





'''simple docstring'''






import json
import os
import tempfile
import unittest

import numpy as np
from datasets import load_dataset

from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin


if is_torch_available():
					import torch

if is_vision_available():
					from PIL import Image

					from transformers import ImageGPTImageProcessor
class 		lowerCamelCase  (      unittest.TestCase       ):



	def __init__(   self		,      a_		,      a_=7		,      a_=3		,      a_=18		,      a_=30		,      a_=400		,      a_=True		,      a_=None		,      a_=True		,      ):
			lowerCAmelCase     :    Union[str, Any]						=   size if size is not None else {"height": 18, "width": 18}
			lowerCAmelCase     :    List[str]						=   parent
			lowerCAmelCase     :    str						=   batch_size
			lowerCAmelCase     :    Dict						=   num_channels
			lowerCAmelCase     :    str						=   image_size
			lowerCAmelCase     :    Optional[int]						=   min_resolution
			lowerCAmelCase     :    Optional[int]						=   max_resolution
			lowerCAmelCase     :    Tuple						=   do_resize
			lowerCAmelCase     :    int						=   size
			lowerCAmelCase     :    int						=   do_normalize





	def   _lowerCamelCase    (   self	):
			return {
			    # here we create 2 clusters for the sake of simplicity
			    "clusters": np.asarray(
			        [
			            [0.8866443634033203, 0.6618829369544983, 0.3891746401786804],
			            [-0.6042559146881104, -0.02295008860528469, 0.5423797369003296],
			        ]	),
			    "do_resize": self.do_resize,
			    "size": self.size,
			    "do_normalize": self.do_normalize,
			}






@require_torch
@require_vision
class 		lowerCamelCase  (      _A      ,   unittest.TestCase       ):
	snake_case_      =   ImageGPTImageProcessor if is_vision_available() else None



	def   _lowerCamelCase    (   self	):
			lowerCAmelCase     :    str						=   ImageGPTImageProcessingTester(self	)



	@property
	def   _lowerCamelCase    (   self	):
			return self.image_processor_tester.prepare_image_processor_dict()



	def   _lowerCamelCase    (   self	):
			lowerCAmelCase     :    str						=   self.image_processing_class(**self.image_processor_dict	)
			self.assertTrue(hasattr(a_		,      "clusters"	)	)
			self.assertTrue(hasattr(a_		,      "do_resize"	)	)
			self.assertTrue(hasattr(a_		,      "size"	)	)
			self.assertTrue(hasattr(a_		,      "do_normalize"	)	)



	def   _lowerCamelCase    (   self	):
			lowerCAmelCase     :    Union[str, Any]						=   self.image_processing_class.from_dict(self.image_processor_dict	)
			self.assertEqual(image_processor.size		,      {"height": 18, "width": 18}	)

			lowerCAmelCase     :    List[str]						=   self.image_processing_class.from_dict(self.image_processor_dict		,      size=42	)
			self.assertEqual(image_processor.size		,      {"height": 42, "width": 42}	)



	def   _lowerCamelCase    (   self	):
			lowerCAmelCase     :    Any						=   self.image_processing_class(**self.image_processor_dict	)
			lowerCAmelCase     :    int						=   json.loads(image_processor.to_json_string()	)
			for key, value in self.image_processor_dict.items():
					if key == "clusters":
							self.assertTrue(np.array_equal(a_		,      obj[key]	)	)
					else:
							self.assertEqual(obj[key]		,      a_	)



	def   _lowerCamelCase    (   self	):
			lowerCAmelCase     :    str						=   self.image_processing_class(**self.image_processor_dict	)

			with tempfile.TemporaryDirectory() as tmpdirname:
					lowerCAmelCase     :    int						=   os.path.join(a_		,      "image_processor.json"	)
					image_processor_first.to_json_file(a_	)
					lowerCAmelCase     :    List[str]						=   self.image_processing_class.from_json_file(a_	).to_dict()

			lowerCAmelCase     :    Optional[Any]						=   image_processor_first.to_dict()
			for key, value in image_processor_first.items():
					if key == "clusters":
							self.assertTrue(np.array_equal(a_		,      image_processor_second[key]	)	)
					else:
							self.assertEqual(image_processor_first[key]		,      a_	)



	def   _lowerCamelCase    (   self	):
			lowerCAmelCase     :    Tuple						=   self.image_processing_class(**self.image_processor_dict	)

			with tempfile.TemporaryDirectory() as tmpdirname:
					image_processor_first.save_pretrained(a_	)
					lowerCAmelCase     :    Optional[Any]						=   self.image_processing_class.from_pretrained(a_	).to_dict()

			lowerCAmelCase     :    Tuple						=   image_processor_first.to_dict()
			for key, value in image_processor_first.items():
					if key == "clusters":
							self.assertTrue(np.array_equal(a_		,      image_processor_second[key]	)	)
					else:
							self.assertEqual(image_processor_first[key]		,      a_	)





	@unittest.skip("ImageGPT requires clusters at initialization"	)
	def   _lowerCamelCase    (   self	):
			pass






def      __A						(							):
		lowerCAmelCase     :    Dict						=   load_dataset("hf-internal-testing/fixtures_image_utils"      ,split="test"	)

		lowerCAmelCase     :    Union[str, Any]						=   Image.open(dataset[4]["file"]	)
		lowerCAmelCase     :    str						=   Image.open(dataset[5]["file"]	)

		lowerCAmelCase     :    Tuple						=   [imagea, imagea]

		return images

@require_vision
@require_torch
class 		lowerCamelCase  (      unittest.TestCase       ):



	@slow
	def   _lowerCamelCase    (   self	):
			lowerCAmelCase     :    str						=   ImageGPTImageProcessor.from_pretrained("openai/imagegpt-small"	)

			lowerCAmelCase     :    Tuple						=   prepare_images()

			# test non-batched
			lowerCAmelCase     :    int						=   image_processing(images[0]		,      return_tensors="pt"	)

			self.assertIsInstance(encoding.input_ids		,      torch.LongTensor	)
			self.assertEqual(encoding.input_ids.shape		,      (1, 1_024)	)

			lowerCAmelCase     :    Tuple						=   [306, 191, 191]
			self.assertEqual(encoding.input_ids[0, :3].tolist()		,      a_	)

			# test batched
			lowerCAmelCase     :    List[Any]						=   image_processing(a_		,      return_tensors="pt"	)

			self.assertIsInstance(encoding.input_ids		,      torch.LongTensor	)
			self.assertEqual(encoding.input_ids.shape		,      (2, 1_024)	)

			lowerCAmelCase     :    Any						=   [303, 13, 13]
			self.assertEqual(encoding.input_ids[1, -3:].tolist()		,      a_	)










'''simple docstring'''






import os
import tempfile
import unittest
from pathlib import Path

from transformers import AutoConfig, is_tf_available
from transformers.testing_utils import require_tf


if is_tf_available():
					import tensorflow as tf

					from transformers import TensorFlowBenchmark, TensorFlowBenchmarkArguments
@require_tf
class 		lowerCamelCase  (      unittest.TestCase       ):



	def   _lowerCamelCase    (   self		,      a_	):
			for model_result in results.values():
					for batch_size, sequence_length in zip(model_result["bs"]		,      model_result["ss"]	):
							lowerCAmelCase     :    Tuple						=   model_result["result"][batch_size][sequence_length]
							self.assertIsNotNone(a_	)



	def   _lowerCamelCase    (   self	):
			lowerCAmelCase     :    Optional[int]						=   "sshleifer/tiny-gpt2"
			lowerCAmelCase     :    Tuple						=   TensorFlowBenchmarkArguments(
			    models=[MODEL_ID]		,      training=a_		,      inference=a_		,      sequence_lengths=[8]		,      batch_sizes=[1]		,      eager_mode=a_		,      multi_process=a_		,      )
			lowerCAmelCase     :    Optional[Any]						=   TensorFlowBenchmark(a_	)
			lowerCAmelCase     :    Optional[Any]						=   benchmark.run()
			self.check_results_dict_not_empty(results.time_inference_result	)
			self.check_results_dict_not_empty(results.memory_inference_result	)



	def   _lowerCamelCase    (   self	):
			lowerCAmelCase     :    Union[str, Any]						=   "sgugger/tiny-distilbert-classification"
			lowerCAmelCase     :    Tuple						=   TensorFlowBenchmarkArguments(
			    models=[MODEL_ID]		,      training=a_		,      inference=a_		,      sequence_lengths=[8]		,      batch_sizes=[1]		,      multi_process=a_		,      only_pretrain_model=a_		,      )
			lowerCAmelCase     :    Any						=   TensorFlowBenchmark(a_	)
			lowerCAmelCase     :    str						=   benchmark.run()
			self.check_results_dict_not_empty(results.time_inference_result	)
			self.check_results_dict_not_empty(results.memory_inference_result	)



	def   _lowerCamelCase    (   self	):
			lowerCAmelCase     :    Union[str, Any]						=   "sshleifer/tiny-gpt2"
			lowerCAmelCase     :    str						=   TensorFlowBenchmarkArguments(
			    models=[MODEL_ID]		,      training=a_		,      inference=a_		,      sequence_lengths=[8]		,      batch_sizes=[1]		,      multi_process=a_		,      )
			lowerCAmelCase     :    List[str]						=   TensorFlowBenchmark(a_	)
			lowerCAmelCase     :    str						=   benchmark.run()
			self.check_results_dict_not_empty(results.time_inference_result	)
			self.check_results_dict_not_empty(results.memory_inference_result	)



	def   _lowerCamelCase    (   self	):
			lowerCAmelCase     :    List[str]						=   "sshleifer/tiny-gpt2"
			lowerCAmelCase     :    Union[str, Any]						=   AutoConfig.from_pretrained(a_	)
			lowerCAmelCase     :    Optional[Any]						=   TensorFlowBenchmarkArguments(
			    models=[MODEL_ID]		,      training=a_		,      inference=a_		,      sequence_lengths=[8]		,      batch_sizes=[1]		,      eager_mode=a_		,      multi_process=a_		,      )
			lowerCAmelCase     :    Any						=   TensorFlowBenchmark(a_		,      [config]	)
			lowerCAmelCase     :    Union[str, Any]						=   benchmark.run()
			self.check_results_dict_not_empty(results.time_inference_result	)
			self.check_results_dict_not_empty(results.memory_inference_result	)



	def   _lowerCamelCase    (   self	):
			lowerCAmelCase     :    int						=   "sshleifer/tiny-gpt2"
			lowerCAmelCase     :    Union[str, Any]						=   AutoConfig.from_pretrained(a_	)
			lowerCAmelCase     :    Union[str, Any]						=   TensorFlowBenchmarkArguments(
			    models=[MODEL_ID]		,      training=a_		,      inference=a_		,      sequence_lengths=[8]		,      batch_sizes=[1]		,      multi_process=a_		,      )
			lowerCAmelCase     :    Tuple						=   TensorFlowBenchmark(a_		,      [config]	)
			lowerCAmelCase     :    Any						=   benchmark.run()
			self.check_results_dict_not_empty(results.time_inference_result	)
			self.check_results_dict_not_empty(results.memory_inference_result	)



	def   _lowerCamelCase    (   self	):
			lowerCAmelCase     :    Tuple						=   "sshleifer/tiny-gpt2"
			lowerCAmelCase     :    List[str]						=   TensorFlowBenchmarkArguments(
			    models=[MODEL_ID]		,      training=a_		,      inference=a_		,      sequence_lengths=[8]		,      batch_sizes=[1]		,      multi_process=a_		,      )
			lowerCAmelCase     :    List[str]						=   TensorFlowBenchmark(a_	)
			lowerCAmelCase     :    str						=   benchmark.run()
			self.check_results_dict_not_empty(results.time_train_result	)
			self.check_results_dict_not_empty(results.memory_train_result	)



	def   _lowerCamelCase    (   self	):
			lowerCAmelCase     :    List[Any]						=   "sshleifer/tiny-gpt2"
			lowerCAmelCase     :    Union[str, Any]						=   AutoConfig.from_pretrained(a_	)
			lowerCAmelCase     :    Dict						=   TensorFlowBenchmarkArguments(
			    models=[MODEL_ID]		,      training=a_		,      inference=a_		,      sequence_lengths=[8]		,      batch_sizes=[1]		,      multi_process=a_		,      )
			lowerCAmelCase     :    Dict						=   TensorFlowBenchmark(a_		,      [config]	)
			lowerCAmelCase     :    int						=   benchmark.run()
			self.check_results_dict_not_empty(results.time_train_result	)
			self.check_results_dict_not_empty(results.memory_train_result	)



	def   _lowerCamelCase    (   self	):
			lowerCAmelCase     :    str						=   "patrickvonplaten/t5-tiny-random"
			lowerCAmelCase     :    Tuple						=   AutoConfig.from_pretrained(a_	)
			lowerCAmelCase     :    Dict						=   TensorFlowBenchmarkArguments(
			    models=[MODEL_ID]		,      training=a_		,      inference=a_		,      sequence_lengths=[8]		,      batch_sizes=[1]		,      multi_process=a_		,      )
			lowerCAmelCase     :    List[str]						=   TensorFlowBenchmark(a_		,      configs=[config]	)
			lowerCAmelCase     :    List[Any]						=   benchmark.run()
			self.check_results_dict_not_empty(results.time_inference_result	)
			self.check_results_dict_not_empty(results.memory_inference_result	)



	@unittest.skipIf(is_tf_available() and len(tf.config.list_physical_devices("GPU"	)	) == 0		,      "Cannot do xla on CPU."	)
	def   _lowerCamelCase    (   self	):
			lowerCAmelCase     :    str						=   "sshleifer/tiny-gpt2"
			lowerCAmelCase     :    List[Any]						=   TensorFlowBenchmarkArguments(
			    models=[MODEL_ID]		,      training=a_		,      inference=a_		,      sequence_lengths=[8]		,      batch_sizes=[1]		,      use_xla=a_		,      multi_process=a_		,      )
			lowerCAmelCase     :    Any						=   TensorFlowBenchmark(a_	)
			lowerCAmelCase     :    str						=   benchmark.run()
			self.check_results_dict_not_empty(results.time_inference_result	)
			self.check_results_dict_not_empty(results.memory_inference_result	)



	def   _lowerCamelCase    (   self	):
			lowerCAmelCase     :    Tuple						=   "sshleifer/tiny-gpt2"
			with tempfile.TemporaryDirectory() as tmp_dir:
					lowerCAmelCase     :    Optional[int]						=   TensorFlowBenchmarkArguments(
					    models=[MODEL_ID]		,      inference=a_		,      save_to_csv=a_		,      sequence_lengths=[8]		,      batch_sizes=[1]		,      inference_time_csv_file=os.path.join(a_		,      "inf_time.csv"	)		,      inference_memory_csv_file=os.path.join(a_		,      "inf_mem.csv"	)		,      env_info_csv_file=os.path.join(a_		,      "env.csv"	)		,      multi_process=a_		,      )
					lowerCAmelCase     :    Optional[Any]						=   TensorFlowBenchmark(a_	)
					benchmark.run()
					self.assertTrue(Path(os.path.join(a_		,      "inf_time.csv"	)	).exists()	)
					self.assertTrue(Path(os.path.join(a_		,      "inf_mem.csv"	)	).exists()	)
					self.assertTrue(Path(os.path.join(a_		,      "env.csv"	)	).exists()	)





	def   _lowerCamelCase    (   self	):
			lowerCAmelCase     :    Any						=   "sshleifer/tiny-gpt2"

			def _check_summary_is_not_empty(a_	):
					self.assertTrue(hasattr(a_		,      "sequential"	)	)
					self.assertTrue(hasattr(a_		,      "cumulative"	)	)
					self.assertTrue(hasattr(a_		,      "current"	)	)
					self.assertTrue(hasattr(a_		,      "total"	)	)

			with tempfile.TemporaryDirectory() as tmp_dir:
					lowerCAmelCase     :    int						=   TensorFlowBenchmarkArguments(
					    models=[MODEL_ID]		,      inference=a_		,      sequence_lengths=[8]		,      batch_sizes=[1]		,      log_filename=os.path.join(a_		,      "log.txt"	)		,      log_print=a_		,      trace_memory_line_by_line=a_		,      eager_mode=a_		,      multi_process=a_		,      )
					lowerCAmelCase     :    Optional[Any]						=   TensorFlowBenchmark(a_	)
					lowerCAmelCase     :    Tuple						=   benchmark.run()
					_check_summary_is_not_empty(result.inference_summary	)
					self.assertTrue(Path(os.path.join(a_		,      "log.txt"	)	).exists()	)



