





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			       =      'platform'

							import jax
							import jax.numpy as jnp

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




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




						"""simple docstring"""







						def __init__(		self      ,							a__      ,							a__=13      ,							a__=7      ,							a__=True      ,							a__=False      ,							a__=99      ,							a__=16      ,							a__=2      ,							a__=4      ,							a__=4      ,							a__="gelu"      ,							a__=0.1      ,							a__=0.1      ,							a__=32      ,							a__=2      ,							a__=1      ,							a__=0      ,							a__=0.02      ,							):



													"""simple docstring"""





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


						def 				__snake_case			(		self):



													"""simple docstring"""





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

													_lowerCamelCase							:     Tuple	=		shift_tokens_right(a__      ,							1      ,							2)

													_lowerCamelCase							:     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=a__      ,							)
													_lowerCamelCase							:     Union[str, Any]	=		prepare_blenderbot_inputs_dict(a__      ,							a__      ,							a__)
													return config, inputs_dict


						def 				__snake_case			(		self):



													"""simple docstring"""





													_lowerCamelCase							:     Union[str, Any]	=		self.prepare_config_and_inputs()
													return config, inputs_dict


						def 				__snake_case			(		self      ,							a__      ,							a__      ,							a__):



													"""simple docstring"""





													_lowerCamelCase							:     Dict	=		20
													_lowerCamelCase							:     Any	=		model_class_name(a__)

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

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

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

													_lowerCamelCase							:     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)      ,							)
													_lowerCamelCase							:     List[str]	=		model.decode(
													    decoder_input_ids[:, :-1]      ,							a__      ,							decoder_attention_mask=a__      ,							past_key_values=a__      ,							decoder_position_ids=a__      ,							)

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

													_lowerCamelCase							:     Dict	=		model.decode(a__      ,							a__)

													_lowerCamelCase							:     Tuple	=		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 				__snake_case			(		self      ,							a__      ,							a__      ,							a__):



													"""simple docstring"""





													_lowerCamelCase							:     int	=		20
													_lowerCamelCase							:     List[Any]	=		model_class_name(a__)

													_lowerCamelCase							:     Dict	=		model.encode(inputs_dict['''input_ids'''])

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

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

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

													_lowerCamelCase							:     Dict	=		model.decode(
													    decoder_input_ids[:, :-1]      ,							a__      ,							decoder_attention_mask=a__      ,							past_key_values=a__      ,							decoder_position_ids=a__      ,							)
													_lowerCamelCase							:     Tuple	=		jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]      ,							dtype='''i4''')
													_lowerCamelCase							:     Any	=		model.decode(
													    decoder_input_ids[:, -1:]      ,							a__      ,							past_key_values=outputs_cache.past_key_values      ,							decoder_attention_mask=a__      ,							decoder_position_ids=a__      ,							)

													_lowerCamelCase							:     Tuple	=		model.decode(a__      ,							a__      ,							decoder_attention_mask=a__)

													_lowerCamelCase							:     str	=		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 			__A       (      unittest.TestCase  ):




						"""simple docstring"""







						UpperCAmelCase__						=      99


						def 				__snake_case			(		self):



													"""simple docstring"""





													_lowerCamelCase							:     Tuple	=		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      ,							)

													_lowerCamelCase							:     Dict	=		input_ids.shape[0]
													_lowerCamelCase							:     Union[str, Any]	=		BlenderbotSmallConfig(
													    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 				__snake_case			(		self):



													"""simple docstring"""





													_lowerCamelCase							:     List[str]	=		self._get_config_and_data()
													_lowerCamelCase							:     Any	=		FlaxBlenderbotSmallForConditionalGeneration(a__)
													_lowerCamelCase							:     Any	=		lm_model(input_ids=a__)
													_lowerCamelCase							:     List[Any]	=		(batch_size, input_ids.shape[1], config.vocab_size)
													self.assertEqual(outputs['''logits'''].shape      ,							a__)


						def 				__snake_case			(		self):



													"""simple docstring"""





													_lowerCamelCase							:     int	=		BlenderbotSmallConfig(
													    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      ,							)
													_lowerCamelCase							:     Tuple	=		FlaxBlenderbotSmallForConditionalGeneration(a__)
													_lowerCamelCase							:     Any	=		np.array([[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]]      ,							dtype=np.intaa)
													_lowerCamelCase							:     Dict	=		np.array([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]]      ,							dtype=np.intaa)
													_lowerCamelCase							:     Optional[int]	=		lm_model(input_ids=a__      ,							decoder_input_ids=a__)
													_lowerCamelCase							:     Tuple	=		(*summary.shape, config.vocab_size)
													self.assertEqual(outputs['''logits'''].shape      ,							a__)



						def 				__snake_case			(		self):



													"""simple docstring"""





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







@require_flax
class 			__A       (      lowerCamelCase__ ,unittest.TestCase ,lowerCamelCase__  ):




						"""simple docstring"""







						UpperCAmelCase__						=      True
						UpperCAmelCase__						=      (
						    (
						        FlaxBlenderbotSmallModel,
						        FlaxBlenderbotSmallForConditionalGeneration,
						    )
						    if is_flax_available()
						    else ()
						)
						UpperCAmelCase__						=      (FlaxBlenderbotSmallForConditionalGeneration,) if is_flax_available() else ()


						def 				__snake_case			(		self):



													"""simple docstring"""





													_lowerCamelCase							:     List[Any]	=		FlaxBlenderbotSmallModelTester(self)


						def 				__snake_case			(		self):



													"""simple docstring"""





													_lowerCamelCase							:     List[str]	=		self.model_tester.prepare_config_and_inputs()
													for model_class in self.all_model_classes:
																				self.model_tester.check_use_cache_forward(a__      ,							a__      ,							a__)


						def 				__snake_case			(		self):



													"""simple docstring"""





													_lowerCamelCase							:     List[str]	=		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(a__      ,							a__      ,							a__)


						def 				__snake_case			(		self):



													"""simple docstring"""





													_lowerCamelCase							:     List[str]	=		self.model_tester.prepare_config_and_inputs_for_common()

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

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

																											with self.subTest('''JIT Enabled'''):
																																		_lowerCamelCase							:     Union[str, Any]	=		encode_jitted(**a__).to_tuple()

																											with self.subTest('''JIT Disabled'''):
																																		with jax.disable_jit():
																																									_lowerCamelCase							:     Dict	=		encode_jitted(**a__).to_tuple()

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


						def 				__snake_case			(		self):



													"""simple docstring"""





													_lowerCamelCase							:     Optional[Any]	=		self.model_tester.prepare_config_and_inputs_for_common()

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

																											_lowerCamelCase							:     Tuple	=		{
																											    '''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__      ,							a__      ,							a__):
																																		return model.decode(
																																		    decoder_input_ids=a__      ,							decoder_attention_mask=a__      ,							encoder_outputs=a__      ,							)

																											with self.subTest('''JIT Enabled'''):
																																		_lowerCamelCase							:     str	=		decode_jitted(**a__).to_tuple()

																											with self.subTest('''JIT Disabled'''):
																																		with jax.disable_jit():
																																									_lowerCamelCase							:     Union[str, Any]	=		decode_jitted(**a__).to_tuple()

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



						@slow
						def 				__snake_case			(		self):



													"""simple docstring"""





													for model_class_name in self.all_model_classes:
																				_lowerCamelCase							:     Optional[int]	=		model_class_name.from_pretrained('''facebook/blenderbot_small-90M''')
																				# FlaxBlenderbotForSequenceClassification expects eos token in input_ids
																				_lowerCamelCase							:     Optional[Any]	=		np.ones((1, 1)) * model.config.eos_token_id
																				_lowerCamelCase							:     Dict	=		model(a__)
																				self.assertIsNotNone(a__)








import importlib.util
import os
import platform
from argparse import ArgumentParser

import huggingface_hub

from .. import __version__ as version
from ..utils import (
    is_accelerate_available,
    is_flax_available,
    is_safetensors_available,
    is_tf_available,
    is_torch_available,
)
from . import BaseTransformersCLICommand




def    __UpperCAmelCase(      lowercase_      ):
       return EnvironmentCommand()




def    __UpperCAmelCase(      lowercase_      ):
       return EnvironmentCommand(args.accelerate_config_file      )







class 			__A       (      lowerCamelCase__  ):




      """simple docstring"""







      @staticmethod
      def 				__snake_case			(		a__):



             """simple docstring"""





             _lowerCamelCase							:     List[Any]	=		parser.add_parser('''env''')
             download_parser.set_defaults(func=a__)
             download_parser.add_argument(
                 '''--accelerate-config_file'''      ,							default=a__      ,							help='''The accelerate config file to use for the default values in the launching script.'''      ,							)
             download_parser.set_defaults(func=a__)


      def __init__(		self      ,							a__      ,							*a__):



             """simple docstring"""





             _lowerCamelCase							:     str	=		accelerate_config_file


      def 				__snake_case			(		self):



             """simple docstring"""





             _lowerCamelCase							:     Union[str, Any]	=		'''not installed'''
             if is_safetensors_available():
                    import safetensors

                    _lowerCamelCase							:     Optional[Any]	=		safetensors.__version__
             elif importlib.util.find_spec('''safetensors''') is not None:
                    import safetensors

                    _lowerCamelCase							:     Optional[int]	=		F"""{safetensors.__version__} but is ignored because of PyTorch version too old."""

             _lowerCamelCase							:     Union[str, Any]	=		'''not installed'''
             _lowerCamelCase							:     Any	=		'''not found'''
             if is_accelerate_available():
                    import accelerate
                    from accelerate.commands.config import default_config_file, load_config_from_file

                    _lowerCamelCase							:     Optional[int]	=		accelerate.__version__
                    # Get the default from the config file.
                    if self._accelerate_config_file is not None or os.path.isfile(a__):
                           _lowerCamelCase							:     Optional[int]	=		load_config_from_file(self._accelerate_config_file).to_dict()

                    _lowerCamelCase							:     str	=		(
                        '''\n'''.join([F"""\t- {prop}: {val}""" for prop, val in accelerate_config.items()])
                        if isinstance(a__      ,							a__)
                        else F"""\t{accelerate_config}"""
                    )

             _lowerCamelCase							:     List[Any]	=		'''not installed'''
             _lowerCamelCase							:     Tuple	=		'''NA'''
             if is_torch_available():
                    import torch

                    _lowerCamelCase							:     int	=		torch.__version__
                    _lowerCamelCase							:     List[str]	=		torch.cuda.is_available()

             _lowerCamelCase							:     str	=		'''not installed'''
             _lowerCamelCase							:     Union[str, Any]	=		'''NA'''
             if is_tf_available():
                    import tensorflow as tf

                    _lowerCamelCase							:     List[str]	=		tf.__version__
                    try:
                           # deprecated in v2.1
                           _lowerCamelCase							:     Optional[int]	=		tf.test.is_gpu_available()
                    except AttributeError:
                           # returns list of devices, convert to bool
                           _lowerCamelCase							:     Optional[int]	=		bool(tf.config.list_physical_devices('''GPU'''))

             _lowerCamelCase							:     str	=		'''not installed'''
             _lowerCamelCase							:     List[Any]	=		'''not installed'''
             _lowerCamelCase							:     List[Any]	=		'''not installed'''
             _lowerCamelCase							:     Optional[int]	=		'''NA'''
             if is_flax_available():
                    import flax
                    import jax
                    import jaxlib

                    _lowerCamelCase							:     Any	=		flax.__version__
                    _lowerCamelCase							:     str	=		jax.__version__
                    _lowerCamelCase							:     Any	=		jaxlib.__version__
                    _lowerCamelCase							:     int	=		jax.lib.xla_bridge.get_backend().platform

             _lowerCamelCase							:     int	=		{
                 '''`transformers` version''': version,
                 '''Platform''': platform.platform(),
                 '''Python version''': platform.python_version(),
                 '''Huggingface_hub version''': huggingface_hub.__version__,
                 '''Safetensors version''': F"""{safetensors_version}""",
                 '''Accelerate version''': F"""{accelerate_version}""",
                 '''Accelerate config''': F"""{accelerate_config_str}""",
                 '''PyTorch version (GPU?)''': F"""{pt_version} ({pt_cuda_available})""",
                 '''Tensorflow version (GPU?)''': F"""{tf_version} ({tf_cuda_available})""",
                 '''Flax version (CPU?/GPU?/TPU?)''': F"""{flax_version} ({jax_backend})""",
                 '''Jax version''': F"""{jax_version}""",
                 '''JaxLib version''': F"""{jaxlib_version}""",
                 '''Using GPU in script?''': '''<fill in>''',
                 '''Using distributed or parallel set-up in script?''': '''<fill in>''',
             }

             print('''\nCopy-and-paste the text below in your GitHub issue and FILL OUT the two last points.\n''')
             print(self.format_dict(a__))

             return info



      @staticmethod
      def 				__snake_case			(		a__):



             """simple docstring"""





             return "\n".join([F"""- {prop}: {val}""" for prop, val in d.items()]) + "\n"

