






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
  __a      :       List[str]				  =   "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						( __lowercase   :			List[Any]			,				__lowercase   :			List[str]			,				__lowercase   :			str=None			,				__lowercase   :			Any=None			,				__lowercase   :			Optional[Any]=None			,				__lowercase   :			List[str]=None			,				__lowercase   :			Tuple=None			,				__lowercase   :			Tuple=None			,				)						->      List[str]:





    """simple docstring"""

    if attention_mask is None:
        __A         =	np.where(input_ids != config.pad_token_id			,				1			,				0    )
    if decoder_attention_mask is None:
        __A         =	np.where(decoder_input_ids != config.pad_token_id			,				1			,				0    )
    if head_mask is None:
        __A         =	np.ones((config.encoder_layers, config.encoder_attention_heads)    )
    if decoder_head_mask is None:
        __A         =	np.ones((config.decoder_layers, config.decoder_attention_heads)    )
    if cross_attn_head_mask is None:
        __A         =	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      :







     '''simple docstring'''




     def __init__(				self       :			str ,	UpperCamelCase_       :			Dict ,	UpperCamelCase_       :			str=13 ,	UpperCamelCase_       :			Optional[int]=7 ,	UpperCamelCase_       :			List[str]=True ,	UpperCamelCase_       :			Any=False ,	UpperCamelCase_       :			Any=99 ,	UpperCamelCase_       :			List[str]=16 ,	UpperCamelCase_       :			Any=2 ,	UpperCamelCase_       :			Optional[Any]=4 ,	UpperCamelCase_       :			List[str]=4 ,	UpperCamelCase_       :			Tuple="gelu" ,	UpperCamelCase_       :			int=0.1 ,	UpperCamelCase_       :			Union[str, Any]=0.1 ,	UpperCamelCase_       :			Optional[int]=32 ,	UpperCamelCase_       :			Optional[Any]=2 ,	UpperCamelCase_       :			int=1 ,	UpperCamelCase_       :			str=0 ,	UpperCamelCase_       :			Any=0.02 ,	):


         """simple docstring"""



         __A         =	parent
         __A         =	batch_size
         __A         =	seq_length
         __A         =	is_training
         __A         =	use_labels
         __A         =	vocab_size
         __A         =	hidden_size
         __A         =	num_hidden_layers
         __A         =	num_attention_heads
         __A         =	intermediate_size
         __A         =	hidden_act
         __A         =	hidden_dropout_prob
         __A         =	attention_probs_dropout_prob
         __A         =	max_position_embeddings
         __A         =	eos_token_id
         __A         =	pad_token_id
         __A         =	bos_token_id
         __A         =	initializer_range




     def 		lowerCAmelCase_     (				self       :			Tuple				):


         """simple docstring"""



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

         __A         =	shift_tokens_right(UpperCamelCase_ ,	1 ,	2				)

         __A         =	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=UpperCamelCase_ ,	)
         __A         =	prepare_blenderbot_inputs_dict(UpperCamelCase_ ,	UpperCamelCase_ ,	UpperCamelCase_				)
         return config, inputs_dict




     def 		lowerCAmelCase_     (				self       :			str				):


         """simple docstring"""



         __A    ,	__A         =	self.prepare_config_and_inputs()
         return config, inputs_dict




     def 		lowerCAmelCase_     (				self       :			Optional[Any] ,	UpperCamelCase_       :			int ,	UpperCamelCase_       :			List[Any] ,	UpperCamelCase_       :			Dict				):


         """simple docstring"""



         __A         =	20
         __A         =	model_class_name(UpperCamelCase_				)

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

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

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

         __A         =	jnp.broadcast_to(
             jnp.arange(decoder_input_ids.shape[-1] - 1				)[None, :] ,	(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1) ,	)
         __A         =	model.decode(
             decoder_input_ids[:, :-1] ,	UpperCamelCase_ ,	decoder_attention_mask=UpperCamelCase_ ,	past_key_values=UpperCamelCase_ ,	decoder_position_ids=UpperCamelCase_ ,	)

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

         __A         =	model.decode(UpperCamelCase_ ,	UpperCamelCase_				)

         __A         =	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 		lowerCAmelCase_     (				self       :			List[Any] ,	UpperCamelCase_       :			List[str] ,	UpperCamelCase_       :			Tuple ,	UpperCamelCase_       :			Any				):


         """simple docstring"""



         __A         =	20
         __A         =	model_class_name(UpperCamelCase_				)

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

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

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

         __A         =	model.init_cache(decoder_input_ids.shape[0] ,	UpperCamelCase_ ,	UpperCamelCase_				)
         __A         =	jnp.broadcast_to(
             jnp.arange(decoder_input_ids.shape[-1] - 1				)[None, :] ,	(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1) ,	)

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

         __A         =	model.decode(UpperCamelCase_ ,	UpperCamelCase_ ,	decoder_attention_mask=UpperCamelCase_				)

         __A         =	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  ):







     '''simple docstring'''
     SCREAMING_SNAKE_CASE   						=				99




     def 		lowerCAmelCase_     (				self       :			Dict				):


         """simple docstring"""



         __A         =	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 ,	)

         __A         =	input_ids.shape[0]
         __A         =	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 		lowerCAmelCase_     (				self       :			str				):


         """simple docstring"""



         __A    ,	__A    ,	__A         =	self._get_config_and_data()
         __A         =	FlaxBlenderbotSmallForConditionalGeneration(UpperCamelCase_				)
         __A         =	lm_model(input_ids=UpperCamelCase_				)
         __A         =	(batch_size, input_ids.shape[1], config.vocab_size)
         self.assertEqual(outputs["""logits"""].shape ,	UpperCamelCase_				)




     def 		lowerCAmelCase_     (				self       :			List[Any]				):


         """simple docstring"""



         __A         =	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 ,	)
         __A         =	FlaxBlenderbotSmallForConditionalGeneration(UpperCamelCase_				)
         __A         =	np.array([[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]] ,	dtype=np.intaa				)
         __A         =	np.array([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]] ,	dtype=np.intaa				)
         __A         =	lm_model(input_ids=UpperCamelCase_ ,	decoder_input_ids=UpperCamelCase_				)
         __A         =	(*summary.shape, config.vocab_size)
         self.assertEqual(outputs["""logits"""].shape ,	UpperCamelCase_				)





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


         """simple docstring"""



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


@require_flax
class       __lowercase      (	lowercase_			,      unittest.TestCase			,      lowercase_  ):







     '''simple docstring'''
     SCREAMING_SNAKE_CASE   						=				True
     SCREAMING_SNAKE_CASE   						=				(
         (
             FlaxBlenderbotSmallModel,
             FlaxBlenderbotSmallForConditionalGeneration,
         )
         if is_flax_available()
         else ()
     )
     SCREAMING_SNAKE_CASE   						=				(FlaxBlenderbotSmallForConditionalGeneration,) if is_flax_available() else ()




     def 		lowerCAmelCase_     (				self       :			Tuple				):


         """simple docstring"""



         __A         =	FlaxBlenderbotSmallModelTester(self				)




     def 		lowerCAmelCase_     (				self       :			Any				):


         """simple docstring"""



         __A    ,	__A         =	self.model_tester.prepare_config_and_inputs()
         for model_class in self.all_model_classes:
             self.model_tester.check_use_cache_forward(UpperCamelCase_ ,	UpperCamelCase_ ,	UpperCamelCase_				)




     def 		lowerCAmelCase_     (				self       :			Optional[int]				):


         """simple docstring"""



         __A    ,	__A         =	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(UpperCamelCase_ ,	UpperCamelCase_ ,	UpperCamelCase_				)




     def 		lowerCAmelCase_     (				self       :			Tuple				):


         """simple docstring"""



         __A    ,	__A         =	self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
             with self.subTest(model_class.__name__				):
                 __A         =	self._prepare_for_class(UpperCamelCase_ ,	UpperCamelCase_				)
                 __A         =	model_class(UpperCamelCase_				)

                 @jax.jit
                 def encode_jitted(UpperCamelCase_       :			Tuple ,	UpperCamelCase_       :			Tuple=None ,	**UpperCamelCase_       :			Union[str, Any]				):
                     return model.encode(input_ids=UpperCamelCase_ ,	attention_mask=UpperCamelCase_				)

                 with self.subTest("""JIT Enabled"""				):
                     __A         =	encode_jitted(**UpperCamelCase_				).to_tuple()

                 with self.subTest("""JIT Disabled"""				):
                     with jax.disable_jit():
                         __A         =	encode_jitted(**UpperCamelCase_				).to_tuple()

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




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


         """simple docstring"""



         __A    ,	__A         =	self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
             with self.subTest(model_class.__name__				):
                 __A         =	model_class(UpperCamelCase_				)
                 __A         =	model.encode(inputs_dict["""input_ids"""] ,	inputs_dict["""attention_mask"""]				)

                 __A         =	{
                     """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(UpperCamelCase_       :			Optional[Any] ,	UpperCamelCase_       :			Dict ,	UpperCamelCase_       :			int				):
                     return model.decode(
                         decoder_input_ids=UpperCamelCase_ ,	decoder_attention_mask=UpperCamelCase_ ,	encoder_outputs=UpperCamelCase_ ,	)

                 with self.subTest("""JIT Enabled"""				):
                     __A         =	decode_jitted(**UpperCamelCase_				).to_tuple()

                 with self.subTest("""JIT Disabled"""				):
                     with jax.disable_jit():
                         __A         =	decode_jitted(**UpperCamelCase_				).to_tuple()

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





     @slow
     def 		lowerCAmelCase_     (				self       :			Optional[int]				):


         """simple docstring"""



         for model_class_name in self.all_model_classes:
             __A         =	model_class_name.from_pretrained("""facebook/blenderbot_small-90M"""				)
             # FlaxBlenderbotForSequenceClassification expects eos token in input_ids
             __A         =	np.ones((1, 1)				) * model.config.eos_token_id
             __A         =	model(UpperCamelCase_				)
             self.assertIsNotNone(UpperCamelCase_				)












import platform
from argparse import ArgumentParser

import huggingface_hub

from .. import __version__ as version
from ..utils import is_accelerate_available, is_torch_available, is_transformers_available, is_xformers_available
from . import BaseDiffusersCLICommand



def 			_SCREAMING_SNAKE_CASE						( __lowercase   :			Any    )						->      Optional[int]:





    """simple docstring"""

    return EnvironmentCommand()






class       __lowercase      (	lowercase_  ):







     '''simple docstring'''




     @staticmethod
     def 		lowerCAmelCase_     (				UpperCamelCase_       :			ArgumentParser				):


         """simple docstring"""



         __A         =	parser.add_parser("""env"""				)
         download_parser.set_defaults(func=UpperCamelCase_				)




     def 		lowerCAmelCase_     (				self       :			Any				):


         """simple docstring"""



         __A         =	huggingface_hub.__version__

         __A         =	"""not installed"""
         __A         =	"""NA"""
         if is_torch_available():
             import torch

             __A         =	torch.__version__
             __A         =	torch.cuda.is_available()

         __A         =	"""not installed"""
         if is_transformers_available():
             import transformers

             __A         =	transformers.__version__

         __A         =	"""not installed"""
         if is_accelerate_available():
             import accelerate

             __A         =	accelerate.__version__

         __A         =	"""not installed"""
         if is_xformers_available():
             import xformers

             __A         =	xformers.__version__

         __A         =	{
             """`diffusers` version""": version,
             """Platform""": platform.platform(),
             """Python version""": platform.python_version(),
             """PyTorch version (GPU?)""": F"{pt_version} ({pt_cuda_available})",
             """Huggingface_hub version""": hub_version,
             """Transformers version""": transformers_version,
             """Accelerate version""": accelerate_version,
             """xFormers version""": xformers_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(UpperCamelCase_				)				)

         return info





     @staticmethod
     def 		lowerCAmelCase_     (				UpperCamelCase_       :			str				):


         """simple docstring"""



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




