

import json
import os
import tempfile

import datasets
from utils import generate_example_dataset, get_duration


lowerCAmelCase_       	=	5_00_00
lowerCAmelCase_       	=	50_00

lowerCAmelCase_	,      lowerCAmelCase_       	=	os.path.split(__file__)
lowerCAmelCase_       	=	os.path.join(RESULTS_BASEPATH, '''results''', RESULTS_FILENAME.replace('''.py''', '''.json'''))


@get_duration
def   __SCREAMING_SNAKE_CASE					(SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__       ):
						for i in range(SCREAMING_SNAKE_CASE__       ):
												snake_case_   =	dataset[i]


@get_duration
def   __SCREAMING_SNAKE_CASE					(SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__       ):
						for i in range(0  ,		len(SCREAMING_SNAKE_CASE__       )  ,		SCREAMING_SNAKE_CASE__       ):
												snake_case_   =	dataset[i : i + batch_size]


@get_duration
def   __SCREAMING_SNAKE_CASE					(SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__       ):
						with dataset.formatted_as(type=SCREAMING_SNAKE_CASE__       ):
												for i in range(SCREAMING_SNAKE_CASE__       ):
																		snake_case_   =	dataset[i]


@get_duration
def   __SCREAMING_SNAKE_CASE					(SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__       ):
						with dataset.formatted_as(type=SCREAMING_SNAKE_CASE__       ):
												for i in range(0  ,		SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__       ):
																		snake_case_   =	dataset[i : i + batch_size]


def   __SCREAMING_SNAKE_CASE					():
						snake_case_   =	{'''num examples''': SPEED_TEST_N_EXAMPLES}
						snake_case_   =	[
						    (read, {'''length''': SMALL_TEST}),
						    (read, {'''length''': SPEED_TEST_N_EXAMPLES}),
						    (read_batch, {'''length''': SPEED_TEST_N_EXAMPLES, '''batch_size''': 10}),
						    (read_batch, {'''length''': SPEED_TEST_N_EXAMPLES, '''batch_size''': 100}),
						    (read_batch, {'''length''': SPEED_TEST_N_EXAMPLES, '''batch_size''': 1000}),
						    (read_formatted, {'''type''': '''numpy''', '''length''': SMALL_TEST}),
						    (read_formatted, {'''type''': '''pandas''', '''length''': SMALL_TEST}),
						    (read_formatted, {'''type''': '''torch''', '''length''': SMALL_TEST}),
						    (read_formatted, {'''type''': '''tensorflow''', '''length''': SMALL_TEST}),
						    (read_formatted_batch, {'''type''': '''numpy''', '''length''': SMALL_TEST, '''batch_size''': 10}),
						    (read_formatted_batch, {'''type''': '''numpy''', '''length''': SMALL_TEST, '''batch_size''': 1000}),
						]

						snake_case_   =	[
						    (read, {'''length''': SMALL_TEST}),
						    (read, {'''length''': SPEED_TEST_N_EXAMPLES}),
						    (read_batch, {'''length''': SPEED_TEST_N_EXAMPLES, '''batch_size''': 10}),
						    (read_batch, {'''length''': SPEED_TEST_N_EXAMPLES, '''batch_size''': 100}),
						    (read_batch, {'''length''': SPEED_TEST_N_EXAMPLES, '''batch_size''': 1000}),
						    (read_formatted, {'''type''': '''numpy''', '''length''': SMALL_TEST}),
						    (read_formatted_batch, {'''type''': '''numpy''', '''length''': SMALL_TEST, '''batch_size''': 10}),
						    (read_formatted_batch, {'''type''': '''numpy''', '''length''': SMALL_TEST, '''batch_size''': 1000}),
						]
						with tempfile.TemporaryDirectory() as tmp_dir:
												print('''generating dataset'''       )
												snake_case_   =	datasets.Features(
												    {'''list''': datasets.Sequence(datasets.Value('''float32'''       )       ), '''numbers''': datasets.Value('''float32'''       )}       )
												snake_case_   =	generate_example_dataset(
												    os.path.join(SCREAMING_SNAKE_CASE__  ,		'''dataset.arrow'''       )  ,		SCREAMING_SNAKE_CASE__  ,		num_examples=SCREAMING_SNAKE_CASE__  ,		seq_shapes={'''list''': (100,)}  ,		)
												print('''first set of iterations'''       )
												for func, kwargs in functions:
																		print(func.__name__  ,		str(SCREAMING_SNAKE_CASE__       )       )
																		snake_case_   =	func(SCREAMING_SNAKE_CASE__  ,		**SCREAMING_SNAKE_CASE__       )

												print('''shuffling dataset'''       )
												snake_case_   =	dataset.shuffle()
												print('''Second set of iterations (after shuffling'''       )
												for func, kwargs in functions_shuffled:
																		print('''shuffled '''  ,		func.__name__  ,		str(SCREAMING_SNAKE_CASE__       )       )
																		snake_case_   =	func(
																		    SCREAMING_SNAKE_CASE__  ,		**SCREAMING_SNAKE_CASE__       )

						with open(SCREAMING_SNAKE_CASE__  ,		'''wb'''       ) as f:
												f.write(json.dumps(SCREAMING_SNAKE_CASE__       ).encode('''utf-8'''       )       )


if __name__ == "__main__":  # useful to run the profiler
					benchmark_iterating()


# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from ..models.auto import AutoModelForSeqaSeqLM, AutoTokenizer
from .base import PipelineTool





class 	snake_case_   (		__A					):







			'''simple docstring'''

			SCREAMING_SNAKE_CASE     :   Optional[Any]									=				"philschmid/bart-large-cnn-samsum"
			SCREAMING_SNAKE_CASE     :   Tuple									=				(
			    "This is a tool that summarizes an English text. It takes an input `text` containing the text to summarize, "
			    "and returns a summary of the text."
			)
			SCREAMING_SNAKE_CASE     :   str									=				"summarizer"
			SCREAMING_SNAKE_CASE     :   str									=				AutoTokenizer
			SCREAMING_SNAKE_CASE     :   str									=				AutoModelForSeqaSeqLM

			SCREAMING_SNAKE_CASE     :   Optional[int]									=				["text"]
			SCREAMING_SNAKE_CASE     :   Optional[int]									=				["text"]







			def 				snake_case__(     self :				str							,   _UpperCamelCase :				int							)      ->Optional[int]:
									return self.pre_processor(_UpperCamelCase							,   return_tensors='''pt'''							,   truncation=_UpperCamelCase							)







			def 				snake_case__(     self :				Tuple							,   _UpperCamelCase :				Optional[int]							)      ->Tuple:
									return self.model.generate(**_UpperCamelCase							)[0]







			def 				snake_case__(     self :				Optional[Any]							,   _UpperCamelCase :				Optional[int]							)      ->Any:
									return self.pre_processor.decode(_UpperCamelCase							,   skip_special_tokens=_UpperCamelCase							,   clean_up_tokenization_spaces=_UpperCamelCase							)