a		)
								model.eval()
								UpperCamelCase   =	model(__a   ,							attention_mask=__a		)
								UpperCamelCase   =	model(__a		)
								self.parent.assertEqual(result.last_hidden_state.shape   ,							(self.batch_size, self.seq_length, self.hidden_size)		)


						def     snake_case_   (self   ,							__a   ,							__a   ,							__a		) ->   Optional[int]:
								UpperCamelCase   =	True
								UpperCamelCase   =	GPTNeoXModel(__a		)
								model.to(__a		)
								model.eval()
								UpperCamelCase   =	model(__a   ,							attention_mask=__a		)
								self.parent.assertEqual(result.last_hidden_state.shape   ,							(self.batch_size, self.seq_length, self.hidden_size)		)


						def     snake_case_   (self   ,							__a   ,							__a   ,							__a   ,							__a		) ->   List[Any]:
								UpperCamelCase   =	GPTNeoXForCausalLM(config=__a		)
								model.to(__a		)
								model.eval()
								UpperCamelCase   =	model(__a   ,							attention_mask=__a   ,							labels=__a		)
								self.parent.assertEqual(result.logits.shape   ,							(self.batch_size, self.seq_length, self.vocab_size)		)


						def     snake_case_   (self   ,							__a   ,							__a   ,							__a   ,							__a		) ->   Optional[int]:
								UpperCamelCase   =	self.num_labels
								UpperCamelCase   =	GPTNeoXForQuestionAnswering(__a		)
								model.to(__a		)
								model.eval()
								UpperCamelCase   =	model(__a   ,							attention_mask=__a		)
								self.parent.assertEqual(result.start_logits.shape   ,							(self.batch_size, self.seq_length)		)
								self.parent.assertEqual(result.end_logits.shape   ,							(self.batch_size, self.seq_length)		)


						def     snake_case_   (self   ,							__a   ,							__a   ,							__a   ,							__a		) ->   Optional[int]:
								UpperCamelCase   =	self.num_labels
								UpperCamelCase   =	GPTNeoXForSequenceClassification(__a		)
								model.to(__a		)
								model.eval()
								UpperCamelCase   =	ids_tensor([self.batch_size]   ,							self.type_sequence_label_size		)
								UpperCamelCase   =	model(__a   ,							attention_mask=__a   ,							labels=__a		)
								self.parent.assertEqual(result.logits.shape   ,							(self.batch_size, self.num_labels)		)


						def     snake_case_   (self   ,							__a   ,							__a   ,							__a   ,							__a		) ->   str:
								UpperCamelCase   =	self.num_labels
								UpperCamelCase   =	GPTNeoXForTokenClassification(__a		)
								model.to(__a		)
								model.eval()
								UpperCamelCase   =	model(__a   ,							attention_mask=__a   ,							labels=__a		)
								self.parent.assertEqual(result.logits.shape   ,							(self.batch_size, self.seq_length, self.num_labels)		)


						def     snake_case_   (self   ,							__a   ,							__a   ,							__a		) ->   Any:
								UpperCamelCase   =	True
								UpperCamelCase   =	GPTNeoXForCausalLM(config=__a		)
								model.to(__a		)
								model.eval()

								# first forward pass
								UpperCamelCase   =	model(__a   ,							attention_mask=__a   ,							use_cache=__a		)
								UpperCamelCase   =	outputs.past_key_values

								# create hypothetical multiple next token and extent to next_input_ids
								UpperCamelCase   =	ids_tensor((self.batch_size, 3)   ,							config.vocab_size		)
								UpperCamelCase   =	ids_tensor((self.batch_size, 3)   ,							vocab_size=2		)

								# append to next input_ids and
								UpperCamelCase   =	torch.cat([input_ids, next_tokens]   ,							dim=-1		)
								UpperCamelCase   =	torch.cat([input_mask, next_mask]   ,							dim=-1		)

								UpperCamelCase   =	model(__a   ,							attention_mask=__a   ,							output_hidden_states=__a		)
								UpperCamelCase   =	output_from_no_past["hidden_states"][0]
								UpperCamelCase   =	model(
								    __a   ,							attention_mask=__a   ,							past_key_values=__a   ,							output_hidden_states=__a   ,							)["hidden_states"][0]

								# select random slice
								UpperCamelCase   =	ids_tensor((1,)   ,							output_from_past.shape[-1]		).item()
								UpperCamelCase   =	output_from_no_past[:, -3:, random_slice_idx].detach()
								UpperCamelCase   =	output_from_past[:, :, random_slice_idx].detach()

								self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1]		)

								# test that outputs are equal for slice
								self.parent.assertTrue(torch.allclose(__a   ,							__a   ,							atol=1e-3		)		)




						def     snake_case_   (self		) ->   str:
								UpperCamelCase   =	self.prepare_config_and_inputs()
								UpperCamelCase   ,				UpperCamelCase   ,				UpperCamelCase   ,				UpperCamelCase   =	config_and_inputs
								UpperCamelCase   =	{"input_ids": input_ids, "attention_mask": input_mask}
								return config, inputs_dict





@require_torch
class        _lowerCamelCase      (       _lowercase  ,			_lowercase  ,			_lowercase  ,			unittest.TestCase ):
						UpperCAmelCase_ =			(
						    (
						        GPTNeoXModel,
						        GPTNeoXForCausalLM,
						        GPTNeoXForQuestionAnswering,
						        GPTNeoXForSequenceClassification,
						        GPTNeoXForTokenClassification,
						    )
						    if is_torch_available()
						    else ()
						)
						UpperCAmelCase_ =			(GPTNeoXForCausalLM,) if is_torch_available() else ()
						UpperCAmelCase_ =			(
						    {
						        "feature-extraction": GPTNeoXModel,
						        "question-answering": GPTNeoXForQuestionAnswering,
						        "text-classification": GPTNeoXForSequenceClassification,
						        "text-generation": GPTNeoXForCausalLM,
						        "token-classification": GPTNeoXForTokenClassification,
						        "zero-shot": GPTNeoXForSequenceClassification,
						    }
						    if is_torch_available()
						    else {}
						)
						UpperCAmelCase_ =			False
						UpperCAmelCase_ =			False
						UpperCAmelCase_ =			False
						UpperCAmelCase_ =			False


						def     snake_case_   (self		) ->   str:
								UpperCamelCase   =	GPTNeoXModelTester(self		)
								UpperCamelCase   =	ConfigTester(self   ,							config_class=__a   ,							hidden_size=64   ,							num_attention_heads=8		)


						def     snake_case_   (self		) ->   int:
								self.config_tester.run_common_tests()


						def     snake_case_   (self		) ->   Optional[int]:
								UpperCamelCase   ,				UpperCamelCase   ,				UpperCamelCase   ,				UpperCamelCase   =	self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(__a   ,							__a   ,							__a		)


						def     snake_case_   (self		) ->   str:
								UpperCamelCase   ,				UpperCamelCase   ,				UpperCamelCase   ,				UpperCamelCase   =	self.model_tester.prepare_config_and_inputs_for_decoder()
								self.model_tester.create_and_check_model_as_decoder(__a   ,							__a   ,							__a		)


						def     snake_case_   (self		) ->   Optional[Any]:
								# This regression test was failing with PyTorch < 1.3
								UpperCamelCase   ,				UpperCamelCase   ,				UpperCamelCase   ,				UpperCamelCase   =	self.model_tester.prepare_config_and_inputs_for_decoder()

								UpperCamelCase   =	None

								self.model_tester.create_and_check_model_as_decoder(__a   ,							__a   ,							__a		)


						def     snake_case_   (self		) ->   Union[str, Any]:
								UpperCamelCase   ,				UpperCamelCase   ,				UpperCamelCase   ,				UpperCamelCase   =	self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_decoder_model_past_large_inputs(__a   ,							__a   ,							__a		)


						def     snake_case_   (self		) ->   Any:
								UpperCamelCase   =	self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_causal_lm(*__a		)


						def     snake_case_   (self		) ->   Union[str, Any]:
								UpperCamelCase   =	self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_question_answering(*__a		)


						def     snake_case_   (self		) ->   Optional[int]:
								UpperCamelCase   =	self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_sequence_classification(*__a		)


						def     snake_case_   (self		) ->   Optional[int]:
								UpperCamelCase   =	self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_token_classification(*__a		)


						@unittest.skip(reason="Feed forward chunking is not implemented"		)
						def     snake_case_   (self		) ->   Any:
								pass




						@parameterized.expand([("linear",), ("dynamic",)]		)
						def     snake_case_   (self   ,							__a		) ->   Dict:
								UpperCamelCase   ,				UpperCamelCase   =	self.model_tester.prepare_config_and_inputs_for_common()
								UpperCamelCase   =	ids_tensor([1, 10]   ,							config.vocab_size		)
								UpperCamelCase   =	ids_tensor([1, int(config.max_position_embeddings * 1.5		)]   ,							config.vocab_size		)

								set_seed(42		)  # Fixed seed at init time so the two models get the same random weights
								UpperCamelCase   =	GPTNeoXModel(__a		)
								original_model.to(__a		)
								original_model.eval()
								UpperCamelCase   =	original_model(__a		).last_hidden_state
								UpperCamelCase   =	original_model(__a		).last_hidden_state

								set_seed(42		)  # Fixed seed at init time so the two models get the same random weights
								UpperCamelCase   =	{"type": scaling_type, "factor": 10.0}
								UpperCamelCase   =	GPTNeoXModel(__a		)
								scaled_model.to(__a		)
								scaled_model.eval()
								UpperCamelCase   =	scaled_model(__a		).last_hidden_state
								UpperCamelCase   =	scaled_model(__a		).last_hidden_state

								# Dynamic scaling does not change the RoPE embeddings until it receives an input longer than the original
								# maximum sequence length, so the outputs for the short input should match.
								if scaling_type == "dynamic":
										self.assertTrue(torch.allclose(__a   ,							__a   ,							atol=1e-5		)		)
								else:
										self.assertFalse(torch.allclose(__a   ,							__a   ,							atol=1e-5		)		)

								# The output should be different for long inputs
								self.assertFalse(torch.allclose(__a   ,							__a   ,							atol=1e-5		)		)





@require_torch
class        _lowerCamelCase      (       unittest.TestCase ):


						@slow
						def     snake_case_   (self		) ->   Optional[int]:
								UpperCamelCase   =	AutoTokenizer.from_pretrained("EleutherAI/pythia-410m-deduped"		)
								for checkpointing in [True, False]:
										UpperCamelCase   =	GPTNeoXForCausalLM.from_pretrained("EleutherAI/pythia-410m-deduped"		)

										if checkpointing:
												model.gradient_checkpointing_enable()
										else:
												model.gradient_checkpointing_disable()
										model.to(__a		)

										UpperCamelCase   =	tokenizer("My favorite food is"   ,							return_tensors="pt"		).to(__a		)
										# The hub repo. is updated on 2023-04-04, resulting in poor outputs.
										# See: https://github.com/huggingface/transformers/pull/24193
										UpperCamelCase   =	"My favorite food is a good old-fashioned, old-fashioned, old-fashioned.\n\nI'm not sure"

										UpperCamelCase   =	model.generate(**__a   ,							do_sample=__a   ,							max_new_tokens=20		)
										UpperCamelCase   =	tokenizer.batch_decode(__a		)[0]

										self.assertEqual(__a   ,							__a		)






