
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
     __magic_name__  							=	'''platform'''

     import jax
     import jax.numpy as jnp

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





def 						SCREAMING_SNAKE_CASE__     (   __lowerCAmelCase     ,		__lowerCAmelCase     ,		__lowerCAmelCase=None     ,		__lowerCAmelCase=None     ,		__lowerCAmelCase=None     ,		__lowerCAmelCase=None     ,		__lowerCAmelCase=None     ,		__lowerCAmelCase=None     ,		):
    if attention_mask is None:
        snake_case__    =					np.where(input_ids != config.pad_token_id     ,		1     ,		0				)
    if decoder_attention_mask is None:
        snake_case__    =					np.where(decoder_input_ids != config.pad_token_id     ,		1     ,		0				)
    if head_mask is None:
        snake_case__    =					np.ones((config.encoder_layers, config.encoder_attention_heads)				)
    if decoder_head_mask is None:
        snake_case__    =					np.ones((config.decoder_layers, config.decoder_attention_heads)				)
    if cross_attn_head_mask is None:
        snake_case__    =					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        _SCREAMING_SNAKE_CASE	:
      def __init__(      self		,						lowerCamelCase		,						lowerCamelCase=13		,						lowerCamelCase=7		,						lowerCamelCase=True		,						lowerCamelCase=False		,						lowerCamelCase=99		,						lowerCamelCase=16		,						lowerCamelCase=2		,						lowerCamelCase=4		,						lowerCamelCase=4		,						lowerCamelCase="gelu"		,						lowerCamelCase=0.1		,						lowerCamelCase=0.1		,						lowerCamelCase=32		,						lowerCamelCase=2		,						lowerCamelCase=1		,						lowerCamelCase=0		,						lowerCamelCase=0.0_2		,						):
          snake_case__    =					parent
          snake_case__    =					batch_size
          snake_case__    =					seq_length
          snake_case__    =					is_training
          snake_case__    =					use_labels
          snake_case__    =					vocab_size
          snake_case__    =					hidden_size
          snake_case__    =					num_hidden_layers
          snake_case__    =					num_attention_heads
          snake_case__    =					intermediate_size
          snake_case__    =					hidden_act
          snake_case__    =					hidden_dropout_prob
          snake_case__    =					attention_probs_dropout_prob
          snake_case__    =					max_position_embeddings
          snake_case__    =					eos_token_id
          snake_case__    =					pad_token_id
          snake_case__    =					bos_token_id
          snake_case__    =					initializer_range
      def A_      (      self			):
          snake_case__    =					np.clip(ids_tensor([self.batch_size, self.seq_length - 1]		,						self.vocab_size			)		,						3		,						self.vocab_size			)
          snake_case__    =					np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)		,						dtype=np.intaa			))		,						-1			)

          snake_case__    =					shift_tokens_right(lowerCamelCase		,						1		,						2			)

          snake_case__    =					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		,						)
          snake_case__    =					prepare_blenderbot_inputs_dict(lowerCamelCase		,						lowerCamelCase		,						lowerCamelCase			)
          return config, inputs_dict
      def A_      (      self			):
          snake_case__    ,       snake_case__    =					self.prepare_config_and_inputs()
          return config, inputs_dict
      def A_      (      self		,						lowerCamelCase		,						lowerCamelCase		,						lowerCamelCase			):
          snake_case__    =					20
          snake_case__    =					model_class_name(lowerCamelCase			)

          snake_case__    =					model.encode(inputs_dict["input_ids"]			)

          snake_case__    ,       snake_case__    =					(
              inputs_dict["decoder_input_ids"],
              inputs_dict["decoder_attention_mask"],
          )

          snake_case__    =					model.init_cache(decoder_input_ids.shape[0]		,						lowerCamelCase		,						lowerCamelCase			)
          snake_case__    =					jnp.ones((decoder_input_ids.shape[0], max_decoder_length)		,						dtype="i4"			)

          snake_case__    =					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__    =					model.decode(
              decoder_input_ids[:, :-1]		,						lowerCamelCase		,						decoder_attention_mask=lowerCamelCase		,						past_key_values=lowerCamelCase		,						decoder_position_ids=lowerCamelCase		,						)

          snake_case__    =					jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]		,						dtype="i4"			)
          snake_case__    =					model.decode(
              decoder_input_ids[:, -1:]		,						lowerCamelCase		,						decoder_attention_mask=lowerCamelCase		,						past_key_values=outputs_cache.past_key_values		,						decoder_position_ids=lowerCamelCase		,						)

          snake_case__    =					model.decode(lowerCamelCase		,						lowerCamelCase			)

          snake_case__    =					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 A_      (      self		,						lowerCamelCase		,						lowerCamelCase		,						lowerCamelCase			):
          snake_case__    =					20
          snake_case__    =					model_class_name(lowerCamelCase			)

          snake_case__    =					model.encode(inputs_dict["input_ids"]			)

          snake_case__    ,       snake_case__    =					(
              inputs_dict["decoder_input_ids"],
              inputs_dict["decoder_attention_mask"],
          )

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

          snake_case__    =					model.init_cache(decoder_input_ids.shape[0]		,						lowerCamelCase		,						lowerCamelCase			)
          snake_case__    =					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__    =					model.decode(
              decoder_input_ids[:, :-1]		,						lowerCamelCase		,						decoder_attention_mask=lowerCamelCase		,						past_key_values=lowerCamelCase		,						decoder_position_ids=lowerCamelCase		,						)
          snake_case__    =					jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]		,						dtype="i4"			)
          snake_case__    =					model.decode(
              decoder_input_ids[:, -1:]		,						lowerCamelCase		,						past_key_values=outputs_cache.past_key_values		,						decoder_attention_mask=lowerCamelCase		,						decoder_position_ids=lowerCamelCase		,						)

          snake_case__    =					model.decode(lowerCamelCase		,						lowerCamelCase		,						decoder_attention_mask=lowerCamelCase			)

          snake_case__    =					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        _SCREAMING_SNAKE_CASE	(   unittest.TestCase			):
      _A    :		Union[str, Any]     						=  99
      def A_      (      self			):
          snake_case__    =					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__    =					input_ids.shape[0]
          snake_case__    =					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 A_      (      self			):
          snake_case__    ,       snake_case__    ,       snake_case__    =					self._get_config_and_data()
          snake_case__    =					FlaxBlenderbotSmallForConditionalGeneration(lowerCamelCase			)
          snake_case__    =					lm_model(input_ids=lowerCamelCase			)
          snake_case__    =					(batch_size, input_ids.shape[1], config.vocab_size)
          self.assertEqual(outputs["logits"].shape		,						lowerCamelCase			)
      def A_      (      self			):
          snake_case__    =					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__    =					FlaxBlenderbotSmallForConditionalGeneration(lowerCamelCase			)
          snake_case__    =					np.array([[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]]		,						dtype=np.intaa			)
          snake_case__    =					np.array([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]]		,						dtype=np.intaa			)
          snake_case__    =					lm_model(input_ids=lowerCamelCase		,						decoder_input_ids=lowerCamelCase			)
          snake_case__    =					(*summary.shape, config.vocab_size)
          self.assertEqual(outputs["logits"].shape		,						lowerCamelCase			)



      def A_      (      self			):
          snake_case__    =					np.array([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]]		,						dtype=np.intaa			)
          snake_case__    =					shift_tokens_right(lowerCamelCase		,						1		,						2			)
          snake_case__    =					np.equal(lowerCamelCase		,						1			).astype(np.floataa			).sum()
          snake_case__    =					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        _SCREAMING_SNAKE_CASE	(   __UpperCamelCase							,       unittest.TestCase							,       __UpperCamelCase			):
      _A    :		Optional[int]     						=  True
      _A    :		int     						=  (
          (
              FlaxBlenderbotSmallModel,
              FlaxBlenderbotSmallForConditionalGeneration,
          )
          if is_flax_available()
          else ()
      )
      _A    :		Union[str, Any]     						=  (FlaxBlenderbotSmallForConditionalGeneration,) if is_flax_available() else ()
      def A_      (      self			):
          snake_case__    =					FlaxBlenderbotSmallModelTester(self			)
      def A_      (      self			):
          snake_case__    ,       snake_case__    =					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 A_      (      self			):
          snake_case__    ,       snake_case__    =					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 A_      (      self			):
          snake_case__    ,       snake_case__    =					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__    =					self._prepare_for_class(lowerCamelCase		,						lowerCamelCase			)
                  snake_case__    =					model_class(lowerCamelCase			)

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

                  with self.subTest("JIT Enabled"			):
                      snake_case__    =					encode_jitted(**lowerCamelCase			).to_tuple()

                  with self.subTest("JIT Disabled"			):
                      with jax.disable_jit():
                          snake_case__    =					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 A_      (      self			):
          snake_case__    ,       snake_case__    =					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__    =					model_class(lowerCamelCase			)
                  snake_case__    =					model.encode(inputs_dict["input_ids"]		,						inputs_dict["attention_mask"]			)

                  snake_case__    =					{
                      "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		,						lowerCamelCase		,						lowerCamelCase			):
                      return model.decode(
                          decoder_input_ids=lowerCamelCase		,						decoder_attention_mask=lowerCamelCase		,						encoder_outputs=lowerCamelCase		,						)

                  with self.subTest("JIT Enabled"			):
                      snake_case__    =					decode_jitted(**lowerCamelCase			).to_tuple()

                  with self.subTest("JIT Disabled"			):
                      with jax.disable_jit():
                          snake_case__    =					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 A_      (      self			):
          for model_class_name in self.all_model_classes:
              snake_case__    =					model_class_name.from_pretrained("facebook/blenderbot_small-90M"			)
              # FlaxBlenderbotForSequenceClassification expects eos token in input_ids
              snake_case__    =					np.ones((1, 1)			) * model.config.eos_token_id
              snake_case__    =					model(lowerCamelCase			)
              self.assertIsNotNone(lowerCamelCase			)





from ...configuration_utils import PretrainedConfig
from ...utils import logging


__magic_name__  							=	logging.get_logger(__name__)

__magic_name__  							=	{
    '''microsoft/biogpt''': '''https://huggingface.co/microsoft/biogpt/resolve/main/config.json''',
    # See all BioGPT models at https://huggingface.co/models?filter=biogpt
}




class        _SCREAMING_SNAKE_CASE	(   __UpperCamelCase			):
      _A    :		Dict     						=  'biogpt'
      def __init__(      self		,						lowerCamelCase=4_23_84		,						lowerCamelCase=10_24		,						lowerCamelCase=24		,						lowerCamelCase=16		,						lowerCamelCase=40_96		,						lowerCamelCase="gelu"		,						lowerCamelCase=0.1		,						lowerCamelCase=0.1		,						lowerCamelCase=10_24		,						lowerCamelCase=0.0_2		,						lowerCamelCase=1e-12		,						lowerCamelCase=True		,						lowerCamelCase=True		,						lowerCamelCase=0.0		,						lowerCamelCase=0.0		,						lowerCamelCase=1		,						lowerCamelCase=0		,						lowerCamelCase=2		,						**lowerCamelCase		,						):
          snake_case__    =					vocab_size
          snake_case__    =					max_position_embeddings
          snake_case__    =					hidden_size
          snake_case__    =					num_hidden_layers
          snake_case__    =					num_attention_heads
          snake_case__    =					intermediate_size
          snake_case__    =					hidden_act
          snake_case__    =					hidden_dropout_prob
          snake_case__    =					attention_probs_dropout_prob
          snake_case__    =					initializer_range
          snake_case__    =					layer_norm_eps
          snake_case__    =					scale_embedding
          snake_case__    =					use_cache
          snake_case__    =					layerdrop
          snake_case__    =					activation_dropout
          super().__init__(pad_token_id=lowerCamelCase		,						bos_token_id=lowerCamelCase		,						eos_token_id=lowerCamelCase		,						**lowerCamelCase			)



