

import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import BlenderbotSmallConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor


if is_flax_available():
     import os

     # The slow tests are often failing with OOM error on GPU
     # This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
     # but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
     _lowerCamelCase    :       Dict        =							"platform"

     import jax
     import jax.numpy as jnp

     from transformers.models.blenderbot_small.modeling_flax_blenderbot_small import (
         FlaxBlenderbotSmallForConditionalGeneration,
         FlaxBlenderbotSmallModel,
         shift_tokens_right,
     )

def       a__					(		UpperCAmelCase							:      List[Any]    ,			UpperCAmelCase							:      Union[str, Any]    ,			UpperCAmelCase							:      Optional[int]=None    ,			UpperCAmelCase							:      Optional[int]=None    ,			UpperCAmelCase							:      Dict=None    ,			UpperCAmelCase							:      List[str]=None    ,			UpperCAmelCase							:      Dict=None    ,			UpperCAmelCase							:      Optional[Any]=None    ,			)  ->						List[Any]:





  if attention_mask is None:
    UpperCAmelCase	:							List[Any]						=			np.where(input_ids != config.pad_token_id    ,			1    ,			0      )
  if decoder_attention_mask is None:
    UpperCAmelCase	:							Any						=			np.where(decoder_input_ids != config.pad_token_id    ,			1    ,			0      )
  if head_mask is None:
    UpperCAmelCase	:							Optional[Any]						=			np.ones((config.encoder_layers, config.encoder_attention_heads)      )
  if decoder_head_mask is None:
    UpperCAmelCase	:							List[str]						=			np.ones((config.decoder_layers, config.decoder_attention_heads)      )
  if cross_attn_head_mask is None:
    UpperCAmelCase	:							Optional[int]						=			np.ones((config.decoder_layers, config.decoder_attention_heads)      )
  return {
      "input_ids": input_ids,
      "decoder_input_ids": decoder_input_ids,
      "attention_mask": attention_mask,
      "decoder_attention_mask": attention_mask,
  }





class  __UpperCAmelCase					:



     def __init__(							self							:					Optional[int],		__A							:					Dict,		__A							:					Union[str, Any]=1_3,		__A							:					Any=7,		__A							:					Union[str, Any]=True,		__A							:					Optional[Any]=False,		__A							:					int=9_9,		__A							:					List[Any]=1_6,		__A							:					Optional[int]=2,		__A							:					Union[str, Any]=4,		__A							:					List[Any]=4,		__A							:					Dict="gelu",		__A							:					int=0.1,		__A							:					Union[str, Any]=0.1,		__A							:					int=3_2,		__A							:					Dict=2,		__A							:					str=1,		__A							:					Union[str, Any]=0,		__A							:					Optional[Any]=0.0_2,		):
       UpperCAmelCase	:							Optional[int]						=			parent
       UpperCAmelCase	:							Union[str, Any]						=			batch_size
       UpperCAmelCase	:							Dict						=			seq_length
       UpperCAmelCase	:							Optional[Any]						=			is_training
       UpperCAmelCase	:							Optional[Any]						=			use_labels
       UpperCAmelCase	:							List[str]						=			vocab_size
       UpperCAmelCase	:							int						=			hidden_size
       UpperCAmelCase	:							List[Any]						=			num_hidden_layers
       UpperCAmelCase	:							int						=			num_attention_heads
       UpperCAmelCase	:							Dict						=			intermediate_size
       UpperCAmelCase	:							str						=			hidden_act
       UpperCAmelCase	:							Any						=			hidden_dropout_prob
       UpperCAmelCase	:							Any						=			attention_probs_dropout_prob
       UpperCAmelCase	:							Any						=			max_position_embeddings
       UpperCAmelCase	:							str						=			eos_token_id
       UpperCAmelCase	:							int						=			pad_token_id
       UpperCAmelCase	:							int						=			bos_token_id
       UpperCAmelCase	:							Optional[Any]						=			initializer_range



     def 	__magic_name__   (							self							:					List[str]						):
       UpperCAmelCase	:							Dict						=			np.clip(ids_tensor([self.batch_size, self.seq_length - 1],		self.vocab_size						),		3,		self.vocab_size						)
       UpperCAmelCase	:							Optional[int]						=			np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1),		dtype=np.intaa						)),		-1						)

       UpperCAmelCase	:							Optional[Any]						=			shift_tokens_right(__lowerCAmelCase,		1,		2						)

       UpperCAmelCase	:							Optional[int]						=			BlenderbotSmallConfig(
           vocab_size=self.vocab_size,		d_model=self.hidden_size,		encoder_layers=self.num_hidden_layers,		decoder_layers=self.num_hidden_layers,		encoder_attention_heads=self.num_attention_heads,		decoder_attention_heads=self.num_attention_heads,		encoder_ffn_dim=self.intermediate_size,		decoder_ffn_dim=self.intermediate_size,		dropout=self.hidden_dropout_prob,		attention_dropout=self.attention_probs_dropout_prob,		max_position_embeddings=self.max_position_embeddings,		eos_token_id=self.eos_token_id,		bos_token_id=self.bos_token_id,		pad_token_id=self.pad_token_id,		initializer_range=self.initializer_range,		use_cache=__lowerCAmelCase,		)
       UpperCAmelCase	:							List[Any]						=			prepare_blenderbot_inputs_dict(__lowerCAmelCase,		__lowerCAmelCase,		__lowerCAmelCase						)
       return config, inputs_dict



     def 	__magic_name__   (							self							:					Optional[Any]						):
       UpperCAmelCase			,						UpperCAmelCase	:							str						=			self.prepare_config_and_inputs()
       return config, inputs_dict



     def 	__magic_name__   (							self							:					str,		__A							:					Dict,		__A							:					Optional[int],		__A							:					Optional[int]						):
       UpperCAmelCase	:							Dict						=			2_0
       UpperCAmelCase	:							Optional[Any]						=			model_class_name(__lowerCAmelCase						)

       UpperCAmelCase	:							Optional[int]						=			model.encode(inputs_dict['''input_ids''']						)

       UpperCAmelCase			,						UpperCAmelCase	:							Union[str, Any]						=			(
           inputs_dict['''decoder_input_ids'''],
           inputs_dict['''decoder_attention_mask'''],
       )

       UpperCAmelCase	:							List[Any]						=			model.init_cache(decoder_input_ids.shape[0],		__lowerCAmelCase,		__lowerCAmelCase						)
       UpperCAmelCase	:							Optional[Any]						=			jnp.ones((decoder_input_ids.shape[0], max_decoder_length),		dtype='''i4'''						)

       UpperCAmelCase	:							Union[str, Any]						=			jnp.broadcast_to(
           jnp.arange(decoder_input_ids.shape[-1] - 1						)[None, :],		(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1),		)
       UpperCAmelCase	:							Dict						=			model.decode(
           decoder_input_ids[:, :-1],		__lowerCAmelCase,		decoder_attention_mask=__lowerCAmelCase,		past_key_values=__lowerCAmelCase,		decoder_position_ids=__lowerCAmelCase,		)

       UpperCAmelCase	:							List[str]						=			jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]],		dtype='''i4'''						)
       UpperCAmelCase	:							List[str]						=			model.decode(
           decoder_input_ids[:, -1:],		__lowerCAmelCase,		decoder_attention_mask=__lowerCAmelCase,		past_key_values=outputs_cache.past_key_values,		decoder_position_ids=__lowerCAmelCase,		)

       UpperCAmelCase	:							Tuple						=			model.decode(__lowerCAmelCase,		__lowerCAmelCase						)

       UpperCAmelCase	:							int						=			np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])						)						)
       self.parent.assertTrue(diff < 1E-3,		msg=F'''Max diff is {diff}'''						)





     def 	__magic_name__   (							self							:					List[Any],		__A							:					Union[str, Any],		__A							:					int,		__A							:					str						):
       UpperCAmelCase	:							List[Any]						=			2_0
       UpperCAmelCase	:							List[str]						=			model_class_name(__lowerCAmelCase						)

       UpperCAmelCase	:							Union[str, Any]						=			model.encode(inputs_dict['''input_ids''']						)

       UpperCAmelCase			,						UpperCAmelCase	:							List[str]						=			(
           inputs_dict['''decoder_input_ids'''],
           inputs_dict['''decoder_attention_mask'''],
       )

       UpperCAmelCase	:							List[str]						=			jnp.concatenate(
           [
               decoder_attention_mask,
               jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])						),
           ],		axis=-1,		)

       UpperCAmelCase	:							Optional[Any]						=			model.init_cache(decoder_input_ids.shape[0],		__lowerCAmelCase,		__lowerCAmelCase						)
       UpperCAmelCase	:							Optional[int]						=			jnp.broadcast_to(
           jnp.arange(decoder_input_ids.shape[-1] - 1						)[None, :],		(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1),		)

       UpperCAmelCase	:							str						=			model.decode(
           decoder_input_ids[:, :-1],		__lowerCAmelCase,		decoder_attention_mask=__lowerCAmelCase,		past_key_values=__lowerCAmelCase,		decoder_position_ids=__lowerCAmelCase,		)
       UpperCAmelCase	:							Optional[int]						=			jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]],		dtype='''i4'''						)
       UpperCAmelCase	:							Optional[int]						=			model.decode(
           decoder_input_ids[:, -1:],		__lowerCAmelCase,		past_key_values=outputs_cache.past_key_values,		decoder_attention_mask=__lowerCAmelCase,		decoder_position_ids=__lowerCAmelCase,		)

       UpperCAmelCase	:							int						=			model.decode(__lowerCAmelCase,		__lowerCAmelCase,		decoder_attention_mask=__lowerCAmelCase						)

       UpperCAmelCase	:							List[Any]						=			np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])						)						)
       self.parent.assertTrue(diff < 1E-3,		msg=F'''Max diff is {diff}'''						)






@require_flax
class  __UpperCAmelCase					(	unittest.TestCase						):
     UpperCamelCase       					=					9_9



     def 	__magic_name__   (							self							:					Tuple						):
       UpperCAmelCase	:							Any						=			np.array(
           [
               [7_1, 8_2, 1_8, 3_3, 4_6, 9_1, 2],
               [6_8, 3_4, 2_6, 5_8, 3_0, 8_2, 2],
               [5, 9_7, 1_7, 3_9, 9_4, 4_0, 2],
               [7_6, 8_3, 9_4, 2_5, 7_0, 7_8, 2],
               [8_7, 5_9, 4_1, 3_5, 4_8, 6_6, 2],
               [5_5, 1_3, 1_6, 5_8, 5, 2, 1],  # note padding
               [6_4, 2_7, 3_1, 5_1, 1_2, 7_5, 2],
               [5_2, 6_4, 8_6, 1_7, 8_3, 3_9, 2],
               [4_8, 6_1, 9, 2_4, 7_1, 8_2, 2],
               [2_6, 1, 6_0, 4_8, 2_2, 1_3, 2],
               [2_1, 5, 6_2, 2_8, 1_4, 7_6, 2],
               [4_5, 9_8, 3_7, 8_6, 5_9, 4_8, 2],
               [7_0, 7_0, 5_0, 9, 2_8, 0, 2],
           ],		dtype=np.intaa,		)

       UpperCAmelCase	:							Dict						=			input_ids.shape[0]
       UpperCAmelCase	:							Optional[Any]						=			BlenderbotSmallConfig(
           vocab_size=self.vocab_size,		d_model=2_4,		encoder_layers=2,		decoder_layers=2,		encoder_attention_heads=2,		decoder_attention_heads=2,		encoder_ffn_dim=3_2,		decoder_ffn_dim=3_2,		max_position_embeddings=4_8,		eos_token_id=2,		pad_token_id=1,		bos_token_id=0,		)
       return config, input_ids, batch_size



     def 	__magic_name__   (							self							:					Optional[int]						):
       UpperCAmelCase			,						UpperCAmelCase			,						UpperCAmelCase	:							Tuple						=			self._get_config_and_data()
       UpperCAmelCase	:							Dict						=			FlaxBlenderbotSmallForConditionalGeneration(__lowerCAmelCase						)
       UpperCAmelCase	:							str						=			lm_model(input_ids=__lowerCAmelCase						)
       UpperCAmelCase	:							Dict						=			(batch_size, input_ids.shape[1], config.vocab_size)
       self.assertEqual(outputs['''logits'''].shape,		__lowerCAmelCase						)



     def 	__magic_name__   (							self							:					Dict						):
       UpperCAmelCase	:							str						=			BlenderbotSmallConfig(
           vocab_size=self.vocab_size,		d_model=1_4,		encoder_layers=2,		decoder_layers=2,		encoder_attention_heads=2,		decoder_attention_heads=2,		encoder_ffn_dim=8,		decoder_ffn_dim=8,		max_position_embeddings=4_8,		)
       UpperCAmelCase	:							str						=			FlaxBlenderbotSmallForConditionalGeneration(__lowerCAmelCase						)
       UpperCAmelCase	:							Optional[int]						=			np.array([[7_1, 8_2, 1_8, 3_3, 4_6, 9_1, 2], [6_8, 3_4, 2_6, 5_8, 3_0, 2, 1]],		dtype=np.intaa						)
       UpperCAmelCase	:							Union[str, Any]						=			np.array([[8_2, 7_1, 8_2, 1_8, 2], [5_8, 6_8, 2, 1, 1]],		dtype=np.intaa						)
       UpperCAmelCase	:							Optional[int]						=			lm_model(input_ids=__lowerCAmelCase,		decoder_input_ids=__lowerCAmelCase						)
       UpperCAmelCase	:							Optional[int]						=			(*summary.shape, config.vocab_size)
       self.assertEqual(outputs['''logits'''].shape,		__lowerCAmelCase						)





     def 	__magic_name__   (							self							:					Optional[Any]						):
       UpperCAmelCase	:							int						=			np.array([[7_1, 8_2, 1_8, 3_3, 2, 1, 1], [6_8, 3_4, 2_6, 5_8, 3_0, 8_2, 2]],		dtype=np.intaa						)
       UpperCAmelCase	:							Optional[int]						=			shift_tokens_right(__lowerCAmelCase,		1,		2						)
       UpperCAmelCase	:							Union[str, Any]						=			np.equal(__lowerCAmelCase,		1						).astype(np.floataa						).sum()
       UpperCAmelCase	:							List[str]						=			np.equal(__lowerCAmelCase,		1						).astype(np.floataa						).sum()
       self.assertEqual(shifted.shape,		input_ids.shape						)
       self.assertEqual(__lowerCAmelCase,		n_pad_before - 1						)
       self.assertTrue(np.equal(shifted[:, 0],		2						).all()						)






@require_flax
class  __UpperCAmelCase					(	_a      ,  unittest.TestCase      ,  _a						):
     UpperCamelCase       					=					True
     UpperCamelCase       					=					(
         (
             FlaxBlenderbotSmallModel,
             FlaxBlenderbotSmallForConditionalGeneration,
         )
         if is_flax_available()
         else ()
     )
     UpperCamelCase       					=					(FlaxBlenderbotSmallForConditionalGeneration,) if is_flax_available() else ()



     def 	__magic_name__   (							self							:					List[Any]						):
       UpperCAmelCase	:							Tuple						=			FlaxBlenderbotSmallModelTester(self						)



     def 	__magic_name__   (							self							:					Dict						):
       UpperCAmelCase			,						UpperCAmelCase	:							Optional[Any]						=			self.model_tester.prepare_config_and_inputs()
       for model_class in self.all_model_classes:
         self.model_tester.check_use_cache_forward(__lowerCAmelCase,		__lowerCAmelCase,		__lowerCAmelCase						)



     def 	__magic_name__   (							self							:					Union[str, Any]						):
       UpperCAmelCase			,						UpperCAmelCase	:							Tuple						=			self.model_tester.prepare_config_and_inputs()
       for model_class in self.all_model_classes:
         self.model_tester.check_use_cache_forward_with_attn_mask(__lowerCAmelCase,		__lowerCAmelCase,		__lowerCAmelCase						)



     def 	__magic_name__   (							self							:					Union[str, Any]						):
       UpperCAmelCase			,						UpperCAmelCase	:							str						=			self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
         with self.subTest(model_class.__name__						):
           UpperCAmelCase	:							List[Any]						=			self._prepare_for_class(__lowerCAmelCase,		__lowerCAmelCase						)
           UpperCAmelCase	:							str						=			model_class(__lowerCAmelCase						)

           @jax.jit
           def encode_jitted(__A							:					Dict,		__A							:					Union[str, Any]=None,		**__A							:					Any						):
             return model.encode(input_ids=__lowerCAmelCase,		attention_mask=__lowerCAmelCase						)

           with self.subTest('''JIT Enabled'''						):
             UpperCAmelCase	:							Any						=			encode_jitted(**__lowerCAmelCase						).to_tuple()

           with self.subTest('''JIT Disabled'''						):
             with jax.disable_jit():
               UpperCAmelCase	:							Tuple						=			encode_jitted(**__lowerCAmelCase						).to_tuple()

           self.assertEqual(len(__lowerCAmelCase						),		len(__lowerCAmelCase						)						)
           for jitted_output, output in zip(__lowerCAmelCase,		__lowerCAmelCase						):
             self.assertEqual(jitted_output.shape,		output.shape						)



     def 	__magic_name__   (							self							:					Optional[Any]						):
       UpperCAmelCase			,						UpperCAmelCase	:							Union[str, Any]						=			self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
         with self.subTest(model_class.__name__						):
           UpperCAmelCase	:							Tuple						=			model_class(__lowerCAmelCase						)
           UpperCAmelCase	:							List[str]						=			model.encode(inputs_dict['''input_ids'''],		inputs_dict['''attention_mask''']						)

           UpperCAmelCase	:							str						=			{
               '''decoder_input_ids''': inputs_dict['''decoder_input_ids'''],
               '''decoder_attention_mask''': inputs_dict['''decoder_attention_mask'''],
               '''encoder_outputs''': encoder_outputs,
           }

           @jax.jit
           def decode_jitted(__A							:					Optional[Any],		__A							:					Dict,		__A							:					Union[str, Any]						):
             return model.decode(
                 decoder_input_ids=__lowerCAmelCase,		decoder_attention_mask=__lowerCAmelCase,		encoder_outputs=__lowerCAmelCase,		)

           with self.subTest('''JIT Enabled'''						):
             UpperCAmelCase	:							str						=			decode_jitted(**__lowerCAmelCase						).to_tuple()

           with self.subTest('''JIT Disabled'''						):
             with jax.disable_jit():
               UpperCAmelCase	:							int						=			decode_jitted(**__lowerCAmelCase						).to_tuple()

           self.assertEqual(len(__lowerCAmelCase						),		len(__lowerCAmelCase						)						)
           for jitted_output, output in zip(__lowerCAmelCase,		__lowerCAmelCase						):
             self.assertEqual(jitted_output.shape,		output.shape						)





     @slow
     def 	__magic_name__   (							self							:					Any						):
       for model_class_name in self.all_model_classes:
         UpperCAmelCase	:							List[str]						=			model_class_name.from_pretrained('''facebook/blenderbot_small-90M'''						)
         # FlaxBlenderbotForSequenceClassification expects eos token in input_ids
         UpperCAmelCase	:							Optional[int]						=			np.ones((1, 1)						) * model.config.eos_token_id
         UpperCAmelCase	:							List[str]						=			model(__lowerCAmelCase						)
         self.assertIsNotNone(__lowerCAmelCase						)








# Copyright 2021 The HuggingFace 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 argparse
import os

from accelerate.utils import ComputeEnvironment

from .cluster import get_cluster_input
from .config_args import cache_dir, default_config_file, default_yaml_config_file, load_config_from_file  # noqa: F401
from .config_utils import _ask_field, _ask_options, _convert_compute_environment  # noqa: F401
from .sagemaker import get_sagemaker_input


_lowerCamelCase    :       Optional[Any]        =							"Launches a series of prompts to create and save a `default_config.yaml` configuration file for your training system. Should always be ran first on your machine"

def       a__					(		)  ->						List[Any]:
  UpperCAmelCase	:							Dict						=			_ask_options(
      '''In which compute environment are you running?'''    ,			['''This machine''', '''AWS (Amazon SageMaker)''']    ,			_convert_compute_environment    ,			)
  if compute_environment == ComputeEnvironment.AMAZON_SAGEMAKER:
    UpperCAmelCase	:							List[Any]						=			get_sagemaker_input()
  else:
    UpperCAmelCase	:							Optional[Any]						=			get_cluster_input()
  return config

def       a__					(		UpperCAmelCase							:      Union[str, Any]=None      )  ->						List[Any]:
  if subparsers is not None:
    UpperCAmelCase	:							Optional[Any]						=			subparsers.add_parser('''config'''    ,			description=UpperCAmelCase      )
  else:
    UpperCAmelCase	:							List[str]						=			argparse.ArgumentParser('''Accelerate config command'''    ,			description=UpperCAmelCase      )

  parser.add_argument(
      '''--config_file'''    ,			default=UpperCAmelCase    ,			help=(
          '''The path to use to store the config file. Will default to a file named default_config.yaml in the cache '''
          '''location, which is the content of the environment `HF_HOME` suffixed with \'accelerate\', or if you don\'t have '''
          '''such an environment variable, your cache directory (\'~/.cache\' or the content of `XDG_CACHE_HOME`) suffixed '''
          '''with \'huggingface\'.'''
      )    ,			)

  if subparsers is not None:
    parser.set_defaults(func=UpperCAmelCase      )
  return parser

def       a__					(		UpperCAmelCase							:      List[Any]      )  ->						Optional[int]:
  UpperCAmelCase	:							str						=			get_user_input()
  if args.config_file is not None:
    UpperCAmelCase	:							Any						=			args.config_file
  else:
    if not os.path.isdir(UpperCAmelCase      ):
      os.makedirs(UpperCAmelCase      )
    UpperCAmelCase	:							List[str]						=			default_yaml_config_file

  if config_file.endswith('''.json'''      ):
    config.to_json_file(UpperCAmelCase      )
  else:
    config.to_yaml_file(UpperCAmelCase      )
  print(f'''accelerate configuration saved at {config_file}'''      )

def       a__					(		)  ->						Dict:
  UpperCAmelCase	:							str						=			config_command_parser()
  UpperCAmelCase	:							str						=			parser.parse_args()
  config_command(UpperCAmelCase      )


if __name__ == "__main__":
     main()





