
# 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.
import re

from ..utils import cached_file


# docstyle-ignore
__a  			=					'\nHuman: <<task>>\n\nAssistant: '


__a  			=					'huggingface-tools/default-prompts'
__a  			=					{'chat': 'chat_prompt_template.txt', 'run': 'run_prompt_template.txt'}




def 				a						(							snake_case__:					Union[str, Any]						,   snake_case__:					str						,   snake_case__:					Tuple="run"					):


				'''simple docstring'''
				if prompt_or_repo_id is None:
								lowercase_       =					DEFAULT_PROMPTS_REPO

				# prompt is considered a repo ID when it does not contain any kind of space
				if re.search('''\\s'''						,   snake_case__					) is not None:
								return prompt_or_repo_id

				lowercase_       =					cached_file(
				    snake_case__						,   PROMPT_FILES[mode]						,   repo_type='''dataset'''						,   user_agent={'''agent''': agent_name}					)
				with open(snake_case__						,   '''r'''						,   encoding='''utf-8'''					) as f:
								return f.read()









"""simple docstring"""







import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_tf


if is_tf_available():
     import tensorflow as tf
     from tensorflow.python.eager import context
     from tensorflow.python.framework import ops

     from transformers import GradientAccumulator, create_optimizer







@require_tf
class       UpperCamelCase__   (	unittest.TestCase  ):




      """simple docstring"""




      def      SCREAMING_SNAKE_CASE__ (      self				:      List[str]    ,			SCREAMING_SNAKE_CASE_				:      str    ,			SCREAMING_SNAKE_CASE_				:      List[Any]    ,			SCREAMING_SNAKE_CASE_				:      List[str]       ):
        self.assertEqual(len(SCREAMING_SNAKE_CASE_       )    ,			len(SCREAMING_SNAKE_CASE_       )       )
        for a, b in zip(SCREAMING_SNAKE_CASE_    ,			SCREAMING_SNAKE_CASE_       ):
          self.assertAlmostEqual(SCREAMING_SNAKE_CASE_    ,			SCREAMING_SNAKE_CASE_    ,			delta=SCREAMING_SNAKE_CASE_       )



      def      SCREAMING_SNAKE_CASE__ (      self				:      int       ):
        lowerCAmelCase_   :				int								= GradientAccumulator()
        accumulator([tf.constant([1.0, 2.0]       )]       )
        accumulator([tf.constant([-2.0, 1.0]       )]       )
        accumulator([tf.constant([-1.0, 2.0]       )]       )
        with self.assertRaises(SCREAMING_SNAKE_CASE_       ):
          accumulator([tf.constant([1.0, 1.0]       ), tf.constant([2.0, 2.0]       )]       )
        self.assertEqual(accumulator.step    ,			3       )
        self.assertEqual(len(accumulator.gradients       )    ,			1       )
        self.assertListAlmostEqual(accumulator.gradients[0].numpy().tolist()    ,			[-2.0, 5.0]    ,			tol=1E-2       )
        accumulator.reset()
        self.assertEqual(accumulator.step    ,			0       )
        self.assertListAlmostEqual(accumulator.gradients[0].numpy().tolist()    ,			[0.0, 0.0]    ,			tol=1E-2       )





      def      SCREAMING_SNAKE_CASE__ (      self				:      str       ):
        lowerCAmelCase_   :				Optional[int]								= None
        ops.enable_eager_execution_internal()
        lowerCAmelCase_   :				str								= tf.config.list_physical_devices('CPU'       )
        if len(SCREAMING_SNAKE_CASE_       ) == 1:
          tf.config.set_logical_device_configuration(
              physical_devices[0]    ,			[tf.config.LogicalDeviceConfiguration(), tf.config.LogicalDeviceConfiguration()]       )
        lowerCAmelCase_   :				Dict								= tf.config.list_logical_devices(device_type='CPU'       )
        lowerCAmelCase_   :				Optional[int]								= tf.distribute.MirroredStrategy(devices=devices[:2]       )

        with strategy.scope():
          lowerCAmelCase_   :				Union[str, Any]								= GradientAccumulator()
          lowerCAmelCase_   :				int								= tf.Variable([4.0, 3.0]       )
          lowerCAmelCase_						,lowerCAmelCase_   :				Optional[int]								= create_optimizer(5E-5    ,			1_0    ,			5       )
          lowerCAmelCase_   :				Union[str, Any]								= tf.Variable([0.0, 0.0]    ,			trainable=SCREAMING_SNAKE_CASE_       )

        def accumulate_on_replica(SCREAMING_SNAKE_CASE_				:      Optional[int]       ):
          accumulator([gradient]       )

        def apply_on_replica():
          optimizer.apply_gradients(list(zip(accumulator.gradients    ,			[variable]       )       )       )

        @tf.function
        def accumulate(SCREAMING_SNAKE_CASE_				:      Optional[int]    ,			SCREAMING_SNAKE_CASE_				:      Union[str, Any]       ):
          with strategy.scope():
            lowerCAmelCase_   :				Tuple								= strategy.experimental_local_results(SCREAMING_SNAKE_CASE_       )
            local_variables[0].assign(SCREAMING_SNAKE_CASE_       )
            local_variables[1].assign(SCREAMING_SNAKE_CASE_       )
            strategy.run(SCREAMING_SNAKE_CASE_    ,			args=(gradient_placeholder,)       )

        @tf.function
        def apply_grad():
          with strategy.scope():
            strategy.run(SCREAMING_SNAKE_CASE_       )

        def _check_local_values(SCREAMING_SNAKE_CASE_				:      Union[str, Any]    ,			SCREAMING_SNAKE_CASE_				:      Optional[int]       ):
          lowerCAmelCase_   :				List[Any]								= strategy.experimental_local_results(accumulator._gradients[0]       )
          self.assertListAlmostEqual(values[0].value()    ,			SCREAMING_SNAKE_CASE_    ,			tol=1E-2       )
          self.assertListAlmostEqual(values[1].value()    ,			SCREAMING_SNAKE_CASE_    ,			tol=1E-2       )

        accumulate([1.0, 2.0]    ,			[-1.0, 1.0]       )
        accumulate([3.0, -1.0]    ,			[-1.0, -1.0]       )
        accumulate([-2.0, 2.0]    ,			[3.0, -2.0]       )
        self.assertEqual(accumulator.step    ,			3       )
        _check_local_values([2.0, 3.0]    ,			[1.0, -2.0]       )
        apply_grad()
        self.assertListAlmostEqual(variable.value()    ,			[4.0, 3.0]    ,			tol=1E-2       )
        accumulator.reset()
        self.assertEqual(accumulator.step    ,			0       )
        _check_local_values([0.0, 0.0]    ,			[0.0, 0.0]       )





