





import argparse
import json
import math
import os
import time
import traceback
import zipfile
from collections import Counter

import requests


def       a__					(					_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE=None    ):




		"""simple docstring"""


		UpperCamelCase   =	None
		if token is not None:
				UpperCamelCase   =	{'''Accept''': '''application/vnd.github+json''', '''Authorization''': F"Bearer {token}"}

		UpperCamelCase   =	F"https://api.github.com/repos/huggingface/transformers/actions/runs/{workflow_run_id}/jobs?per_page=100"
		UpperCamelCase   =	requests.get(a_  ,	headers=a_    ).json()
		UpperCamelCase   =	{}

		try:
				job_links.update({job["name"]: job["html_url"] for job in result["jobs"]}    )
				UpperCamelCase   =	math.ceil((result["total_count"] - 100) / 100    )

				for i in range(a_    ):
						UpperCamelCase   =	requests.get(url + F"&page={i + 2}"  ,	headers=a_    ).json()
						job_links.update({job["name"]: job["html_url"] for job in result["jobs"]}    )

				return job_links
		except Exception:
				print(F"Unknown error, could not fetch links:\n{traceback.format_exc()}"    )

		return {}


def       a__					(					_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE=None    ):




		"""simple docstring"""


		UpperCamelCase   =	None
		if token is not None:
				UpperCamelCase   =	{'''Accept''': '''application/vnd.github+json''', '''Authorization''': F"Bearer {token}"}

		UpperCamelCase   =	F"https://api.github.com/repos/huggingface/transformers/actions/runs/{worflow_run_id}/artifacts?per_page=100"
		UpperCamelCase   =	requests.get(a_  ,	headers=a_    ).json()
		UpperCamelCase   =	{}

		try:
				artifacts.update({artifact["name"]: artifact["archive_download_url"] for artifact in result["artifacts"]}    )
				UpperCamelCase   =	math.ceil((result["total_count"] - 100) / 100    )

				for i in range(a_    ):
						UpperCamelCase   =	requests.get(url + F"&page={i + 2}"  ,	headers=a_    ).json()
						artifacts.update({artifact["name"]: artifact["archive_download_url"] for artifact in result["artifacts"]}    )

				return artifacts
		except Exception:
				print(F"Unknown error, could not fetch links:\n{traceback.format_exc()}"    )

		return {}


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




		"""simple docstring"""


		UpperCamelCase   =	None
		if token is not None:
				UpperCamelCase   =	{'''Accept''': '''application/vnd.github+json''', '''Authorization''': F"Bearer {token}"}

		UpperCamelCase   =	requests.get(a_  ,	headers=a_  ,	allow_redirects=a_    )
		UpperCamelCase   =	result.headers['''Location''']
		UpperCamelCase   =	requests.get(a_  ,	allow_redirects=a_    )
		UpperCamelCase   =	os.path.join(a_  ,	F"{artifact_name}.zip"    )
		with open(a_  ,	"wb"    ) as fp:
				fp.write(response.content    )


def       a__					(					_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE=None    ):




		"""simple docstring"""


		UpperCamelCase   =	[]
		UpperCamelCase   =	[]
		UpperCamelCase   =	None

		with zipfile.ZipFile(a_    ) as z:
				for filename in z.namelist():
						if not os.path.isdir(a_    ):
								# read the file
								if filename in ["failures_line.txt", "summary_short.txt", "job_name.txt"]:
										with z.open(a_    ) as f:
												for line in f:
														UpperCamelCase   =	line.decode("UTF-8"    ).strip()
														if filename == "failures_line.txt":
																try:
																		# `error_line` is the place where `error` occurs
																		UpperCamelCase   =	line[: line.index(": "    )]
																		UpperCamelCase   =	line[line.index(": "    ) + len(": "    ) :]
																		errors.append([error_line, error]    )
																except Exception:
																		# skip un-related lines
																		pass
														elif filename == "summary_short.txt" and line.startswith("FAILED "    ):
																# `test` is the test method that failed
																UpperCamelCase   =	line[len("FAILED "    ) :]
																failed_tests.append(a_    )
														elif filename == "job_name.txt":
																UpperCamelCase   =	line

		if len(a_    ) != len(a_    ):
				raise ValueError(
				    F"`errors` and `failed_tests` should have the same number of elements. Got {len(a_    )} for `errors` "
				    F"and {len(a_    )} for `failed_tests` instead. The test reports in {artifact_zip_path} have some"
				    " problem."    )

		UpperCamelCase   =	None
		if job_name and job_links:
				UpperCamelCase   =	job_links.get(a_  ,	a_    )

		# A list with elements of the form (line of error, error, failed test)
		UpperCamelCase   =	[x + [y] + [job_link] for x, y in zip(a_  ,	a_    )]

		return result


def       a__					(					_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE=None    ):




		"""simple docstring"""


		UpperCamelCase   =	[]

		UpperCamelCase   =	[os.path.join(a_  ,	a_    ) for p in os.listdir(a_    ) if p.endswith(".zip"    )]
		for p in paths:
				errors.extend(get_errors_from_single_artifact(a_  ,	job_links=a_    )    )

		return errors


def       a__					(					_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE=None    ):




		"""simple docstring"""


		UpperCamelCase   =	Counter()
		counter.update([x[1] for x in logs]    )
		UpperCamelCase   =	counter.most_common()
		UpperCamelCase   =	{}
		for error, count in counts:
				if error_filter is None or error not in error_filter:
						UpperCamelCase   =	{'''count''': count, '''failed_tests''': [(x[2], x[0]) for x in logs if x[1] == error]}

		UpperCamelCase   =	dict(sorted(r.items()  ,	key=lambda _SCREAMING_SNAKE_CASE    : item[1]["count"]  ,	reverse=a_    )    )
		return r


def       a__					(					_SCREAMING_SNAKE_CASE    ):




		"""simple docstring"""


		UpperCamelCase   =	test.split("::"    )[0]
		if test.startswith("tests/models/"    ):
				UpperCamelCase   =	test.split("/"    )[2]
		else:
				UpperCamelCase   =	None

		return test


def       a__					(					_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE=None    ):




		"""simple docstring"""


		UpperCamelCase   =	[(x[0], x[1], get_model(x[2]    )) for x in logs]
		UpperCamelCase   =	[x for x in logs if x[2] is not None]
		UpperCamelCase   =	{x[2] for x in logs}

		UpperCamelCase   =	{}
		for test in tests:
				UpperCamelCase   =	Counter()
				# count by errors in `test`
				counter.update([x[1] for x in logs if x[2] == test]    )
				UpperCamelCase   =	counter.most_common()
				UpperCamelCase   =	{error: count for error, count in counts if (error_filter is None or error not in error_filter)}
				UpperCamelCase   =	sum(error_counts.values()    )
				if n_errors > 0:
						UpperCamelCase   =	{'''count''': n_errors, '''errors''': error_counts}

		UpperCamelCase   =	dict(sorted(r.items()  ,	key=lambda _SCREAMING_SNAKE_CASE    : item[1]["count"]  ,	reverse=a_    )    )
		return r


def       a__					(					_SCREAMING_SNAKE_CASE    ):




		"""simple docstring"""


		UpperCamelCase   =	'''| no. | error | status |'''
		UpperCamelCase   =	'''|-:|:-|:-|'''
		UpperCamelCase   =	[header, sep]
		for error in reduced_by_error:
				UpperCamelCase   =	reduced_by_error[error]['''count''']
				UpperCamelCase   =	F"| {count} | {error[:100]} |  |"
				lines.append(a_    )

		return "\n".join(a_    )


def       a__					(					_SCREAMING_SNAKE_CASE    ):




		"""simple docstring"""


		UpperCamelCase   =	'''| model | no. of errors | major error | count |'''
		UpperCamelCase   =	'''|-:|-:|-:|-:|'''
		UpperCamelCase   =	[header, sep]
		for model in reduced_by_model:
				UpperCamelCase   =	reduced_by_model[model]['''count''']
				UpperCamelCase   =	list(reduced_by_model[model]["errors"].items()    )[0]
				UpperCamelCase   =	F"| {model} | {count} | {error[:60]} | {_count} |"
				lines.append(a_    )

		return "\n".join(a_    )


if __name__ == "__main__":
						lowerCAmelCase__     			=			argparse.ArgumentParser()
						# Required parameters
						parser.add_argument('''--workflow_run_id''', type=str, required=True, help='''A GitHub Actions workflow run id.''')
						parser.add_argument(
						    '''--output_dir''',
						    type=str,
						    required=True,
						    help='''Where to store the downloaded artifacts and other result files.''',
						)
						parser.add_argument('''--token''', default=None, type=str, help='''A token that has actions:read permission.''')
						lowerCAmelCase__     			=			parser.parse_args()

						os.makedirs(args.output_dir, exist_ok=True)

						lowerCAmelCase__     			=			get_job_links(args.workflow_run_id, token=args.token)
						lowerCAmelCase__     			=			{}
						# To deal with `workflow_call` event, where a job name is the combination of the job names in the caller and callee.
						# For example, `PyTorch 1.11 / Model tests (models/albert, single-gpu)`.
						if _job_links:
												for k, v in _job_links.items():
																		# This is how GitHub actions combine job names.
																		if " / " in k:
																								lowerCAmelCase__     			=			k.find(''' / ''')
																								lowerCAmelCase__     			=			k[index + len(''' / ''') :]
																		lowerCAmelCase__     			=			v
						with open(os.path.join(args.output_dir, '''job_links.json'''), '''w''', encoding='''UTF-8''') as fp:
												json.dump(job_links, fp, ensure_ascii=False, indent=4)

						lowerCAmelCase__     			=			get_artifacts_links(args.workflow_run_id, token=args.token)
						with open(os.path.join(args.output_dir, '''artifacts.json'''), '''w''', encoding='''UTF-8''') as fp:
												json.dump(artifacts, fp, ensure_ascii=False, indent=4)

						for idx, (name, url) in enumerate(artifacts.items()):
												download_artifact(name, url, args.output_dir, args.token)
												# Be gentle to GitHub
												time.sleep(1)

						lowerCAmelCase__     			=			get_all_errors(args.output_dir, job_links=job_links)

						# `e[1]` is the error
						lowerCAmelCase__     			=			Counter()
						counter.update([e[1] for e in errors])

						# print the top 30 most common test errors
						lowerCAmelCase__     			=			counter.most_common(30)
						for item in most_common:
												print(item)

						with open(os.path.join(args.output_dir, '''errors.json'''), '''w''', encoding='''UTF-8''') as fp:
												json.dump(errors, fp, ensure_ascii=False, indent=4)

						lowerCAmelCase__     			=			reduce_by_error(errors)
						lowerCAmelCase__     			=			reduce_by_model(errors)

						lowerCAmelCase__     			=			make_github_table(reduced_by_error)
						lowerCAmelCase__     			=			make_github_table_per_model(reduced_by_model)

						with open(os.path.join(args.output_dir, '''reduced_by_error.txt'''), '''w''', encoding='''UTF-8''') as fp:
												fp.write(sa)
						with open(os.path.join(args.output_dir, '''reduced_by_model.txt'''), '''w''', encoding='''UTF-8''') as fp:
												fp.write(sa)













"""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		)






