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
				__SCREAMING_SNAKE_CASE						:   Union[str, 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  snake_case						(__lowercase ,      __lowercase ,      __lowercase=None ,      __lowercase=None ,      __lowercase=None ,      __lowercase=None ,      __lowercase=None ,      __lowercase=None ,      )	->	Union[str, Any]:

						'''simple docstring'''



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



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



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

													_snake_case						:					Dict    =   shift_tokens_right(lowercase_				,    1				,    2    )

													_snake_case						:					Dict    =   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=lowercase_				,    )
													_snake_case						:					str    =   prepare_blenderbot_inputs_dict(lowercase_				,    lowercase_				,    lowercase_    )
													return config, inputs_dict



							def 	UpperCamelCase	(						self    ):
													_snake_case						:					Optional[int]    =   self.prepare_config_and_inputs()
													return config, inputs_dict



							def 	UpperCamelCase	(						self				,    lowercase_				,    lowercase_				,    lowercase_    ):
													_snake_case						:					Optional[int]    =   20
													_snake_case						:					List[str]    =   model_class_name(lowercase_    )

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

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

													_snake_case						:					List[Any]    =   model.init_cache(decoder_input_ids.shape[0]				,    lowercase_				,    lowercase_    )
													_snake_case						:					Any    =   jnp.ones((decoder_input_ids.shape[0], max_decoder_length)				,    dtype="i4"    )

													_snake_case						:					Dict    =   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						:					Optional[int]    =   model.decode(
													    decoder_input_ids[:, :-1]				,    lowercase_				,    decoder_attention_mask=lowercase_				,    past_key_values=lowercase_				,    decoder_position_ids=lowercase_				,    )

													_snake_case						:					str    =   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_				,    decoder_attention_mask=lowercase_				,    past_key_values=outputs_cache.past_key_values				,    decoder_position_ids=lowercase_				,    )

													_snake_case						:					str    =   model.decode(lowercase_				,    lowercase_    )

													_snake_case						:					List[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}"""    )



							def 	UpperCamelCase	(						self				,    lowercase_				,    lowercase_				,    lowercase_    ):
													_snake_case						:					Union[str, Any]    =   20
													_snake_case						:					List[str]    =   model_class_name(lowercase_    )

													_snake_case						:					Optional[int]    =   model.encode(inputs_dict["input_ids"]    )

													_snake_case						:					List[Any]    =   (
													    inputs_dict["decoder_input_ids"],
													    inputs_dict["decoder_attention_mask"],
													)

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

													_snake_case						:					int    =   model.init_cache(decoder_input_ids.shape[0]				,    lowercase_				,    lowercase_    )
													_snake_case						:					Dict    =   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						:					Any    =   model.decode(
													    decoder_input_ids[:, :-1]				,    lowercase_				,    decoder_attention_mask=lowercase_				,    past_key_values=lowercase_				,    decoder_position_ids=lowercase_				,    )
													_snake_case						:					List[Any]    =   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_				,    past_key_values=outputs_cache.past_key_values				,    decoder_attention_mask=lowercase_				,    decoder_position_ids=lowercase_				,    )

													_snake_case						:					Any    =   model.decode(lowercase_				,    lowercase_				,    decoder_attention_mask=lowercase_    )

													_snake_case						:					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        lowercase_   (					unittest.TestCase		):
							_lowerCamelCase						=		99



							def 	UpperCamelCase	(						self    ):
													_snake_case						:					Any    =   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						:					Optional[int]    =   input_ids.shape[0]
													_snake_case						:					Tuple    =   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 	UpperCamelCase	(						self    ):
													_snake_case						:					List[Any]    =   self._get_config_and_data()
													_snake_case						:					int    =   FlaxBlenderbotSmallForConditionalGeneration(lowercase_    )
													_snake_case						:					int    =   lm_model(input_ids=lowercase_    )
													_snake_case						:					List[str]    =   (batch_size, input_ids.shape[1], config.vocab_size)
													self.assertEqual(outputs["logits"].shape				,    lowercase_    )



							def 	UpperCamelCase	(						self    ):
													_snake_case						:					Tuple    =   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				,    )
													_snake_case						:					Union[str, Any]    =   FlaxBlenderbotSmallForConditionalGeneration(lowercase_    )
													_snake_case						:					Tuple    =   np.array([[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]]				,    dtype=np.intaa    )
													_snake_case						:					Dict    =   np.array([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]]				,    dtype=np.intaa    )
													_snake_case						:					int    =   lm_model(input_ids=lowercase_				,    decoder_input_ids=lowercase_    )
													_snake_case						:					List[Any]    =   (*summary.shape, config.vocab_size)
													self.assertEqual(outputs["logits"].shape				,    lowercase_    )



							def 	UpperCamelCase	(						self    ):
													_snake_case						:					List[str]    =   np.array([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]]				,    dtype=np.intaa    )
													_snake_case						:					Any    =   shift_tokens_right(lowercase_				,    1				,    2    )
													_snake_case						:					int    =   np.equal(lowercase_				,    1    ).astype(np.floataa    ).sum()
													_snake_case						:					Tuple    =   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        lowercase_   (					__snake_case						,			unittest.TestCase						,			__snake_case		):
							_lowerCamelCase						=		True
							_lowerCamelCase						=		(
							    (
							        FlaxBlenderbotSmallModel,
							        FlaxBlenderbotSmallForConditionalGeneration,
							    )
							    if is_flax_available()
							    else ()
							)
							_lowerCamelCase						=		(FlaxBlenderbotSmallForConditionalGeneration,) if is_flax_available() else ()



							def 	UpperCamelCase	(						self    ):
													_snake_case						:					List[str]    =   FlaxBlenderbotSmallModelTester(self    )



							def 	UpperCamelCase	(						self    ):
													_snake_case						:					str    =   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    ):
													_snake_case						:					Optional[Any]    =   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    ):
													_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[Any]    =   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						:					List[Any]    =   encode_jitted(**lowercase_    ).to_tuple()

																									with self.subTest("JIT Disabled"    ):
																															with jax.disable_jit():
																																					_snake_case						:					Dict    =   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    ):
													_snake_case						:					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__    ):
																									_snake_case						:					List[str]    =   model_class(lowercase_    )
																									_snake_case						:					Optional[int]    =   model.encode(inputs_dict["input_ids"]				,    inputs_dict["attention_mask"]    )

																									_snake_case						:					Union[str, 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						:					Tuple    =   decode_jitted(**lowercase_    ).to_tuple()

																									with self.subTest("JIT Disabled"    ):
																															with jax.disable_jit():
																																					_snake_case						:					List[str]    =   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    ):
													for model_class_name in self.all_model_classes:
																			_snake_case						:					Optional[int]    =   model_class_name.from_pretrained("facebook/blenderbot_small-90M"    )
																			# FlaxBlenderbotForSequenceClassification expects eos token in input_ids
																			_snake_case						:					Any    =   np.ones((1, 1)    ) * model.config.eos_token_id
																			_snake_case						:					Union[str, Any]    =   model(lowercase_    )
																			self.assertIsNotNone(lowercase_    )






# 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

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


__SCREAMING_SNAKE_CASE						:   Any         =			{'configuration_mra': ['MRA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MraConfig']}

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__SCREAMING_SNAKE_CASE						:   Optional[Any]         =			[
				    'MRA_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'MraForMaskedLM',
				    'MraForMultipleChoice',
				    'MraForQuestionAnswering',
				    'MraForSequenceClassification',
				    'MraForTokenClassification',
				    'MraLayer',
				    'MraModel',
				    'MraPreTrainedModel',
				]


if TYPE_CHECKING:
				from .configuration_mra import MRA_PRETRAINED_CONFIG_ARCHIVE_MAP, MraConfig

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_mra import (
								    MRA_PRETRAINED_MODEL_ARCHIVE_LIST,
								    MraForMaskedLM,
								    MraForMultipleChoice,
								    MraForQuestionAnswering,
								    MraForSequenceClassification,
								    MraForTokenClassification,
								    MraLayer,
								    MraModel,
								    MraPreTrainedModel,
								)
else:
				import sys

				__SCREAMING_SNAKE_CASE						:   str         =			_LazyModule(__name__, globals()['__file__'], _import_structure)