





'''simple docstring'''

import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import BlenderbotConfig, is_flax_available
from transformers.testing_utils import jax_device, 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					:		Optional[Any]		=							'platform'

			import jax
			import jax.numpy as jnp

			from transformers import BlenderbotTokenizer
			from transformers.models.blenderbot.modeling_flax_blenderbot import (
			    FlaxBlenderbotForConditionalGeneration,
			    FlaxBlenderbotModel,
			    shift_tokens_right,
			)




def    _a    (							lowerCAmelCase_			,      lowerCAmelCase_			,      lowerCAmelCase_=None			,      lowerCAmelCase_=None			,      lowerCAmelCase_=None			,      lowerCAmelCase_=None			,      lowerCAmelCase_=None			,      lowerCAmelCase_=None			,      ):





						"""simple docstring"""



						if attention_mask is None:
												_snake_case		:	Optional[Any]					     =     np.where(input_ids != config.pad_token_id			,      1			,      0	)
						if decoder_attention_mask is None:
												_snake_case		:	Union[str, Any]					     =     np.where(decoder_input_ids != config.pad_token_id			,      1			,      0	)
						if head_mask is None:
												_snake_case		:	Dict					     =     np.ones((config.encoder_layers, config.encoder_attention_heads)	)
						if decoder_head_mask is None:
												_snake_case		:	List[Any]					     =     np.ones((config.decoder_layers, config.decoder_attention_heads)	)
						if cross_attn_head_mask is None:
												_snake_case		:	Any					     =     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 lowerCamelCase						:







		def __init__(		self     ,     lowercase__     ,     lowercase__=13     ,     lowercase__=7     ,     lowercase__=True     ,     lowercase__=False     ,     lowercase__=99     ,     lowercase__=16     ,     lowercase__=2     ,     lowercase__=4     ,     lowercase__=4     ,     lowercase__="gelu"     ,     lowercase__=0.1     ,     lowercase__=0.1     ,     lowercase__=32     ,     lowercase__=2     ,     lowercase__=1     ,     lowercase__=0     ,     lowercase__=0.02     ,     )		->						Union[str, Any]:






								"""simple docstring"""





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







		def 				UpperCAmelCase_				(		self		)		->						List[Any]:






								"""simple docstring"""





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

								_snake_case		:	Any					     =     shift_tokens_right(lowercase__     ,     1     ,     2		)

								_snake_case		:	Any					     =     BlenderbotConfig(
								    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=lowercase__     ,     )
								_snake_case		:	List[Any]					     =     prepare_blenderbot_inputs_dict(lowercase__     ,     lowercase__     ,     lowercase__		)
								return config, inputs_dict







		def 				UpperCAmelCase_				(		self		)		->						Optional[Any]:






								"""simple docstring"""





								_snake_case		:	Any					     =     self.prepare_config_and_inputs()
								return config, inputs_dict







		def 				UpperCAmelCase_				(		self     ,     lowercase__     ,     lowercase__     ,     lowercase__		)		->						Optional[Any]:






								"""simple docstring"""





								_snake_case		:	Union[str, Any]					     =     20
								_snake_case		:	int					     =     model_class_name(lowercase__		)

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

								_snake_case		:	Optional[Any]					     =     (
								    inputs_dict['''decoder_input_ids'''],
								    inputs_dict['''decoder_attention_mask'''],
								)

								_snake_case		:	int					     =     model.init_cache(decoder_input_ids.shape[0]     ,     lowercase__     ,     lowercase__		)
								_snake_case		:	Dict					     =     jnp.ones((decoder_input_ids.shape[0], max_decoder_length)     ,     dtype='''i4'''		)

								_snake_case		:	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)     ,     )
								_snake_case		:	List[Any]					     =     model.decode(
								    decoder_input_ids[:, :-1]     ,     lowercase__     ,     decoder_attention_mask=lowercase__     ,     past_key_values=lowercase__     ,     decoder_position_ids=lowercase__     ,     )

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

								_snake_case		:	Optional[Any]					     =     model.decode(lowercase__     ,     lowercase__		)

								_snake_case		:	Optional[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}'''		)







		def 				UpperCAmelCase_				(		self     ,     lowercase__     ,     lowercase__     ,     lowercase__		)		->						Tuple:






								"""simple docstring"""





								_snake_case		:	Dict					     =     20
								_snake_case		:	Any					     =     model_class_name(lowercase__		)

								_snake_case		:	List[Any]					     =     model.encode(inputs_dict['''input_ids''']		)

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

								_snake_case		:	Tuple					     =     jnp.concatenate(
								    [
								        decoder_attention_mask,
								        jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])		),
								    ]     ,     axis=-1     ,     )

								_snake_case		:	Tuple					     =     model.init_cache(decoder_input_ids.shape[0]     ,     lowercase__     ,     lowercase__		)
								_snake_case		:	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)     ,     )

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

								_snake_case		:	Optional[Any]					     =     model.decode(lowercase__     ,     lowercase__     ,     decoder_attention_mask=lowercase__		)

								_snake_case		:	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}'''		)





@require_flax
class lowerCamelCase						(unittest.TestCase					):
		_lowercase  :	Optional[Any]       					=				99







		def 				UpperCAmelCase_				(		self		)		->						List[Any]:






								"""simple docstring"""





								_snake_case		:	Dict					     =     np.array(
								    [
								        [71, 82, 18, 33, 46, 91, 2],
								        [68, 34, 26, 58, 30, 82, 2],
								        [5, 97, 17, 39, 94, 40, 2],
								        [76, 83, 94, 25, 70, 78, 2],
								        [87, 59, 41, 35, 48, 66, 2],
								        [55, 13, 16, 58, 5, 2, 1],  # note padding
								        [64, 27, 31, 51, 12, 75, 2],
								        [52, 64, 86, 17, 83, 39, 2],
								        [48, 61, 9, 24, 71, 82, 2],
								        [26, 1, 60, 48, 22, 13, 2],
								        [21, 5, 62, 28, 14, 76, 2],
								        [45, 98, 37, 86, 59, 48, 2],
								        [70, 70, 50, 9, 28, 0, 2],
								    ]     ,     dtype=np.intaa     ,     )

								_snake_case		:	Union[str, Any]					     =     input_ids.shape[0]
								_snake_case		:	Union[str, Any]					     =     BlenderbotConfig(
								    vocab_size=self.vocab_size     ,     d_model=24     ,     encoder_layers=2     ,     decoder_layers=2     ,     encoder_attention_heads=2     ,     decoder_attention_heads=2     ,     encoder_ffn_dim=32     ,     decoder_ffn_dim=32     ,     max_position_embeddings=48     ,     eos_token_id=2     ,     pad_token_id=1     ,     bos_token_id=0     ,     )
								return config, input_ids, batch_size







		def 				UpperCAmelCase_				(		self		)		->						Optional[Any]:






								"""simple docstring"""





								_snake_case		:	Tuple					     =     self._get_config_and_data()
								_snake_case		:	Dict					     =     FlaxBlenderbotForConditionalGeneration(lowercase__		)
								_snake_case		:	List[Any]					     =     lm_model(input_ids=lowercase__		)
								_snake_case		:	Optional[Any]					     =     (batch_size, input_ids.shape[1], config.vocab_size)
								self.assertEqual(outputs['''logits'''].shape     ,     lowercase__		)







		def 				UpperCAmelCase_				(		self		)		->						int:






								"""simple docstring"""





								_snake_case		:	Any					     =     BlenderbotConfig(
								    vocab_size=self.vocab_size     ,     d_model=14     ,     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=48     ,     )
								_snake_case		:	Optional[Any]					     =     FlaxBlenderbotForConditionalGeneration(lowercase__		)
								_snake_case		:	Dict					     =     np.array([[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]]     ,     dtype=np.intaa		)
								_snake_case		:	Union[str, Any]					     =     np.array([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]]     ,     dtype=np.intaa		)
								_snake_case		:	List[str]					     =     lm_model(input_ids=lowercase__     ,     decoder_input_ids=lowercase__		)
								_snake_case		:	Any					     =     (*summary.shape, config.vocab_size)
								self.assertEqual(outputs['''logits'''].shape     ,     lowercase__		)







		def 				UpperCAmelCase_				(		self		)		->						Any:






								"""simple docstring"""





								_snake_case		:	Any					     =     np.array([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]]     ,     dtype=np.intaa		)
								_snake_case		:	Optional[int]					     =     shift_tokens_right(lowercase__     ,     1     ,     2		)
								_snake_case		:	Optional[int]					     =     np.equal(lowercase__     ,     1		).astype(np.floataa		).sum()
								_snake_case		:	Dict					     =     np.equal(lowercase__     ,     1		).astype(np.floataa		).sum()
								self.assertEqual(shifted.shape     ,     input_ids.shape		)
								self.assertEqual(lowercase__     ,     n_pad_before - 1		)
								self.assertTrue(np.equal(shifted[:, 0]     ,     2		).all()		)





@require_flax
class lowerCamelCase						(a__						,      unittest.TestCase						,      a__					):
		_lowercase  :	Optional[Any]       					=				True
		_lowercase  :	List[str]       					=				(
		    (
		        FlaxBlenderbotModel,
		        FlaxBlenderbotForConditionalGeneration,
		    )
		    if is_flax_available()
		    else ()
		)
		_lowercase  :	Tuple       					=				(FlaxBlenderbotForConditionalGeneration,) if is_flax_available() else ()







		def 				UpperCAmelCase_				(		self		)		->						Tuple:






								"""simple docstring"""





								_snake_case		:	Any					     =     FlaxBlenderbotModelTester(self		)







		def 				UpperCAmelCase_				(		self		)		->						List[str]:






								"""simple docstring"""





								_snake_case		:	Optional[int]					     =     self.model_tester.prepare_config_and_inputs()
								for model_class in self.all_model_classes:
														self.model_tester.check_use_cache_forward(lowercase__     ,     lowercase__     ,     lowercase__		)







		def 				UpperCAmelCase_				(		self		)		->						Optional[int]:






								"""simple docstring"""





								_snake_case		:	Optional[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(lowercase__     ,     lowercase__     ,     lowercase__		)







		def 				UpperCAmelCase_				(		self		)		->						Optional[Any]:






								"""simple docstring"""





								_snake_case		:	int					     =     self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
														with self.subTest(model_class.__name__		):
																				_snake_case		:	Dict					     =     self._prepare_for_class(lowercase__     ,     lowercase__		)
																				_snake_case		:	List[str]					     =     model_class(lowercase__		)

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

																				with self.subTest('''JIT Enabled'''		):
																										_snake_case		:	Tuple					     =     encode_jitted(**lowercase__		).to_tuple()

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

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







		def 				UpperCAmelCase_				(		self		)		->						List[Any]:






								"""simple docstring"""





								_snake_case		:	Dict					     =     self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
														with self.subTest(model_class.__name__		):
																				_snake_case		:	Optional[int]					     =     model_class(lowercase__		)
																				_snake_case		:	Tuple					     =     model.encode(inputs_dict['''input_ids''']     ,     inputs_dict['''attention_mask''']		)

																				_snake_case		:	List[Any]					     =     {
																				    '''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(lowercase__     ,     lowercase__     ,     lowercase__		):
																										return model.decode(
																										    decoder_input_ids=lowercase__     ,     decoder_attention_mask=lowercase__     ,     encoder_outputs=lowercase__     ,     )

																				with self.subTest('''JIT Enabled'''		):
																										_snake_case		:	List[str]					     =     decode_jitted(**lowercase__		).to_tuple()

																				with self.subTest('''JIT Disabled'''		):
																										with jax.disable_jit():
																																_snake_case		:	Any					     =     decode_jitted(**lowercase__		).to_tuple()

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







		@slow
		def 				UpperCAmelCase_				(		self		)		->						Tuple:






								"""simple docstring"""





								for model_class_name in self.all_model_classes:
														_snake_case		:	Dict					     =     model_class_name.from_pretrained('''facebook/blenderbot-400M-distill'''		)
														# FlaxBlenderbotForSequenceClassification expects eos token in input_ids
														_snake_case		:	List[Any]					     =     np.ones((1, 1)		) * model.config.eos_token_id
														_snake_case		:	Union[str, Any]					     =     model(lowercase__		)
														self.assertIsNotNone(lowercase__		)







		@unittest.skipUnless(jax_device != '''cpu'''     ,     '''3B test too slow on CPU.'''		)
		@slow
		def 				UpperCAmelCase_				(		self		)		->						List[str]:






								"""simple docstring"""





								_snake_case		:	str					     =     {'''num_beams''': 1, '''early_stopping''': True, '''min_length''': 15, '''max_length''': 25}
								_snake_case		:	Tuple					     =     {'''skip_special_tokens''': True, '''clean_up_tokenization_spaces''': True}

								_snake_case		:	Optional[Any]					     =     FlaxBlenderbotForConditionalGeneration.from_pretrained('''facebook/blenderbot-3B'''     ,     from_pt=lowercase__		)
								_snake_case		:	int					     =     BlenderbotTokenizer.from_pretrained('''facebook/blenderbot-3B'''		)

								_snake_case		:	Any					     =     ['''Sam''']
								_snake_case		:	List[str]					     =     tokenizer(lowercase__     ,     return_tensors='''jax'''		)

								_snake_case		:	Union[str, Any]					     =     model.generate(**lowercase__     ,     **lowercase__		)
								_snake_case		:	Tuple					     =     '''Sam is a great name. It means "sun" in Gaelic.'''

								_snake_case		:	Dict					     =     tokenizer.batch_decode(lowercase__     ,     **lowercase__		)
								assert generated_txt[0].strip() == tgt_text









'''simple docstring'''

# Copyright 2023 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.
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


UpperCAmelCase					:		Dict		=							{'configuration_timm_backbone': ['TimmBackboneConfig']}


try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   UpperCAmelCase					:		Union[str, Any]		=							['TimmBackbone']


if TYPE_CHECKING:
   from .configuration_timm_backbone import TimmBackboneConfig

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_timm_backbone import TimmBackbone

else:
   import sys

   UpperCAmelCase					:		Dict		=							_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)


