code: # coding=utf-8
  # Copyright 2022 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.
  #
  
  import math
  import unittest
  
  from transformers import BloomConfig, is_torch_available
  from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device
  
  from ...generation.test_generation_utils import GenerationTesterMixin
  from ...test_configuration_common import ConfigTester
  from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
  
  
  if is_torch_available():
      import torch
  
      from transformers import (
          BLOOM_PRETRAINED_MODEL_ARCHIVE_LIST,
          BloomForCausalLM,
          BloomForSequenceClassification,
          BloomForTokenClassification,
          BloomModel,
          BloomTokenizerFast,
      )
  
  
  @require_torch
  class BloomModelTester:
      def __init__(
          self,
          parent,
          batch_size=14,
          seq_length=7,
          is_training=True,
          use_token_type_ids=False,
          use_input_mask=True,
          use_labels=True,
          use_mc_token_ids=True,
          vocab_size=99,
          hidden_size=32,
          num_hidden_layers=5,
          num_attention_heads=4,
          intermediate_size=37,
          hidden_act="gelu",
          hidden_dropout_prob=0.1,
          attention_probs_dropout_prob=0.1,
          max_position_embeddings=512,
          type_vocab_size=16,
          type_sequence_label_size=2,
          initializer_range=0.02,
          num_labels=3,
          num_choices=4,
          scope=None,
      ):
          self.parent = parent
          self.batch_size = batch_size
          self.seq_length = seq_length
          self.is_training = is_training
          self.use_token_type_ids = use_token_type_ids
          self.use_input_mask = use_input_mask
          self.use_labels = use_labels
          self.use_mc_token_ids = use_mc_token_ids
          self.vocab_size = vocab_size
          self.hidden_size = hidden_size
          self.num_hidden_layers = num_hidden_layers
          self.num_attention_heads = num_attention_heads
          self.intermediate_size = intermediate_size
          self.hidden_act = hidden_act
          self.hidden_dropout_prob = hidden_dropout_prob
          self.attention_probs_dropout_prob = attention_probs_dropout_prob
          self.max_position_embeddings = max_position_embeddings
          self.type_vocab_size = type_vocab_size
          self.type_sequence_label_size = type_sequence_label_size
          self.initializer_range = initializer_range
          self.num_labels = num_labels
          self.num_choices = num_choices
          self.scope = None
          self.bos_token_id = vocab_size - 1
          self.eos_token_id = vocab_size - 1
          self.pad_token_id = vocab_size - 1
  
      def get_large_model_config(self):
          return BloomConfig.from_pretrained("bigscience/bloom")
  
      def prepare_config_and_inputs(self, gradient_checkpointing=False):
          input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
  
          input_mask = None
          if self.use_input_mask:
              input_mask = random_attention_mask([self.batch_size, self.seq_length])
  
          sequence_labels = None
          if self.use_labels:
              sequence_labels = ids_tensor([self.batch_size], self.type_sequence_label_size)
  
          config = self.get_config(gradient_checkpointing=gradient_checkpointing)
  
          return (config, input_ids, input_mask, sequence_labels)
  
      def get_config(self, gradient_checkpointing=False, slow_but_exact=True):
          return BloomConfig(
              vocab_size=self.vocab_size,
              seq_length=self.seq_length,
              hidden_size=self.hidden_size,
              n_layer=self.num_hidden_layers,
              n_head=self.num_attention_heads,
              resid_pdrop=self.hidden_dropout_prob,
              attn_pdrop=self.attention_probs_dropout_prob,
              n_positions=self.max_position_embeddings,
              type_vocab_size=self.type_vocab_size,
              initializer_range=self.initializer_range,
              use_cache=True,
              bos_token_id=self.bos_token_id,
              eos_token_id=self.eos_token_id,
              pad_token_id=self.pad_token_id,
              num_labels=self.num_labels,
              gradient_checkpointing=gradient_checkpointing,
              slow_but_exact=slow_but_exact,
              dtype="float32",
          )
  
      def create_and_check_bloom_model(self, config, input_ids, input_mask, *args):
          model = BloomModel(config=config)
          model.to(torch_device)
          model.eval()
  
          result = model(input_ids)
  
          self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))
          self.parent.assertEqual(len(result.past_key_values), config.n_layer)
  
      def create_and_check_bloom_model_past(self, config, input_ids, input_mask, *args):
          model = BloomModel(config=config)
  
          model.to(torch_device)
          model.eval()
  
          # first forward pass
          outputs = model(input_ids, attention_mask=torch.ones_like(input_ids), use_cache=True)
          outputs_use_cache_conf = model(input_ids, attention_mask=torch.ones_like(input_ids))
          outputs_no_past = model(input_ids, use_cache=False, attention_mask=torch.ones_like(input_ids))
  
          self.parent.assertTrue(len(outputs) == len(outputs_use_cache_conf))
          self.parent.assertTrue(len(outputs) == len(outputs_no_past) + 1)
  
          past = outputs["past_key_values"]
  
          # create hypothetical next token and extent to next_input_ids
          next_tokens = ids_tensor((self.batch_size, 1), config.vocab_size)
  
          # append to next input_ids and token_type_ids
          next_input_ids = torch.cat([input_ids, next_tokens], dim=-1)
  
          output_from_no_past = model(next_input_ids)["last_hidden_state"]
          output_from_past = model(next_tokens, past_key_values=past)["last_hidden_state"]
  
          # select random slice
          random_slice_idx = ids_tensor((1,), output_from_past.shape[-1]).item()
          output_from_no_past_slice = output_from_no_past[:, -1, random_slice_idx].detach()
          output_from_past_slice = output_from_past[:, 0, random_slice_idx].detach()
  
          # test that outputs are equal for slice
          self.parent.assertTrue(torch.allclose(output_from_past_slice, output_from_no_past_slice, atol=1e-3))
  
      def create_and_check_bloom_model_attention_mask_past(self, config, input_ids, input_mask, *args):
          model = BloomModel(config=config)
          model.to(torch_device)
          model.eval()
  
          # create attention mask
          attn_mask = torch.ones(input_ids.shape, dtype=torch.long, device=torch_device)
          half_seq_length = self.seq_length // 2
          attn_mask[:, half_seq_length:] = 0
  
          # first forward pass
          output, past = model(input_ids, attention_mask=attn_mask).to_tuple()
  
          # create hypothetical next token and extent to next_input_ids
          next_tokens = ids_tensor((self.batch_size, 1), config.vocab_size)
  
          # change a random masked slice from input_ids
          random_seq_idx_to_change = ids_tensor((1,), half_seq_length).item() + 1
          random_other_next_tokens = ids_tensor((self.batch_size, 1), config.vocab_size).squeeze(-1)
          input_ids[:, -random_seq_idx_to_change] = random_other_next_tokens
  
          # append to next input_ids and attn_mask
          next_input_ids = torch.cat([input_ids, next_tokens], dim=-1)
          attn_mask = torch.cat(
              [attn_mask, torch.ones((attn_mask.shape[0], 1), dtype=torch.long, device=torch_device)],
              dim=1,
          )
  
          # get two different outputs
          output_from_no_past = model(next_input_ids, attention_mask=attn_mask)["last_hidden_state"]
          output_from_past = model(next_tokens, past_key_values=past, attention_mask=attn_mask)["last_hidden_state"]
  
          # select random slice
          random_slice_idx = ids_tensor((1,), output_from_past.shape[-1]).item()
          output_from_no_past_slice = output_from_no_past[:, -1, random_slice_idx].detach()
          output_from_past_slice = output_from_past[:, 0, random_slice_idx].detach()
  
          # test that outputs are equal for slice
          self.parent.assertTrue(torch.allclose(output_from_past_slice, output_from_no_past_slice, atol=1e-3))
  
      def create_and_check_bloom_model_past_large_inputs(self, config, input_ids, input_mask, *args):
          model = BloomModel(config=config)
          model.to(torch_device)
          model.eval()
  
          # first forward pass
          outputs = model(input_ids, attention_mask=input_mask, use_cache=True)
  
          output, past = outputs.to_tuple()
  
          # create hypothetical next token and extent to next_input_ids
          next_tokens = ids_tensor((self.batch_size, 3), config.vocab_size)
          next_mask = ids_tensor((self.batch_size, 3), vocab_size=2)
  
          # append to next input_ids and token_type_ids
          next_input_ids = torch.cat([input_ids, next_tokens], dim=-1)
          next_attention_mask = torch.cat([input_mask, next_mask], dim=-1)
  
          output_from_no_past = model(next_input_ids, attention_mask=next_attention_mask)["last_hidden_state"]
          output_from_past = model(next_tokens, attention_mask=next_attention_mask, past_key_values=past)[
              "last_hidden_state"
          ]
          self.parent.assertTrue(output_from_past.shape[1] == next_tokens.shape[1])
  
          # select random slice
          random_slice_idx = ids_tensor((1,), output_from_past.shape[-1]).item()
          output_from_no_past_slice = output_from_no_past[:, -3:, random_slice_idx].detach()
          output_from_past_slice = output_from_past[:, :, random_slice_idx].detach()
  
          # test that outputs are equal for slice
          self.parent.assertTrue(torch.allclose(output_from_past_slice, output_from_no_past_slice, atol=1e-3))
  
      def create_and_check_lm_head_model(self, config, input_ids, input_mask, *args):
          model = BloomForCausalLM(config)
          model.to(torch_device)
          model.eval()
  
          result = model(input_ids, labels=input_ids)
          self.parent.assertEqual(result.loss.shape, ())
          self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size))
  
      def create_and_check_sequence_classification_model(self, config, input_ids, input_mask, *args):
          config.num_labels = self.num_labels
          model = BloomForSequenceClassification(config)
          model.to(torch_device)
          model.eval()
  
          result = model(input_ids, attention_mask=input_mask)
          self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels))
  
      def create_and_check_token_classification_model(self, config, input_ids, input_mask, *args):
          model = BloomForTokenClassification(config)
          model.to(torch_device)
          model.eval()
  
          result = model(input_ids, attention_mask=input_mask)
          self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.num_labels))
  
      def create_and_check_forward_and_backwards(
          self, config, input_ids, input_mask, *args, gradient_checkpointing=False
      ):
          model = BloomForCausalLM(config)
          model.to(torch_device)
          if gradient_checkpointing:
              model.gradient_checkpointing_enable()
  
          result = model(input_ids, labels=input_ids)
          self.parent.assertEqual(result.loss.shape, ())
          self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size))
          result.loss.backward()
  
      def create_and_check_bloom_weight_initialization(self, config, *args):
          model = BloomModel(config)
          model_std = model.config.initializer_range / math.sqrt(2 * model.config.n_layer)
          for key in model.state_dict().keys():
              if "c_proj" in key and "weight" in key:
                  self.parent.assertLessEqual(abs(torch.std(model.state_dict()[key]) - model_std), 0.001)
                  self.parent.assertLessEqual(abs(torch.mean(model.state_dict()[key]) - 0.0), 0.01)
  
      def prepare_config_and_inputs_for_common(self):
          config_and_inputs = self.prepare_config_and_inputs()
  
          config, input_ids, input_mask, sequence_labels = config_and_inputs
  
          inputs_dict = {"input_ids": input_ids}
  
          return config, inputs_dict
  
  
  @require_torch
  class BloomModelTest(ModelTesterMixin, GenerationTesterMixin, unittest.TestCase):
  
      all_model_classes = (
          (
              BloomModel,
              BloomForCausalLM,
              BloomForSequenceClassification,
              BloomForTokenClassification,
          )
          if is_torch_available()
          else ()
      )
  
      all_generative_model_classes = (BloomForCausalLM,) if is_torch_available() else ()
      fx_compatible = False
      test_missing_keys = False
      test_pruning = False
      test_torchscript = True  # torch.autograd functions seems to be not supported
  
      def setUp(self):
          self.model_tester = BloomModelTester(self)
          self.config_tester = ConfigTester(self, config_class=BloomConfig, n_embd=37)
  
      def test_config(self):
          self.config_tester.run_common_tests()
  
      def test_bloom_model(self):
          config_and_inputs = self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_bloom_model(*config_and_inputs)
  
      def test_bloom_model_past(self):
          config_and_inputs = self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_bloom_model_past(*config_and_inputs)
  
      def test_bloom_model_att_mask_past(self):
          config_and_inputs = self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_bloom_model_attention_mask_past(*config_and_inputs)
  
      def test_bloom_model_past_large_inputs(self):
          config_and_inputs = self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_bloom_model_past_large_inputs(*config_and_inputs)
  
      def test_bloom_lm_head_model(self):
          config_and_inputs = self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_lm_head_model(*config_and_inputs)
  
      def test_bloom_sequence_classification_model(self):
          config_and_inputs = self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_sequence_classification_model(*config_and_inputs)
  
      def test_bloom_token_classification_model(self):
          config_and_inputs = self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_token_classification_model(*config_and_inputs)
  
      def test_bloom_gradient_checkpointing(self):
          config_and_inputs = self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_forward_and_backwards(*config_and_inputs, gradient_checkpointing=True)
  
      def test_bloom_weight_initialization(self):
          config_and_inputs = self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_bloom_weight_initialization(*config_and_inputs)
  
      @slow
      def test_model_from_pretrained(self):
          for model_name in BLOOM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
              model = BloomModel.from_pretrained(model_name)
              self.assertIsNotNone(model)
  
      @slow
      @require_torch_gpu
      def test_simple_generation(self):
          path_350m = "bigscience/bloom-350m"
          model = BloomForCausalLM.from_pretrained(path_350m, torch_dtype="auto", use_cache=True).cuda()
          model = model.eval()
          tokenizer = BloomTokenizerFast.from_pretrained(path_350m)
  
          input_sentence = "I enjoy walking with my cute dog"
          EXPECTED_OUTPUT = (
              "I enjoy walking with my cute dog, and I love to watch the kids play. I am a very active person, and I am"
              " a very good listener. I am a very good person, and I am a very good person. I am a"
          )
  
          input_ids = tokenizer.encode(input_sentence, return_tensors="pt")
          greedy_output = model.generate(input_ids.cuda(), max_length=50)
  
          self.assertEqual(tokenizer.decode(greedy_output[0], skip_special_tokens=True), EXPECTED_OUTPUT)
  
      @slow
      @require_torch_gpu
      def test_batch_generation(self):
          path_350m = "bigscience/bloom-350m"
          model = BloomForCausalLM.from_pretrained(path_350m, torch_dtype="auto", use_cache=True).cuda()
          model = model.eval()
          tokenizer = BloomTokenizerFast.from_pretrained(path_350m, padding_side="left")
  
          input_sentence = ["I enjoy walking with my cute dog", "I enjoy walking with my cute dog"]
  
          input_ids = tokenizer.batch_encode_plus(input_sentence, return_tensors="pt", padding=True)
          greedy_output = model.generate(
              input_ids["input_ids"].cuda(), attention_mask=input_ids["attention_mask"], max_length=50, do_sample=False
          )
  
          self.assertEqual(
              tokenizer.decode(greedy_output[0], skip_special_tokens=True),
              tokenizer.decode(greedy_output[1], skip_special_tokens=True),
          )
  
      @slow
      @require_torch_gpu
      def test_batch_generation_padd(self):
          path_350m = "bigscience/bloom-350m"
          model = BloomForCausalLM.from_pretrained(path_350m, torch_dtype="auto", use_cache=True).cuda()
          model = model.eval()
          tokenizer = BloomTokenizerFast.from_pretrained(path_350m, padding_side="left")
  
          input_sentence = ["I enjoy walking with my cute dog", "Hello my name is"]
          input_sentence_without_pad = "Hello my name is"
  
          input_ids = tokenizer.batch_encode_plus(input_sentence, return_tensors="pt", padding=True)
          input_ids_without_pad = tokenizer.encode(input_sentence_without_pad, return_tensors="pt")
  
          greedy_output = model.generate(
              input_ids["input_ids"].cuda(), attention_mask=input_ids["attention_mask"], max_length=50, do_sample=False
          )
          greedy_output_without_pad = model.generate(input_ids_without_pad.cuda(), max_length=50, do_sample=False)
  
          # test token values
          self.assertEqual(greedy_output[-1, 3:].tolist(), greedy_output_without_pad[0, :-3].tolist())
  
          # test reconstructions
          self.assertEqual(
              tokenizer.decode(greedy_output[-1, 3:], skip_special_tokens=True),
              tokenizer.decode(greedy_output_without_pad[0, :-3], skip_special_tokens=True),
          )
  
  
  @require_torch
  class BloomEmbeddingTest(unittest.TestCase):
      """
      The goal here is to compare the embeddings generated by the model trained
      using Megatron-LM with the one from the transformers library, with a small GPT2-like model
      to ensure that the conversion from Megatron-LM to transformers has been done successfully.
      The script compares the logits of the embedding layer and the transformer layers.
  
      WARNING: It is expected that these logits will not have exactly the same statistics when running
      the code on CPU or GPU. For more info, please visit:
        - https://github.com/pytorch/pytorch/issues/76052#issuecomment-1103193548
        - https://discuss.pytorch.org/t/reproducibility-issue-between-intel-and-amd-cpus/144779/9
  
  
      You need to install tokenizers following this readme:
          - https://huggingface.co/bigscience-catalogue-data-dev/byte-level-bpe-tokenizer-no-norm-250k-whitespace-and-eos-regex-alpha-v3-dedup-lines-articles
  
      Tokenizer used during training:
          - https://huggingface.co/bigscience-catalogue-data-dev/byte-level-bpe-tokenizer-no-norm-250k-whitespace-and-eos-regex-alpha-v3-dedup-lines-articles
  
      # TODO change the script (or just add skip) when building the env with tokenizers 0.12.0
      """
  
      def setUp(self):
          super().setUp()
          self.path_bigscience_model = "bigscience/bigscience-small-testing"
  
      @require_torch
      def test_embeddings(self):
          model = BloomForCausalLM.from_pretrained(self.path_bigscience_model, torch_dtype="auto")  # load in fp32
          model.eval()
  
          EMBEDDINGS_DS_BEFORE_LN_BF_16_MEAN = {
              3478: 0.0002307891845703125,
              368: -0.000568389892578125,
              109586: -0.0003910064697265625,
              35433: -0.000194549560546875,
              2: 0.0004138946533203125,
              77: 0.000659942626953125,
              132619: -0.00031280517578125,
              2175: 0.000457763671875,
              23714: 0.000263214111328125,
              73173: -0.000286102294921875,
              144252: 0.00052642822265625,
          }
          EMBEDDINGS_DS_BEFORE_LN_BF_16_MIN = {
              3478: -0.00921630859375,
              368: -0.010009765625,
              109586: -0.01031494140625,
              35433: -0.01177978515625,
              2: -0.0074462890625,
              77: -0.00848388671875,
              132619: -0.009521484375,
              2175: -0.0074462890625,
              23714: -0.0145263671875,
              73173: -0.007415771484375,
              144252: -0.01007080078125,
          }
          EMBEDDINGS_DS_BEFORE_LN_BF_16_MAX = {
              3478: 0.0128173828125,
              368: 0.01214599609375,
              109586: 0.0111083984375,
              35433: 0.01019287109375,
              2: 0.0157470703125,
              77: 0.0174560546875,
              132619: 0.0078125,
              2175: 0.0113525390625,
              23714: 0.0146484375,
              73173: 0.01116943359375,
              144252: 0.01141357421875,
          }
          EMBEDDINGS_DS_BEFORE_LN_BF_16_SUM = {"value": 0.08203125}
  
          EMBEDDINGS_DS_BEFORE_LN_F_16_MEAN = {
              132619: -0.00031256675720214844,
              3478: 0.00023090839385986328,
              368: -0.0005702972412109375,
              109586: -0.00039124488830566406,
              35433: -0.000194549560546875,
              2: 0.0004146099090576172,
              2175: 0.0004572868347167969,
              23714: 0.00026416778564453125,
              73173: -0.0002865791320800781,
              144252: 0.0005254745483398438,
              77: 0.0006618499755859375,
          }
          EMBEDDINGS_DS_BEFORE_LN_F_16_MIN = {
              3478: -0.00921630859375,
              368: -0.010009765625,
              109586: -0.01031494140625,
              35433: -0.01177978515625,
              2: -0.0074462890625,
              77: -0.00848388671875,
              132619: -0.009521484375,
              2175: -0.0074462890625,
              23714: -0.0145263671875,
              73173: -0.007415771484375,
              144252: -0.01007080078125,
          }
          EMBEDDINGS_DS_BEFORE_LN_F_16_MAX = {
              3478: 0.0128173828125,
              368: 0.01214599609375,
              109586: 0.0111083984375,
              35433: 0.01019287109375,
              2: 0.0157470703125,
              77: 0.0174560546875,
              132619: 0.0078125,
              2175: 0.0113525390625,
              23714: 0.0146484375,
              73173: 0.01116943359375,
              144252: 0.01141357421875,
          }
          EMBEDDINGS_DS_BEFORE_LN_F_16_SUM = {"value": 0.0821533203125}
  
          EMBEDDINGS_DS_BEFORE_LN_F_32_MEAN = {
              132619: -0.00031267106533050537,
              3478: 0.00023087859153747559,
              368: -0.0005701072514057159,
              109586: -0.0003911703824996948,
              35433: -0.0001944899559020996,
              2: 0.0004146844148635864,
              2175: 0.00045740045607089996,
              23714: 0.0002641640603542328,
              73173: -0.0002864748239517212,
              144252: 0.0005256589502096176,
              77: 0.0006617321632802486,
          }
          EMBEDDINGS_DS_BEFORE_LN_F_32_MIN = {
              3478: -0.00921630859375,
              368: -0.010009765625,
              109586: -0.01031494140625,
              35433: -0.01177978515625,
              2: -0.0074462890625,
              77: -0.00848388671875,
              132619: -0.009521484375,
              2175: -0.0074462890625,
              23714: -0.0145263671875,
              73173: -0.007415771484375,
              144252: -0.01007080078125,
          }
          EMBEDDINGS_DS_BEFORE_LN_F_32_MAX = {
              3478: 0.0128173828125,
              368: 0.01214599609375,
              109586: 0.0111083984375,
              35433: 0.01019287109375,
              2: 0.0157470703125,
              77: 0.0174560546875,
              132619: 0.0078125,
              2175: 0.0113525390625,
              23714: 0.0146484375,
              73173: 0.01116943359375,
              144252: 0.01141357421875,
          }
          EMBEDDINGS_DS_BEFORE_LN_F_32_SUM = {"value": 0.08217757940292358}
  
          TEST_EMBEDDINGS = {
              "torch.bfloat16": {
                  "mean": EMBEDDINGS_DS_BEFORE_LN_BF_16_MEAN,
                  "max": EMBEDDINGS_DS_BEFORE_LN_BF_16_MAX,
                  "min": EMBEDDINGS_DS_BEFORE_LN_BF_16_MIN,
                  "sum": EMBEDDINGS_DS_BEFORE_LN_BF_16_SUM,
              },
              "torch.float32": {
                  "mean": EMBEDDINGS_DS_BEFORE_LN_F_32_MEAN,
                  "max": EMBEDDINGS_DS_BEFORE_LN_F_32_MAX,
                  "min": EMBEDDINGS_DS_BEFORE_LN_F_32_MIN,
                  "sum": EMBEDDINGS_DS_BEFORE_LN_F_32_SUM,
              },
              "torch.float": {
                  "mean": EMBEDDINGS_DS_BEFORE_LN_F_32_MEAN,
                  "max": EMBEDDINGS_DS_BEFORE_LN_F_32_MAX,
                  "min": EMBEDDINGS_DS_BEFORE_LN_F_32_MIN,
                  "sum": EMBEDDINGS_DS_BEFORE_LN_F_32_SUM,
              },
              "torch.float16": {
                  "mean": EMBEDDINGS_DS_BEFORE_LN_F_16_MEAN,
                  "max": EMBEDDINGS_DS_BEFORE_LN_F_16_MAX,
                  "min": EMBEDDINGS_DS_BEFORE_LN_F_16_MIN,
                  "sum": EMBEDDINGS_DS_BEFORE_LN_F_16_SUM,
              },
          }
  
          # fmt: off
          EXAMPLE_IDS = [3478, 368, 109586, 35433, 2, 77, 132619, 3478, 368, 109586, 35433, 2, 2175, 23714, 73173, 144252, 2, 77, 132619, 3478]
          # fmt: on
  
          EMBEDDINGS_DS_AFTER_LN_MEAN = {
              3478: -6.580352783203125e-05,
              368: 0.0001316070556640625,
              109586: -0.00030517578125,
              35433: 4.00543212890625e-05,
              2: -7.2479248046875e-05,
              77: -8.96453857421875e-05,
              132619: 0.0001583099365234375,
              2175: 2.1219253540039062e-05,
              23714: -0.000247955322265625,
              73173: -0.00021839141845703125,
              144252: -0.0001430511474609375,
          }
          EMBEDDINGS_DS_AFTER_LN_MIN = {
              3478: -1.6953125,
              368: -1.6875,
              109586: -1.6875,
              35433: -2.125,
              2: -1.390625,
              77: -1.5390625,
              132619: -1.875,
              2175: -1.4609375,
              23714: -2.296875,
              73173: -1.3515625,
              144252: -1.78125,
          }
          EMBEDDINGS_DS_AFTER_LN_MAX = {
              3478: 2.265625,
              368: 2.28125,
              109586: 1.953125,
              35433: 1.90625,
              2: 2.703125,
              77: 2.828125,
              132619: 1.65625,
              2175: 2.015625,
              23714: 2.234375,
              73173: 2.171875,
              144252: 1.828125,
          }
  
          EMBEDDINGS_DS_AFTER_LN = {
              "mean": EMBEDDINGS_DS_AFTER_LN_MEAN,
              "min": EMBEDDINGS_DS_AFTER_LN_MIN,
              "max": EMBEDDINGS_DS_AFTER_LN_MAX,
          }
  
          tensor_ids = torch.LongTensor([EXAMPLE_IDS])
          with torch.no_grad():
              embeddings = model.transformer.word_embeddings(tensor_ids)
              embeddings_ln = model.transformer.word_embeddings_layernorm(embeddings)  #
          # first check the embeddings before LN
          output_dict = {"min": {}, "max": {}, "mean": {}, "sum": {"value": embeddings.sum().item()}}
          for i, idx in enumerate(EXAMPLE_IDS):
              output_dict["min"][idx] = embeddings.min(dim=-1).values[0][i].item()
              output_dict["max"][idx] = embeddings.max(dim=-1).values[0][i].item()
              output_dict["mean"][idx] = embeddings.mean(dim=-1)[0][i].item()
  
          for key in TEST_EMBEDDINGS[str(model.dtype)].keys():
              self.assertDictEqual(TEST_EMBEDDINGS[str(model.dtype)][key], output_dict[key])
  
          output_dict_norm = {"min": {}, "max": {}, "mean": {}}
          for i, idx in enumerate(EXAMPLE_IDS):
              output_dict_norm["min"][idx] = embeddings_ln.min(dim=-1).values[0][i].item()
              output_dict_norm["max"][idx] = embeddings_ln.max(dim=-1).values[0][i].item()
              output_dict_norm["mean"][idx] = embeddings_ln.mean(dim=-1)[0][i].item()
  
          # This test does not pass when places = 2
          for i, key in enumerate(output_dict_norm.keys()):
              for j, idx in enumerate(output_dict[key].keys()):
                  self.assertAlmostEqual(EMBEDDINGS_DS_AFTER_LN[key][idx], output_dict_norm[key][idx], places=1)
  
      @require_torch
      def test_hidden_states_transformers(self):
          cuda_available = torch.cuda.is_available()
          model = BloomModel.from_pretrained(self.path_bigscience_model, use_cache=False, torch_dtype="auto").to(
              torch_device
          )
          model.eval()
  
          # fmt: off
          EXAMPLE_IDS = [3478, 368, 109586, 35433, 2, 77, 132619, 3478, 368, 109586, 35433, 2, 2175, 23714, 73173, 144252, 2, 77, 132619, 3478]
          # fmt: on
  
          MEAN_VALUE_LAST_LM = -4.3392181396484375e-05
          MIN_MAX_DICT = {"min": -2.0625, "max": 2.75}
          tensor_ids = torch.LongTensor([EXAMPLE_IDS])
  
          with torch.no_grad():
              logits = model(tensor_ids.to(torch_device))
          output_dict = {
              "min": logits.last_hidden_state.min(dim=-1).values[0][0].item(),
              "max": logits.last_hidden_state.max(dim=-1).values[0][0].item(),
          }
  
          if cuda_available:
              self.assertAlmostEqual(MEAN_VALUE_LAST_LM, logits.last_hidden_state.mean().item(), places=4)
          else:
              self.assertAlmostEqual(MEAN_VALUE_LAST_LM, logits.last_hidden_state.mean().item(), places=3)
  
          self.assertDictEqual(MIN_MAX_DICT, output_dict)
  
      @require_torch
      def test_logits(self):
          cuda_available = torch.cuda.is_available()
          model = BloomForCausalLM.from_pretrained(self.path_bigscience_model, use_cache=False, torch_dtype="auto").to(
              torch_device
          )  # load in bf16
          model.eval()
  
          # fmt: off
          EXAMPLE_IDS = [3478, 368, 109586, 35433, 2, 77, 132619, 3478, 368, 109586, 35433, 2, 2175, 23714, 73173, 144252, 2, 77, 132619, 3478]
          # fmt: on
  
          MEAN_LOGITS_GPU_1 = -1.823902130126953e-05
          MEAN_LOGITS_GPU_2 = 1.9431114196777344e-05
  
          tensor_ids = torch.LongTensor([EXAMPLE_IDS]).to(torch_device)
          with torch.no_grad():
              output = model(tensor_ids).logits
  
          output_gpu_1, output_gpu_2 = output.split(125440, dim=-1)
          if cuda_available:
              self.assertEqual(output_gpu_1.mean().item(), MEAN_LOGITS_GPU_1)
              self.assertEqual(output_gpu_2.mean().item(), MEAN_LOGITS_GPU_2)
          else:
              self.assertAlmostEqual(output_gpu_1.mean().item(), MEAN_LOGITS_GPU_1, places=6)  # 1e-06 precision!!
              self.assertAlmostEqual(output_gpu_2.mean().item(), MEAN_LOGITS_GPU_2, places=6)
  
apis: torch.ones_like torch.ones torch.no_grad torch.cuda.is_available torch.LongTensor torch.cat torch.allclose