from __future__ import annotations

import unittest

from transformers import BlenderbotSmallConfig, BlenderbotSmallTokenizer, is_tf_available
from transformers.testing_utils import require_tf, require_tokenizers, slow
from transformers.utils import cached_property

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
      import tensorflow as tf

      from transformers import TFAutoModelForSeqaSeqLM, TFBlenderbotSmallForConditionalGeneration, TFBlenderbotSmallModel

@require_tf
class    snake_case       :
  lowercase_								=  BlenderbotSmallConfig
  lowercase_								=  {}
  lowercase_								=  'gelu'




  def __init__(   self  :						List[Any]  ,			a_  :						int  ,			a_  :						Any=13  ,			a_  :						List[str]=7  ,			a_  :						Optional[int]=True  ,			a_  :						Tuple=False  ,			a_  :						Optional[int]=99  ,			a_  :						Tuple=32  ,			a_  :						Union[str, Any]=2  ,			a_  :						Union[str, Any]=4  ,			a_  :						str=37  ,			a_  :						List[Any]=0.1  ,			a_  :						int=0.1  ,			a_  :						List[Any]=20  ,			a_  :						Optional[Any]=2  ,			a_  :						List[str]=1  ,			a_  :						Tuple=0  ,			)->							int:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__					:					int      =		parent
       SCREAMING_SNAKE_CASE__					:					Tuple      =		batch_size
       SCREAMING_SNAKE_CASE__					:					Optional[int]      =		seq_length
       SCREAMING_SNAKE_CASE__					:					Optional[Any]      =		is_training
       SCREAMING_SNAKE_CASE__					:					Tuple      =		use_labels
       SCREAMING_SNAKE_CASE__					:					List[Any]      =		vocab_size
       SCREAMING_SNAKE_CASE__					:					List[Any]      =		hidden_size
       SCREAMING_SNAKE_CASE__					:					List[Any]      =		num_hidden_layers
       SCREAMING_SNAKE_CASE__					:					int      =		num_attention_heads
       SCREAMING_SNAKE_CASE__					:					List[Any]      =		intermediate_size

       SCREAMING_SNAKE_CASE__					:					Tuple      =		hidden_dropout_prob
       SCREAMING_SNAKE_CASE__					:					Union[str, Any]      =		attention_probs_dropout_prob
       SCREAMING_SNAKE_CASE__					:					List[Any]      =		max_position_embeddings
       SCREAMING_SNAKE_CASE__					:					Optional[int]      =		eos_token_id
       SCREAMING_SNAKE_CASE__					:					Any      =		pad_token_id
       SCREAMING_SNAKE_CASE__					:					Optional[int]      =		bos_token_id




  def 						__lowercase(   self  :						Optional[int]					)->							Union[str, Any]:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__					:					Dict      =		ids_tensor([self.batch_size, self.seq_length - 1]  ,			self.vocab_size					)
       SCREAMING_SNAKE_CASE__					:					Tuple      =		tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size					)  ,			1					)
       SCREAMING_SNAKE_CASE__					:					Optional[Any]      =		tf.concat([input_ids, eos_tensor]  ,			axis=1					)

       SCREAMING_SNAKE_CASE__					:					List[Any]      =		ids_tensor([self.batch_size, self.seq_length]  ,			self.vocab_size					)

       SCREAMING_SNAKE_CASE__					:					List[Any]      =		self.config_cls(
           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_ids=[2]  ,			bos_token_id=self.bos_token_id  ,			pad_token_id=self.pad_token_id  ,			decoder_start_token_id=self.pad_token_id  ,			**self.config_updates  ,			)
       SCREAMING_SNAKE_CASE__					:					List[Any]      =		prepare_blenderbot_small_inputs_dict(a_  ,			a_  ,			a_					)
       return config, inputs_dict




  def 						__lowercase(   self  :						Dict  ,			a_  :						Dict  ,			a_  :						str					)->							Union[str, Any]:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__					:					List[Any]      =		TFBlenderbotSmallModel(config=a_					).get_decoder()
       SCREAMING_SNAKE_CASE__					:					Optional[int]      =		inputs_dict['input_ids']

       SCREAMING_SNAKE_CASE__					:					Union[str, Any]      =		input_ids[:1, :]
       SCREAMING_SNAKE_CASE__					:					List[str]      =		inputs_dict['attention_mask'][:1, :]
       SCREAMING_SNAKE_CASE__					:					Any      =		inputs_dict['head_mask']
       SCREAMING_SNAKE_CASE__					:					str      =		1

       # first forward pass
       SCREAMING_SNAKE_CASE__					:					Optional[int]      =		model(a_  ,			attention_mask=a_  ,			head_mask=a_  ,			use_cache=a_					)

       SCREAMING_SNAKE_CASE__      ,				SCREAMING_SNAKE_CASE__					:					List[Any]      =		outputs.to_tuple()

       # create hypothetical next token and extent to next_input_ids
       SCREAMING_SNAKE_CASE__					:					Optional[Any]      =		ids_tensor((self.batch_size, 3)  ,			config.vocab_size					)
       SCREAMING_SNAKE_CASE__					:					List[Any]      =		tf.cast(ids_tensor((self.batch_size, 3)  ,			2					)  ,			tf.inta					)

       # append to next input_ids and
       SCREAMING_SNAKE_CASE__					:					List[str]      =		tf.concat([input_ids, next_tokens]  ,			axis=-1					)
       SCREAMING_SNAKE_CASE__					:					Optional[int]      =		tf.concat([attention_mask, next_attn_mask]  ,			axis=-1					)

       SCREAMING_SNAKE_CASE__					:					Union[str, Any]      =		model(a_  ,			attention_mask=a_					)[0]
       SCREAMING_SNAKE_CASE__					:					int      =		model(a_  ,			attention_mask=a_  ,			past_key_values=a_					)[0]

       self.parent.assertEqual(next_tokens.shape[1]  ,			output_from_past.shape[1]					)

       # select random slice
       SCREAMING_SNAKE_CASE__					:					Optional[Any]      =		int(ids_tensor((1,)  ,			output_from_past.shape[-1]					)					)
       SCREAMING_SNAKE_CASE__					:					Any      =		output_from_no_past[:, -3:, random_slice_idx]
       SCREAMING_SNAKE_CASE__					:					Union[str, Any]      =		output_from_past[:, :, random_slice_idx]

       # test that outputs are equal for slice
       tf.debugging.assert_near(a_  ,			a_  ,			rtol=1e-3					)







def      _a					(   lowercase__	:      Any  ,	lowercase__	:      Any  ,	lowercase__	:      List[str]  ,	lowercase__	:      Any=None  ,	lowercase__	:      str=None  ,	lowercase__	:      int=None  ,	lowercase__	:      str=None  ,	lowercase__	:      Tuple=None  ,	):







     '''simple docstring'''


     if attention_mask is None:
          SCREAMING_SNAKE_CASE__					:					Union[str, Any]      =		tf.cast(tf.math.not_equal(lowercase__  ,	config.pad_token_id			)  ,	tf.inta			)
     if decoder_attention_mask is None:
          SCREAMING_SNAKE_CASE__					:					List[Any]      =		tf.concat(
              [
                  tf.ones(decoder_input_ids[:, :1].shape  ,	dtype=tf.inta			),
                  tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:]  ,	config.pad_token_id			)  ,	tf.inta			),
              ]  ,	axis=-1  ,	)
     if head_mask is None:
          SCREAMING_SNAKE_CASE__					:					Tuple      =		tf.ones((config.encoder_layers, config.encoder_attention_heads)			)
     if decoder_head_mask is None:
          SCREAMING_SNAKE_CASE__					:					List[str]      =		tf.ones((config.decoder_layers, config.decoder_attention_heads)			)
     if cross_attn_head_mask is None:
          SCREAMING_SNAKE_CASE__					:					Tuple      =		tf.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": decoder_attention_mask,
         "head_mask": head_mask,
         "decoder_head_mask": decoder_head_mask,
         "cross_attn_head_mask": cross_attn_head_mask,
     }

@require_tf
class    snake_case       (   UpperCamelCase_ ,    UpperCamelCase_ ,    unittest.TestCase    ):
  lowercase_								=  (
      (TFBlenderbotSmallForConditionalGeneration, TFBlenderbotSmallModel) if is_tf_available() else ()
  )
  lowercase_								=  (TFBlenderbotSmallForConditionalGeneration,) if is_tf_available() else ()
  lowercase_								=  (
      {
          'conversational': TFBlenderbotSmallForConditionalGeneration,
          'feature-extraction': TFBlenderbotSmallModel,
          'summarization': TFBlenderbotSmallForConditionalGeneration,
          'text2text-generation': TFBlenderbotSmallForConditionalGeneration,
          'translation': TFBlenderbotSmallForConditionalGeneration,
      }
      if is_tf_available()
      else {}
  )
  lowercase_								=  True
  lowercase_								=  False
  lowercase_								=  False




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




       """simple docstring"""
       SCREAMING_SNAKE_CASE__					:					Optional[Any]      =		TFBlenderbotSmallModelTester(self					)
       SCREAMING_SNAKE_CASE__					:					Dict      =		ConfigTester(self  ,			config_class=a_					)




  def 						__lowercase(   self  :						Optional[Any]					)->							Dict:




       """simple docstring"""
       self.config_tester.run_common_tests()




  def 						__lowercase(   self  :						List[str]					)->							Optional[Any]:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__					:					Tuple      =		self.model_tester.prepare_config_and_inputs_for_common()
       self.model_tester.check_decoder_model_past_large_inputs(*a_					)




@require_tokenizers
@require_tf
class    snake_case       (   unittest.TestCase    ):
  lowercase_								=  [
      'Social anxiety\nWow, I am never shy. Do you have anxiety?\nYes. I end up sweating and blushing and feel like '
      '  i\'m going to throw up.\nand why is that?'
  ]
  lowercase_								=  'facebook/blenderbot_small-90M'




  @cached_property
  def 						__lowercase(   self  :						int					)->							Any:




       """simple docstring"""
       # use "old" tokenizer here because of bug when downloading new tokenizer
       return BlenderbotSmallTokenizer.from_pretrained('facebook/blenderbot-90M'					)




  @cached_property
  def 						__lowercase(   self  :						Optional[int]					)->							Optional[Any]:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__					:					List[Any]      =		TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name					)
       return model




  @slow
  def 						__lowercase(   self  :						str					)->							List[Any]:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__					:					Any      =		self.tokenizer(self.src_text  ,			return_tensors='tf'					)
       SCREAMING_SNAKE_CASE__					:					int      =		self.model.generate(
           model_inputs.input_ids  ,			attention_mask=model_inputs.attention_mask  ,			num_beams=2  ,			use_cache=a_  ,			)
       SCREAMING_SNAKE_CASE__					:					Dict      =		self.tokenizer.batch_decode(generated_ids.numpy()  ,			skip_special_tokens=a_					)[0]
       assert generated_words in (
           "i don't know. i just feel like i'm going to throw up. it's not fun.",
           "i'm not sure. i just feel like i've been feeling like i have to be in a certain place",
           "i'm not sure. i just feel like i've been in a bad situation.",
       )








"""simple docstring"""







import argparse
import json
import os

import tensorstore as ts
import torch
from flax import serialization
from flax.traverse_util import flatten_dict, unflatten_dict
from tensorflow.io import gfile

from transformers.modeling_utils import dtype_byte_size
from transformers.models.switch_transformers.convert_switch_transformers_original_flax_checkpoint_to_pytorch import (
    rename_keys,
)
from transformers.utils import WEIGHTS_INDEX_NAME, WEIGHTS_NAME
from transformers.utils.hub import convert_file_size_to_int

def  __SCREAMING_SNAKE_CASE		(			lowerCamelCase_:							Optional[int]     ,		lowerCamelCase_:							Union[str, Any] ):
						"""simple docstring"""






						if flax_key_tuple[-1] == "kernel" and flax_tensor.ndim == 3:
												# expert layer
												snake_case   :			List[str]						    = flax_key_tuple[:-1] + ("weight",)
												snake_case   :			Union[str, Any]						    = torch.permute(lowerCamelCase_     ,		(0, 2, 1) )
						elif flax_key_tuple[-1] == "kernel" and ".".join(lowerCamelCase_ ):
												# linear layer
												snake_case   :			int						    = flax_key_tuple[:-1] + ("weight",)
												snake_case   :			Dict						    = flax_tensor.T
						elif flax_key_tuple[-1] in ["scale", "embedding"]:
												snake_case   :			Any						    = flax_key_tuple[:-1] + ("weight",)

						return flax_key_tuple, flax_tensor

def  __SCREAMING_SNAKE_CASE		(			lowerCamelCase_:							Union[str, Any]     ,		lowerCamelCase_:							Union[str, Any]     ,		lowerCamelCase_:							str ):
						"""simple docstring"""






						if "metadata" in layer:
												snake_case   :			Dict						    = layer.split("metadata" )
												snake_case   :			Optional[Any]						    = "".join(split_layer[0] )[:-1]
												snake_case   :			Any						    = [tuple(("metadata" + split_layer[1]).split("/" ) )]
						elif "kvstore" in layer:
												snake_case   :			List[str]						    = layer.split("kvstore" )
												snake_case   :			Tuple						    = "".join(split_layer[0] )[:-1]
												snake_case   :			Union[str, Any]						    = [tuple(("kvstore" + split_layer[1]).split("/" ) )]

						else:
												snake_case   :			List[Any]						    = layer.split("/" )
												snake_case   :			Union[str, Any]						    = "/".join(split_layer[:-1] )
												snake_case   :			int						    = (split_layer[-1],)

						if "kvstore/path" in layer:
												snake_case   :			str						    = f'''{switch_checkpoint_path}/{checkpoint_info[layer]}'''
						elif "kvstore/driver" in layer:
												snake_case   :			Tuple						    = "file"
						else:
												snake_case   :			int						    = checkpoint_info[layer]

						return curr_real_layer_name, split_layer, content

def  __SCREAMING_SNAKE_CASE		(			lowerCamelCase_:							Optional[Any]     ,		lowerCamelCase_:							Dict ):
						"""simple docstring"""






						snake_case   :			Optional[int]						    = rename_keys(lowerCamelCase_ )
						snake_case   :			str						    = {}
						for k, v in current_block.items():
												snake_case   :			List[str]						    = v
						snake_case   :			List[str]						    = new_current_block
						torch.save(lowerCamelCase_     ,		lowerCamelCase_ )

def  __SCREAMING_SNAKE_CASE		(			lowerCamelCase_:							Tuple     ,		lowerCamelCase_:							Optional[Any]     ,		lowerCamelCase_:							Dict     ,		lowerCamelCase_:							int     ,		lowerCamelCase_:							str = WEIGHTS_NAME ):
						"""simple docstring"""






						snake_case   :			List[str]						    = convert_file_size_to_int(lowerCamelCase_ )
						snake_case   :			List[Any]						    = []
						snake_case   :			Dict						    = {}
						snake_case   :			str						    = 0
						snake_case   :			List[str]						    = 0

						os.makedirs(lowerCamelCase_     ,		exist_ok=lowerCamelCase_ )
						with gfile.GFile(switch_checkpoint_path + "/checkpoint"     ,		"rb" ) as fp:
												snake_case   :			List[Any]						    = serialization.msgpack_restore(fp.read() )["optimizer"]["target"]
												snake_case   :			Union[str, Any]						    = flatten_dict(lowerCamelCase_     ,		sep="/" )

						snake_case   :			Optional[int]						    = {}
						for layer in checkpoint_info.keys():
												snake_case    ,			snake_case    ,			snake_case   :			Union[str, Any]						    = get_key_and_tensorstore_dict(
												    lowerCamelCase_     ,		lowerCamelCase_     ,		lowerCamelCase_ )
												if curr_real_layer_name in all_layers:
																		snake_case   :			str						    = content
												else:
																		snake_case   :			Any						    = {split_layer[-1]: content}

						for key in all_layers.keys():
												# open tensorstore file
												snake_case   :			Optional[Any]						    = ts.open(unflatten_dict(all_layers[key] ) ).result().read().result()
												snake_case   :			Tuple						    = torch.tensor(lowerCamelCase_ )
												snake_case   :			str						    = raw_weights.numel() * dtype_byte_size(raw_weights.dtype )

												# use the renaming pattern from the small conversion scripts
												snake_case    ,			snake_case   :			Dict						    = rename_base_flax_keys(tuple(key.split("/" ) )     ,		lowerCamelCase_ )
												snake_case   :			Union[str, Any]						    = "/".join(lowerCamelCase_ )

												# If this weight is going to tip up over the maximal size, we split.
												if current_block_size + weight_size > max_shard_size:
																		snake_case   :			str						    = os.path.join(
																		    lowerCamelCase_     ,		weights_name.replace(".bin"     ,		f'''-{len(lowerCamelCase_ )+1:05d}-of-???.bin''' ) )
																		rename_and_save_block(lowerCamelCase_     ,		lowerCamelCase_ )
																		sharded_state_dicts.append(current_block.keys() )
																		del current_block
																		snake_case   :			Any						    = {}
																		snake_case   :			Union[str, Any]						    = 0

												snake_case   :			Any						    = raw_weights.to(getattr(lowerCamelCase_     ,		lowerCamelCase_ ) )
												current_block_size += weight_size
												total_size += weight_size

						# Add the last block
						snake_case   :			List[Any]						    = os.path.join(lowerCamelCase_     ,		weights_name.replace(".bin"     ,		f'''-{len(lowerCamelCase_ )+1:05d}-of-???.bin''' ) )
						rename_and_save_block(lowerCamelCase_     ,		lowerCamelCase_ )
						sharded_state_dicts.append(current_block.keys() )

						# If we only have one shard, we return it
						if len(lowerCamelCase_ ) == 1:
												return {weights_name: sharded_state_dicts[0]}, None

						# Otherwise, let's build the index
						snake_case   :			List[Any]						    = {}
						snake_case   :			Dict						    = {}
						for idx, shard in enumerate(lowerCamelCase_ ):
												snake_case   :			List[Any]						    = weights_name.replace(
												    ".bin"     ,		f'''-{idx+1:05d}-of-{len(lowerCamelCase_ ):05d}.bin''' )  # len(sharded_state_dicts):05d}
												snake_case   :			Tuple						    = os.path.join(lowerCamelCase_     ,		weights_name.replace(".bin"     ,		f'''-{idx+1:05d}-of-???.bin''' ) )
												os.rename(lowerCamelCase_     ,		os.path.join(lowerCamelCase_     ,		lowerCamelCase_ ) )
												snake_case   :			Union[str, Any]						    = shard
												for key in shard:
																		snake_case   :			List[Any]						    = shard_file

    # Add the metadata
						snake_case   :			Optional[int]						    = {"total_size": total_size}
						snake_case   :			List[str]						    = {"metadata": metadata, "weight_map": weight_map}

						with open(os.path.join(lowerCamelCase_     ,		lowerCamelCase_ )     ,		"w"     ,		encoding="utf-8" ) as f:
												snake_case   :			Tuple						    = json.dumps(lowerCamelCase_     ,		indent=2     ,		sort_keys=lowerCamelCase_ ) + "\n"
												f.write(lowerCamelCase_ )

						return metadata, index


if __name__ == "__main__":
			A		=						argparse.ArgumentParser()
			# Required parameters
			parser.add_argument(
			    '--switch_t5x_checkpoint_path',
			    default='/mnt/disks/disk_switch/original_checkpoints/switch-xxl-128/checkpoint_634600',
			    type=str,
			    required=False,
			    help='Path to a directory containing a folder per layer. Follows the original Google format.',
			)
			parser.add_argument('--max_shard_size', default='10GB', required=False, help='Max shard size')
			parser.add_argument('--dtype', default='bfloat16', type=str, required=False, help='dtype of the saved model')
			parser.add_argument(
			    '--pytorch_dump_folder_path',
			    default='/mnt/disks/disk_switch/original_checkpoints/switch-xxl-128-converted',
			    type=str,
			    required=False,
			    help='Path to the output pytorch model.',
			)
			A		=						parser.parse_args()
			shard_on_the_fly(
			    args.switch_tax_checkpoint_path,
			    args.pytorch_dump_folder_path,
			    args.max_shard_size,
			    args.dtype,
			)

def  __SCREAMING_SNAKE_CASE		(			):
						"""simple docstring"""






						from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration, TaTokenizer

						snake_case   :			List[Any]						    = SwitchTransformersConfig.from_pretrained("google/switch-base-8" )
						config.save_pretrained("/home/arthur_huggingface_co/transformers/switch_converted" )
						snake_case   :			List[Any]						    = SwitchTransformersForConditionalGeneration.from_pretrained(
						    "/home/arthur_huggingface_co/transformers/switch_converted"     ,		device_map="auto" )

						snake_case   :			Dict						    = TaTokenizer.from_pretrained("t5-small" )
						snake_case   :			Tuple						    = "A <extra_id_0> walks into a bar a orders a <extra_id_1> with <extra_id_2> pinch of <extra_id_3>."

						snake_case   :			Dict						    = tokenizer(lowerCamelCase_     ,		return_tensors="pt" ).input_ids
						snake_case   :			Optional[int]						    = model.generate(lowerCamelCase_     ,		decoder_start_token_id=0 )
						print(tokenizer.decode(out[0] ) )



