
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
   UpperCAmelCase__		:     Any     			=      'platform'

   import jax
   import jax.numpy as jnp

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






def   lowerCamelCase__							(					a		,		a		,		a=None		,		a=None		,		a=None		,		a=None		,		a=None		,		a=None		,		)      ->   Any:
   if attention_mask is None:
      _A:  Tuple					=       np.where(input_ids != config.pad_token_id		,		1		,		0      )
   if decoder_attention_mask is None:
      _A:  Optional[Any]					=       np.where(decoder_input_ids != config.pad_token_id		,		1		,		0      )
   if head_mask is None:
      _A:  Dict					=       np.ones((config.encoder_layers, config.encoder_attention_heads)      )
   if decoder_head_mask is None:
      _A:  Tuple					=       np.ones((config.decoder_layers, config.decoder_attention_heads)      )
   if cross_attn_head_mask is None:
      _A:  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   :





   '''simple docstring'''


   def __init__(					self     :							Any			,			lowerCAmelCase_     :							List[str]			,			lowerCAmelCase_     :							Union[str, Any]=1_3			,			lowerCAmelCase_     :							int=7			,			lowerCAmelCase_     :							str=True			,			lowerCAmelCase_     :							List[Any]=False			,			lowerCAmelCase_     :							Dict=9_9			,			lowerCAmelCase_     :							Optional[int]=1_6			,			lowerCAmelCase_     :							List[str]=2			,			lowerCAmelCase_     :							int=4			,			lowerCAmelCase_     :							Union[str, Any]=4			,			lowerCAmelCase_     :							Optional[int]="gelu"			,			lowerCAmelCase_     :							List[str]=0.1			,			lowerCAmelCase_     :							Any=0.1			,			lowerCAmelCase_     :							str=3_2			,			lowerCAmelCase_     :							Any=2			,			lowerCAmelCase_     :							Optional[Any]=1			,			lowerCAmelCase_     :							Dict=0			,			lowerCAmelCase_     :							str=0.02			,			):

      """simple docstring"""
      _A:  Union[str, Any]					=       parent
      _A:  Dict					=       batch_size
      _A:  List[Any]					=       seq_length
      _A:  str					=       is_training
      _A:  Tuple					=       use_labels
      _A:  Any					=       vocab_size
      _A:  Union[str, Any]					=       hidden_size
      _A:  Optional[int]					=       num_hidden_layers
      _A:  Optional[int]					=       num_attention_heads
      _A:  Union[str, Any]					=       intermediate_size
      _A:  List[Any]					=       hidden_act
      _A:  int					=       hidden_dropout_prob
      _A:  int					=       attention_probs_dropout_prob
      _A:  Any					=       max_position_embeddings
      _A:  Union[str, Any]					=       eos_token_id
      _A:  Any					=       pad_token_id
      _A:  Optional[int]					=       bos_token_id
      _A:  str					=       initializer_range


   def  __magic_name__				(					self     :							Tuple     ):

      """simple docstring"""
      _A:  List[str]					=       np.clip(ids_tensor([self.batch_size, self.seq_length - 1]			,			self.vocab_size     )			,			3			,			self.vocab_size     )
      _A:  Dict					=       np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)			,			dtype=np.intaa     ))			,			-1     )

      _A:  List[str]					=       shift_tokens_right(lowerCAmelCase_			,			1			,			2     )

      _A:  Any					=       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_			,			)
      _A:  int					=       prepare_blenderbot_inputs_dict(lowerCAmelCase_			,			lowerCAmelCase_			,			lowerCAmelCase_     )
      return config, inputs_dict


   def  __magic_name__				(					self     :							Dict     ):

      """simple docstring"""
      _A			,  _A:  str					=       self.prepare_config_and_inputs()
      return config, inputs_dict


   def  __magic_name__				(					self     :							Any			,			lowerCAmelCase_     :							str			,			lowerCAmelCase_     :							Any			,			lowerCAmelCase_     :							Union[str, Any]     ):

      """simple docstring"""
      _A:  List[str]					=       2_0
      _A:  List[Any]					=       model_class_name(lowerCAmelCase_     )

      _A:  Optional[Any]					=       model.encode(inputs_dict['''input_ids''']     )

      _A			,  _A:  Optional[int]					=       (
          inputs_dict['''decoder_input_ids'''],
          inputs_dict['''decoder_attention_mask'''],
      )

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

      _A:  List[str]					=       jnp.broadcast_to(
          jnp.arange(decoder_input_ids.shape[-1] - 1     )[None, :]			,			(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)			,			)
      _A:  List[str]					=       model.decode(
          decoder_input_ids[:, :-1]			,			lowerCAmelCase_			,			decoder_attention_mask=lowerCAmelCase_			,			past_key_values=lowerCAmelCase_			,			decoder_position_ids=lowerCAmelCase_			,			)

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

      _A:  List[Any]					=       model.decode(lowerCAmelCase_			,			lowerCAmelCase_     )

      _A:  Optional[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     :							Union[str, Any]			,			lowerCAmelCase_     :							Union[str, Any]			,			lowerCAmelCase_     :							Union[str, Any]			,			lowerCAmelCase_     :							Optional[Any]     ):

      """simple docstring"""
      _A:  int					=       2_0
      _A:  Tuple					=       model_class_name(lowerCAmelCase_     )

      _A:  Optional[Any]					=       model.encode(inputs_dict['''input_ids''']     )

      _A			,  _A:  Dict					=       (
          inputs_dict['''decoder_input_ids'''],
          inputs_dict['''decoder_attention_mask'''],
      )

      _A:  Optional[int]					=       jnp.concatenate(
          [
              decoder_attention_mask,
              jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])     ),
          ]			,			axis=-1			,			)

      _A:  List[str]					=       model.init_cache(decoder_input_ids.shape[0]			,			lowerCAmelCase_			,			lowerCAmelCase_     )
      _A:  List[Any]					=       jnp.broadcast_to(
          jnp.arange(decoder_input_ids.shape[-1] - 1     )[None, :]			,			(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)			,			)

      _A:  Union[str, Any]					=       model.decode(
          decoder_input_ids[:, :-1]			,			lowerCAmelCase_			,			decoder_attention_mask=lowerCAmelCase_			,			past_key_values=lowerCAmelCase_			,			decoder_position_ids=lowerCAmelCase_			,			)
      _A:  Union[str, Any]					=       jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]			,			dtype='''i4'''     )
      _A:  str					=       model.decode(
          decoder_input_ids[:, -1:]			,			lowerCAmelCase_			,			past_key_values=outputs_cache.past_key_values			,			decoder_attention_mask=lowerCAmelCase_			,			decoder_position_ids=lowerCAmelCase_			,			)

      _A:  Any					=       model.decode(lowerCAmelCase_			,			lowerCAmelCase_			,			decoder_attention_mask=lowerCAmelCase_     )

      _A:  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   ):





   '''simple docstring'''
   __UpperCamelCase		:    Tuple 				=    99


   def  __magic_name__				(					self     :							Any     ):

      """simple docstring"""
      _A:  str					=       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			,			)

      _A:  List[str]					=       input_ids.shape[0]
      _A:  str					=       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     :							Dict     ):

      """simple docstring"""
      _A			,  _A			,  _A:  int					=       self._get_config_and_data()
      _A:  Union[str, Any]					=       FlaxBlenderbotSmallForConditionalGeneration(lowerCAmelCase_     )
      _A:  List[str]					=       lm_model(input_ids=lowerCAmelCase_     )
      _A:  List[Any]					=       (batch_size, input_ids.shape[1], config.vocab_size)
      self.assertEqual(outputs['''logits'''].shape			,			lowerCAmelCase_     )


   def  __magic_name__				(					self     :							Union[str, Any]     ):

      """simple docstring"""
      _A:  List[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			,			)
      _A:  Optional[Any]					=       FlaxBlenderbotSmallForConditionalGeneration(lowerCAmelCase_     )
      _A:  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     )
      _A:  List[str]					=       np.array([[8_2, 7_1, 8_2, 1_8, 2], [5_8, 6_8, 2, 1, 1]]			,			dtype=np.intaa     )
      _A:  Dict					=       lm_model(input_ids=lowerCAmelCase_			,			decoder_input_ids=lowerCAmelCase_     )
      _A:  Optional[Any]					=       (*summary.shape, config.vocab_size)
      self.assertEqual(outputs['''logits'''].shape			,			lowerCAmelCase_     )


   def  __magic_name__				(					self     :							Union[str, Any]     ):

      """simple docstring"""
      _A:  Optional[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     )
      _A:  int					=       shift_tokens_right(lowerCAmelCase_			,			1			,			2     )
      _A:  Dict					=       np.equal(lowerCAmelCase_			,			1     ).astype(np.floataa     ).sum()
      _A:  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   (						SCREAMING_SNAKE_CASE__							,							unittest.TestCase							,							SCREAMING_SNAKE_CASE__   ):





   '''simple docstring'''
   __UpperCamelCase		:    Tuple 				=    True
   __UpperCamelCase		:    List[str] 				=    (
       (
           FlaxBlenderbotSmallModel,
           FlaxBlenderbotSmallForConditionalGeneration,
       )
       if is_flax_available()
       else ()
   )
   __UpperCamelCase		:    Tuple 				=    (FlaxBlenderbotSmallForConditionalGeneration,) if is_flax_available() else ()


   def  __magic_name__				(					self     :							Any     ):

      """simple docstring"""
      _A:  Dict					=       FlaxBlenderbotSmallModelTester(self     )


   def  __magic_name__				(					self     :							Union[str, Any]     ):

      """simple docstring"""
      _A			,  _A:  Union[str, 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]     ):

      """simple docstring"""
      _A			,  _A:  int					=       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     :							int     ):

      """simple docstring"""
      _A			,  _A:  List[Any]					=       self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
         with self.subTest(model_class.__name__     ):
            _A:  Optional[Any]					=       self._prepare_for_class(lowerCAmelCase_			,			lowerCAmelCase_     )
            _A:  Dict					=       model_class(lowerCAmelCase_     )

            @jax.jit
            def encode_jitted(lowerCAmelCase_     :							Dict			,			lowerCAmelCase_     :							str=None			,			**lowerCAmelCase_     :							Dict     ):
               return model.encode(input_ids=lowerCAmelCase_			,			attention_mask=lowerCAmelCase_     )

            with self.subTest('''JIT Enabled'''     ):
               _A:  Tuple					=       encode_jitted(**lowerCAmelCase_     ).to_tuple()

            with self.subTest('''JIT Disabled'''     ):
               with jax.disable_jit():
                  _A:  List[Any]					=       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     :							Dict     ):

      """simple docstring"""
      _A			,  _A:  Any					=       self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
         with self.subTest(model_class.__name__     ):
            _A:  Tuple					=       model_class(lowerCAmelCase_     )
            _A:  Any					=       model.encode(inputs_dict['''input_ids''']			,			inputs_dict['''attention_mask''']     )

            _A:  List[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(lowerCAmelCase_     :							List[str]			,			lowerCAmelCase_     :							List[Any]			,			lowerCAmelCase_     :							Optional[Any]     ):
               return model.decode(
                   decoder_input_ids=lowerCAmelCase_			,			decoder_attention_mask=lowerCAmelCase_			,			encoder_outputs=lowerCAmelCase_			,			)

            with self.subTest('''JIT Enabled'''     ):
               _A:  Any					=       decode_jitted(**lowerCAmelCase_     ).to_tuple()

            with self.subTest('''JIT Disabled'''     ):
               with jax.disable_jit():
                  _A:  Union[str, Any]					=       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     :							Union[str, Any]     ):

      """simple docstring"""
      for model_class_name in self.all_model_classes:
         _A:  List[Any]					=       model_class_name.from_pretrained('''facebook/blenderbot_small-90M'''     )
         # FlaxBlenderbotForSequenceClassification expects eos token in input_ids
         _A:  Dict					=       np.ones((1, 1)     ) * model.config.eos_token_id
         _A:  Union[str, Any]					=       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

from .config import config_command_parser
from .config_args import default_config_file, load_config_from_file  # noqa: F401
from .default import default_command_parser
from .update import update_command_parser






def   lowerCamelCase__							(					a=None      )      ->   int:
   _A:  Union[str, Any]					=       argparse.ArgumentParser(add_help=a		,		allow_abbrev=a      )
   # The main config parser
   _A:  str					=       config_command_parser(a      )
   # The subparser to add commands to
   _A:  str					=       config_parser.add_subparsers(title='''subcommands'''		,		dest='''subcommand'''      )

   # Then add other parsers with the parent parser
   default_command_parser(a		,		parents=[parent_parser]      )
   update_command_parser(a		,		parents=[parent_parser]      )

   return config_parser






def   lowerCamelCase__							(					)      ->   Union[str, Any]:
   _A:  Any					=       get_config_parser()
   _A:  Tuple					=       config_parser.parse_args()

   if not hasattr(a		,		'''func'''      ):
      config_parser.print_help()
      exit(1      )

   # Run
   args.func(a      )


if __name__ == "__main__":
   main()



