





import argparse
from collections import OrderedDict
from pathlib import Path

import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from torchvision.transforms import functional as F

from transformers import DetrImageProcessor, TableTransformerConfig, TableTransformerForObjectDetection
from transformers.utils import logging


logging.set_verbosity_info()
UpperCamelCase__											= logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
UpperCamelCase__											= []
for i in range(6):
					# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
					rename_keys.append(
					    (f"""transformer.encoder.layers.{i}.self_attn.out_proj.weight""", f"""encoder.layers.{i}.self_attn.out_proj.weight""")
					)
					rename_keys.append(
					    (f"""transformer.encoder.layers.{i}.self_attn.out_proj.bias""", f"""encoder.layers.{i}.self_attn.out_proj.bias""")
					)
					rename_keys.append((f"""transformer.encoder.layers.{i}.linear1.weight""", f"""encoder.layers.{i}.fc1.weight"""))
					rename_keys.append((f"""transformer.encoder.layers.{i}.linear1.bias""", f"""encoder.layers.{i}.fc1.bias"""))
					rename_keys.append((f"""transformer.encoder.layers.{i}.linear2.weight""", f"""encoder.layers.{i}.fc2.weight"""))
					rename_keys.append((f"""transformer.encoder.layers.{i}.linear2.bias""", f"""encoder.layers.{i}.fc2.bias"""))
					rename_keys.append(
					    (f"""transformer.encoder.layers.{i}.norm1.weight""", f"""encoder.layers.{i}.self_attn_layer_norm.weight""")
					)
					rename_keys.append((f"""transformer.encoder.layers.{i}.norm1.bias""", f"""encoder.layers.{i}.self_attn_layer_norm.bias"""))
					rename_keys.append((f"""transformer.encoder.layers.{i}.norm2.weight""", f"""encoder.layers.{i}.final_layer_norm.weight"""))
					rename_keys.append((f"""transformer.encoder.layers.{i}.norm2.bias""", f"""encoder.layers.{i}.final_layer_norm.bias"""))
					# decoder layers: 2 times output projection, 2 feedforward neural networks and 3 layernorms
					rename_keys.append(
					    (f"""transformer.decoder.layers.{i}.self_attn.out_proj.weight""", f"""decoder.layers.{i}.self_attn.out_proj.weight""")
					)
					rename_keys.append(
					    (f"""transformer.decoder.layers.{i}.self_attn.out_proj.bias""", f"""decoder.layers.{i}.self_attn.out_proj.bias""")
					)
					rename_keys.append(
					    (
					        f"""transformer.decoder.layers.{i}.multihead_attn.out_proj.weight""",
					        f"""decoder.layers.{i}.encoder_attn.out_proj.weight""",
					    )
					)
					rename_keys.append(
					    (
					        f"""transformer.decoder.layers.{i}.multihead_attn.out_proj.bias""",
					        f"""decoder.layers.{i}.encoder_attn.out_proj.bias""",
					    )
					)
					rename_keys.append((f"""transformer.decoder.layers.{i}.linear1.weight""", f"""decoder.layers.{i}.fc1.weight"""))
					rename_keys.append((f"""transformer.decoder.layers.{i}.linear1.bias""", f"""decoder.layers.{i}.fc1.bias"""))
					rename_keys.append((f"""transformer.decoder.layers.{i}.linear2.weight""", f"""decoder.layers.{i}.fc2.weight"""))
					rename_keys.append((f"""transformer.decoder.layers.{i}.linear2.bias""", f"""decoder.layers.{i}.fc2.bias"""))
					rename_keys.append(
					    (f"""transformer.decoder.layers.{i}.norm1.weight""", f"""decoder.layers.{i}.self_attn_layer_norm.weight""")
					)
					rename_keys.append((f"""transformer.decoder.layers.{i}.norm1.bias""", f"""decoder.layers.{i}.self_attn_layer_norm.bias"""))
					rename_keys.append(
					    (f"""transformer.decoder.layers.{i}.norm2.weight""", f"""decoder.layers.{i}.encoder_attn_layer_norm.weight""")
					)
					rename_keys.append(
					    (f"""transformer.decoder.layers.{i}.norm2.bias""", f"""decoder.layers.{i}.encoder_attn_layer_norm.bias""")
					)
					rename_keys.append((f"""transformer.decoder.layers.{i}.norm3.weight""", f"""decoder.layers.{i}.final_layer_norm.weight"""))
					rename_keys.append((f"""transformer.decoder.layers.{i}.norm3.bias""", f"""decoder.layers.{i}.final_layer_norm.bias"""))

# convolutional projection + query embeddings + layernorm of encoder + layernorm of decoder + class and bounding box heads
rename_keys.extend(
    [
        ("input_proj.weight", "input_projection.weight"),
        ("input_proj.bias", "input_projection.bias"),
        ("query_embed.weight", "query_position_embeddings.weight"),
        ("transformer.encoder.norm.weight", "encoder.layernorm.weight"),
        ("transformer.encoder.norm.bias", "encoder.layernorm.bias"),
        ("transformer.decoder.norm.weight", "decoder.layernorm.weight"),
        ("transformer.decoder.norm.bias", "decoder.layernorm.bias"),
        ("class_embed.weight", "class_labels_classifier.weight"),
        ("class_embed.bias", "class_labels_classifier.bias"),
        ("bbox_embed.layers.0.weight", "bbox_predictor.layers.0.weight"),
        ("bbox_embed.layers.0.bias", "bbox_predictor.layers.0.bias"),
        ("bbox_embed.layers.1.weight", "bbox_predictor.layers.1.weight"),
        ("bbox_embed.layers.1.bias", "bbox_predictor.layers.1.bias"),
        ("bbox_embed.layers.2.weight", "bbox_predictor.layers.2.weight"),
        ("bbox_embed.layers.2.bias", "bbox_predictor.layers.2.bias"),
    ]
)





def     _UpperCamelCase     (a__   :List[Any]   ,			a__   :List[Any]   ,			a__   :Any       ):






							"""simple docstring"""





							UpperCamelCase__				     =			state_dict.pop(a__       )
							UpperCamelCase__				     =			val





def     _UpperCamelCase     (a__   :Dict       ):






							"""simple docstring"""





							UpperCamelCase__				     =			OrderedDict()
							for key, value in state_dict.items():
														if "backbone.0.body" in key:
																					UpperCamelCase__				     =			key.replace("""backbone.0.body"""   ,			"""backbone.conv_encoder.model"""       )
																					UpperCamelCase__				     =			value
														else:
																					UpperCamelCase__				     =			value

							return new_state_dict





def     _UpperCamelCase     (a__   :Optional[Any]       ):






							"""simple docstring"""





							UpperCamelCase__				     =			""""""

							# first: transformer encoder
							for i in range(6       ):
														# read in weights + bias of input projection layer (in PyTorch's MultiHeadAttention, this is a single matrix + bias)
														UpperCamelCase__				     =			state_dict.pop(f"""{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight"""       )
														UpperCamelCase__				     =			state_dict.pop(f"""{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_bias"""       )
														# next, add query, keys and values (in that order) to the state dict
														UpperCamelCase__				     =			in_proj_weight[:256, :]
														UpperCamelCase__				     =			in_proj_bias[:256]
														UpperCamelCase__				     =			in_proj_weight[256:512, :]
														UpperCamelCase__				     =			in_proj_bias[256:512]
														UpperCamelCase__				     =			in_proj_weight[-256:, :]
														UpperCamelCase__				     =			in_proj_bias[-256:]
							# next: transformer decoder (which is a bit more complex because it also includes cross-attention)
							for i in range(6       ):
														# read in weights + bias of input projection layer of self-attention
														UpperCamelCase__				     =			state_dict.pop(f"""{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight"""       )
														UpperCamelCase__				     =			state_dict.pop(f"""{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_bias"""       )
														# next, add query, keys and values (in that order) to the state dict
														UpperCamelCase__				     =			in_proj_weight[:256, :]
														UpperCamelCase__				     =			in_proj_bias[:256]
														UpperCamelCase__				     =			in_proj_weight[256:512, :]
														UpperCamelCase__				     =			in_proj_bias[256:512]
														UpperCamelCase__				     =			in_proj_weight[-256:, :]
														UpperCamelCase__				     =			in_proj_bias[-256:]
														# read in weights + bias of input projection layer of cross-attention
														UpperCamelCase__				     =			state_dict.pop(
														    f"""{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight"""       )
														UpperCamelCase__				     =			state_dict.pop(f"""{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_bias"""       )
														# next, add query, keys and values (in that order) of cross-attention to the state dict
														UpperCamelCase__				     =			in_proj_weight_cross_attn[:256, :]
														UpperCamelCase__				     =			in_proj_bias_cross_attn[:256]
														UpperCamelCase__				     =			in_proj_weight_cross_attn[256:512, :]
														UpperCamelCase__				     =			in_proj_bias_cross_attn[256:512]
														UpperCamelCase__				     =			in_proj_weight_cross_attn[-256:, :]
														UpperCamelCase__				     =			in_proj_bias_cross_attn[-256:]





def     _UpperCamelCase     (a__   :List[Any]   ,			a__   :Optional[int]       ):






							"""simple docstring"""





							UpperCamelCase__     ,     UpperCamelCase__				     =			image.size
							UpperCamelCase__				     =			max(a__   ,			a__       )
							UpperCamelCase__				     =			800 if """detection""" in checkpoint_url else 1000
							UpperCamelCase__				     =			target_max_size / current_max_size
							UpperCamelCase__				     =			image.resize((int(round(scale * width       )       ), int(round(scale * height       )       ))       )

							return resized_image





def     _UpperCamelCase     (a__   :List[Any]       ):






							"""simple docstring"""





							UpperCamelCase__				     =			F.to_tensor(a__       )
							UpperCamelCase__				     =			F.normalize(a__   ,			mean=[0.485, 0.456, 0.406]   ,			std=[0.229, 0.224, 0.225]       )
							return image





@torch.no_grad()
def     _UpperCamelCase     (a__   :Dict   ,			a__   :Dict   ,			a__   :int       ):






							"""simple docstring"""





							logger.info("""Converting model..."""       )

							# load original state dict
							UpperCamelCase__				     =			torch.hub.load_state_dict_from_url(a__   ,			map_location="""cpu"""       )
							# rename keys
							for src, dest in rename_keys:
														rename_key(a__   ,			a__   ,			a__       )
							UpperCamelCase__				     =			rename_backbone_keys(a__       )
							# query, key and value matrices need special treatment
							read_in_q_k_v(a__       )
							# important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
							UpperCamelCase__				     =			"""model."""
							for key in state_dict.copy().keys():
														if not key.startswith("""class_labels_classifier"""       ) and not key.startswith("""bbox_predictor"""       ):
																					UpperCamelCase__				     =			state_dict.pop(a__       )
																					UpperCamelCase__				     =			val
    # create HuggingFace model and load state dict
							UpperCamelCase__				     =			TableTransformerConfig(
							    backbone="""resnet18"""   ,			mask_loss_coefficient=1   ,			dice_loss_coefficient=1   ,			ce_loss_coefficient=1   ,			bbox_loss_coefficient=5   ,			giou_loss_coefficient=2   ,			eos_coefficient=0.4   ,			class_cost=1   ,			bbox_cost=5   ,			giou_cost=2   ,			)

							if "detection" in checkpoint_url:
														UpperCamelCase__				     =			15
														UpperCamelCase__				     =			2
														UpperCamelCase__				     =			{0: """table""", 1: """table rotated"""}
														UpperCamelCase__				     =			idalabel
														UpperCamelCase__				     =			{v: k for k, v in idalabel.items()}
							else:
														UpperCamelCase__				     =			125
														UpperCamelCase__				     =			6
														UpperCamelCase__				     =			{
														    0: """table""",
														    1: """table column""",
														    2: """table row""",
														    3: """table column header""",
														    4: """table projected row header""",
														    5: """table spanning cell""",
														}
														UpperCamelCase__				     =			idalabel
														UpperCamelCase__				     =			{v: k for k, v in idalabel.items()}

							UpperCamelCase__				     =			DetrImageProcessor(
							    format="""coco_detection"""   ,			max_size=800 if """detection""" in checkpoint_url else 1000       )
							UpperCamelCase__				     =			TableTransformerForObjectDetection(a__       )
							model.load_state_dict(a__       )
							model.eval()

							# verify our conversion
							UpperCamelCase__				     =			"""example_pdf.png""" if """detection""" in checkpoint_url else """example_table.png"""
							UpperCamelCase__				     =			hf_hub_download(repo_id="""nielsr/example-pdf"""   ,			repo_type="""dataset"""   ,			filename=a__       )
							UpperCamelCase__				     =			Image.open(a__       ).convert("""RGB"""       )
							UpperCamelCase__				     =			normalize(resize(a__   ,			a__       )       ).unsqueeze(0       )

							UpperCamelCase__				     =			model(a__       )

							if "detection" in checkpoint_url:
														UpperCamelCase__				     =			(1, 15, 3)
														UpperCamelCase__				     =			torch.tensor(
														    [[-6.7897, -16.9985, 6.7937], [-8.0186, -22.2192, 6.9677], [-7.3117, -21.0708, 7.4055]]       )
														UpperCamelCase__				     =			torch.tensor([[0.4867, 0.1767, 0.6732], [0.6718, 0.4479, 0.3830], [0.4716, 0.1760, 0.6364]]       )

							else:
														UpperCamelCase__				     =			(1, 125, 7)
														UpperCamelCase__				     =			torch.tensor(
														    [[-18.1430, -8.3214, 4.8274], [-18.4685, -7.1361, -4.2667], [-26.3693, -9.3429, -4.9962]]       )
														UpperCamelCase__				     =			torch.tensor([[0.4983, 0.5595, 0.9440], [0.4916, 0.6315, 0.5954], [0.6108, 0.8637, 0.1135]]       )

							assert outputs.logits.shape == expected_shape
							assert torch.allclose(outputs.logits[0, :3, :3]   ,			a__   ,			atol=1e-4       )
							assert torch.allclose(outputs.pred_boxes[0, :3, :3]   ,			a__   ,			atol=1e-4       )
							print("""Looks ok!"""       )

							if pytorch_dump_folder_path is not None:
														# Save model and image processor
														logger.info(f"""Saving PyTorch model and image processor to {pytorch_dump_folder_path}..."""       )
														Path(a__       ).mkdir(exist_ok=a__       )
														model.save_pretrained(a__       )
														image_processor.save_pretrained(a__       )

							if push_to_hub:
														# Push model to HF hub
														logger.info("""Pushing model to the hub..."""       )
														UpperCamelCase__				     =			(
														    """microsoft/table-transformer-detection"""
														    if """detection""" in checkpoint_url
														    else """microsoft/table-transformer-structure-recognition"""
														)
														model.push_to_hub(a__       )
														image_processor.push_to_hub(a__       )


if __name__ == "__main__":
					UpperCamelCase__											= argparse.ArgumentParser()

					parser.add_argument(
					    "--checkpoint_url",
					    default="https://pubtables1m.blob.core.windows.net/model/pubtables1m_detection_detr_r18.pth",
					    type=str,
					    choices=[
					        "https://pubtables1m.blob.core.windows.net/model/pubtables1m_detection_detr_r18.pth",
					        "https://pubtables1m.blob.core.windows.net/model/pubtables1m_structure_detr_r18.pth",
					    ],
					    help="URL of the Table Transformer checkpoint you'd like to convert.",
					)
					parser.add_argument(
					    "--pytorch_dump_folder_path", default=None, type=str, help="Path to the folder to output PyTorch model."
					)
					parser.add_argument(
					    "--push_to_hub", action="store_true", help="Whether or not to push the converted model to the 🤗 hub."
					)
					UpperCamelCase__											= parser.parse_args()
					convert_table_transformer_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)











import unittest

from transformers import (
    MODEL_FOR_CAUSAL_LM_MAPPING,
    TF_MODEL_FOR_CAUSAL_LM_MAPPING,
    TextGenerationPipeline,
    logging,
    pipeline,
)
from transformers.testing_utils import (
    CaptureLogger,
    is_pipeline_test,
    require_accelerate,
    require_tf,
    require_torch,
    require_torch_gpu,
    require_torch_or_tf,
)

from .test_pipelines_common import ANY

@is_pipeline_test
@require_torch_or_tf
class        __SCREAMING_SNAKE_CASE					(     unittest.TestCase       ):
							snake_case     :      Union[str, Any]						     =       MODEL_FOR_CAUSAL_LM_MAPPING
							snake_case     :      List[str]						     =       TF_MODEL_FOR_CAUSAL_LM_MAPPING






							@require_torch
							def 				_lowerCamelCase		(						self  ):
														UpperCamelCase__				     =			pipeline(task="""text-generation"""  , model="""sshleifer/tiny-ctrl"""  , framework="""pt"""  )
														# Using `do_sample=False` to force deterministic output
														UpperCamelCase__				     =			text_generator("""This is a test"""  , do_sample=__lowerCAmelCase  )
														self.assertEqual(
														    __lowerCAmelCase  , [
														        {
														            """generated_text""": (
														                """This is a test ☃ ☃ segmental segmental segmental 议议eski eski flutter flutter Lacy oscope."""
														                """ oscope. FiliFili@@"""
														            )
														        }
														    ]  , )

														UpperCamelCase__				     =			text_generator(["""This is a test""", """This is a second test"""]  )
														self.assertEqual(
														    __lowerCAmelCase  , [
														        [
														            {
														                """generated_text""": (
														                    """This is a test ☃ ☃ segmental segmental segmental 议议eski eski flutter flutter Lacy oscope."""
														                    """ oscope. FiliFili@@"""
														                )
														            }
														        ],
														        [
														            {
														                """generated_text""": (
														                    """This is a second test ☃ segmental segmental segmental 议议eski eski flutter flutter Lacy"""
														                    """ oscope. oscope. FiliFili@@"""
														                )
														            }
														        ],
														    ]  , )

														UpperCamelCase__				     =			text_generator("""This is a test"""  , do_sample=__lowerCAmelCase  , num_return_sequences=2  , return_tensors=__lowerCAmelCase  )
														self.assertEqual(
														    __lowerCAmelCase  , [
														        {"""generated_token_ids""": ANY(__lowerCAmelCase  )},
														        {"""generated_token_ids""": ANY(__lowerCAmelCase  )},
														    ]  , )
														UpperCamelCase__				     =			text_generator.model.config.eos_token_id
														UpperCamelCase__				     =			"""<pad>"""
														UpperCamelCase__				     =			text_generator(
														    ["""This is a test""", """This is a second test"""]  , do_sample=__lowerCAmelCase  , num_return_sequences=2  , batch_size=2  , return_tensors=__lowerCAmelCase  , )
														self.assertEqual(
														    __lowerCAmelCase  , [
														        [
														            {"""generated_token_ids""": ANY(__lowerCAmelCase  )},
														            {"""generated_token_ids""": ANY(__lowerCAmelCase  )},
														        ],
														        [
														            {"""generated_token_ids""": ANY(__lowerCAmelCase  )},
														            {"""generated_token_ids""": ANY(__lowerCAmelCase  )},
														        ],
														    ]  , )






							@require_tf
							def 				_lowerCamelCase		(						self  ):
														UpperCamelCase__				     =			pipeline(task="""text-generation"""  , model="""sshleifer/tiny-ctrl"""  , framework="""tf"""  )

														# Using `do_sample=False` to force deterministic output
														UpperCamelCase__				     =			text_generator("""This is a test"""  , do_sample=__lowerCAmelCase  )
														self.assertEqual(
														    __lowerCAmelCase  , [
														        {
														            """generated_text""": (
														                """This is a test FeyFeyFey(Croatis.), s.), Cannes Cannes Cannes 閲閲Cannes Cannes Cannes 攵"""
														                """ please,"""
														            )
														        }
														    ]  , )

														UpperCamelCase__				     =			text_generator(["""This is a test""", """This is a second test"""]  , do_sample=__lowerCAmelCase  )
														self.assertEqual(
														    __lowerCAmelCase  , [
														        [
														            {
														                """generated_text""": (
														                    """This is a test FeyFeyFey(Croatis.), s.), Cannes Cannes Cannes 閲閲Cannes Cannes Cannes 攵"""
														                    """ please,"""
														                )
														            }
														        ],
														        [
														            {
														                """generated_text""": (
														                    """This is a second test Chieftain Chieftain prefecture prefecture prefecture Cannes Cannes"""
														                    """ Cannes 閲閲Cannes Cannes Cannes 攵 please,"""
														                )
														            }
														        ],
														    ]  , )






							def 				_lowerCamelCase		(						self  , __lowerCAmelCase  , __lowerCAmelCase  , __lowerCAmelCase  ):
														UpperCamelCase__				     =			TextGenerationPipeline(model=__lowerCAmelCase  , tokenizer=__lowerCAmelCase  )
														return text_generator, ["This is a test", "Another test"]






							def 				_lowerCamelCase		(						self  ):
														UpperCamelCase__				     =			"""Hello I believe in"""
														UpperCamelCase__				     =			pipeline("""text-generation"""  , model="""hf-internal-testing/tiny-random-gpt2"""  )
														UpperCamelCase__				     =			text_generator(__lowerCAmelCase  )
														self.assertEqual(
														    __lowerCAmelCase  , [{"""generated_text""": """Hello I believe in fe fe fe fe fe fe fe fe fe fe fe fe"""}]  , )

														UpperCamelCase__				     =			text_generator(__lowerCAmelCase  , stop_sequence=""" fe"""  )
														self.assertEqual(__lowerCAmelCase  , [{"""generated_text""": """Hello I believe in fe"""}]  )






							def 				_lowerCamelCase		(						self  , __lowerCAmelCase  , __lowerCAmelCase  ):
														UpperCamelCase__				     =			text_generator.model
														UpperCamelCase__				     =			text_generator.tokenizer

														UpperCamelCase__				     =			text_generator("""This is a test"""  )
														self.assertEqual(__lowerCAmelCase  , [{"""generated_text""": ANY(__lowerCAmelCase  )}]  )
														self.assertTrue(outputs[0]["""generated_text"""].startswith("""This is a test"""  )  )

														UpperCamelCase__				     =			text_generator("""This is a test"""  , return_full_text=__lowerCAmelCase  )
														self.assertEqual(__lowerCAmelCase  , [{"""generated_text""": ANY(__lowerCAmelCase  )}]  )
														self.assertNotIn("""This is a test"""  , outputs[0]["""generated_text"""]  )

														UpperCamelCase__				     =			pipeline(task="""text-generation"""  , model=__lowerCAmelCase  , tokenizer=__lowerCAmelCase  , return_full_text=__lowerCAmelCase  )
														UpperCamelCase__				     =			text_generator("""This is a test"""  )
														self.assertEqual(__lowerCAmelCase  , [{"""generated_text""": ANY(__lowerCAmelCase  )}]  )
														self.assertNotIn("""This is a test"""  , outputs[0]["""generated_text"""]  )

														UpperCamelCase__				     =			text_generator("""This is a test"""  , return_full_text=__lowerCAmelCase  )
														self.assertEqual(__lowerCAmelCase  , [{"""generated_text""": ANY(__lowerCAmelCase  )}]  )
														self.assertTrue(outputs[0]["""generated_text"""].startswith("""This is a test"""  )  )

														UpperCamelCase__				     =			text_generator(["""This is great !""", """Something else"""]  , num_return_sequences=2  , do_sample=__lowerCAmelCase  )
														self.assertEqual(
														    __lowerCAmelCase  , [
														        [{"""generated_text""": ANY(__lowerCAmelCase  )}, {"""generated_text""": ANY(__lowerCAmelCase  )}],
														        [{"""generated_text""": ANY(__lowerCAmelCase  )}, {"""generated_text""": ANY(__lowerCAmelCase  )}],
														    ]  , )

														if text_generator.tokenizer.pad_token is not None:
																					UpperCamelCase__				     =			text_generator(
																					    ["""This is great !""", """Something else"""]  , num_return_sequences=2  , batch_size=2  , do_sample=__lowerCAmelCase  )
																					self.assertEqual(
																					    __lowerCAmelCase  , [
																					        [{"""generated_text""": ANY(__lowerCAmelCase  )}, {"""generated_text""": ANY(__lowerCAmelCase  )}],
																					        [{"""generated_text""": ANY(__lowerCAmelCase  )}, {"""generated_text""": ANY(__lowerCAmelCase  )}],
																					    ]  , )

														with self.assertRaises(__lowerCAmelCase  ):
																					UpperCamelCase__				     =			text_generator("""test"""  , return_full_text=__lowerCAmelCase  , return_text=__lowerCAmelCase  )
														with self.assertRaises(__lowerCAmelCase  ):
																					UpperCamelCase__				     =			text_generator("""test"""  , return_full_text=__lowerCAmelCase  , return_tensors=__lowerCAmelCase  )
														with self.assertRaises(__lowerCAmelCase  ):
																					UpperCamelCase__				     =			text_generator("""test"""  , return_text=__lowerCAmelCase  , return_tensors=__lowerCAmelCase  )

														# Empty prompt is slighly special
														# it requires BOS token to exist.
														# Special case for Pegasus which will always append EOS so will
														# work even without BOS.
														if (
														    text_generator.tokenizer.bos_token_id is not None
														    or "Pegasus" in tokenizer.__class__.__name__
														    or "Git" in model.__class__.__name__
														):
																					UpperCamelCase__				     =			text_generator(""""""  )
																					self.assertEqual(__lowerCAmelCase  , [{"""generated_text""": ANY(__lowerCAmelCase  )}]  )
														else:
																					with self.assertRaises((ValueError, AssertionError)  ):
																												UpperCamelCase__				     =			text_generator(""""""  )

														if text_generator.framework == "tf":
																					# TF generation does not support max_new_tokens, and it's impossible
																					# to control long generation with only max_length without
																					# fancy calculation, dismissing tests for now.
																					return
														# We don't care about infinite range models.
														# They already work.
														# Skip this test for XGLM, since it uses sinusoidal positional embeddings which are resized on-the-fly.
														UpperCamelCase__				     =			["""RwkvForCausalLM""", """XGLMForCausalLM""", """GPTNeoXForCausalLM"""]
														if (
														    tokenizer.model_max_length < 10000
														    and text_generator.model.__class__.__name__ not in EXTRA_MODELS_CAN_HANDLE_LONG_INPUTS
														):
																					# Handling of large generations
																					with self.assertRaises((RuntimeError, IndexError, ValueError, AssertionError)  ):
																												text_generator("""This is a test""" * 500  , max_new_tokens=20  )

																					UpperCamelCase__				     =			text_generator("""This is a test""" * 500  , handle_long_generation="""hole"""  , max_new_tokens=20  )
																					# Hole strategy cannot work
																					with self.assertRaises(__lowerCAmelCase  ):
																												text_generator(
																												    """This is a test""" * 500  , handle_long_generation="""hole"""  , max_new_tokens=tokenizer.model_max_length + 10  , )






							@require_torch
							@require_accelerate
							@require_torch_gpu
							def 				_lowerCamelCase		(						self  ):
														import torch

														# Classic `model_kwargs`
														UpperCamelCase__				     =			pipeline(
														    model="""hf-internal-testing/tiny-random-bloom"""  , model_kwargs={"""device_map""": """auto""", """torch_dtype""": torch.bfloataa}  , )
														self.assertEqual(pipe.model.device  , torch.device(0  )  )
														self.assertEqual(pipe.model.lm_head.weight.dtype  , torch.bfloataa  )
														UpperCamelCase__				     =			pipe("""This is a test"""  )
														self.assertEqual(
														    __lowerCAmelCase  , [
														        {
														            """generated_text""": (
														                """This is a test test test test test test test test test test test test test test test test"""
														                """ test"""
														            )
														        }
														    ]  , )

														# Upgraded those two to real pipeline arguments (they just get sent for the model as they're unlikely to mean anything else.)
														UpperCamelCase__				     =			pipeline(model="""hf-internal-testing/tiny-random-bloom"""  , device_map="""auto"""  , torch_dtype=torch.bfloataa  )
														self.assertEqual(pipe.model.device  , torch.device(0  )  )
														self.assertEqual(pipe.model.lm_head.weight.dtype  , torch.bfloataa  )
														UpperCamelCase__				     =			pipe("""This is a test"""  )
														self.assertEqual(
														    __lowerCAmelCase  , [
														        {
														            """generated_text""": (
														                """This is a test test test test test test test test test test test test test test test test"""
														                """ test"""
														            )
														        }
														    ]  , )

														# torch_dtype will be automatically set to float32 if not provided - check: https://github.com/huggingface/transformers/pull/20602
														UpperCamelCase__				     =			pipeline(model="""hf-internal-testing/tiny-random-bloom"""  , device_map="""auto"""  )
														self.assertEqual(pipe.model.device  , torch.device(0  )  )
														self.assertEqual(pipe.model.lm_head.weight.dtype  , torch.floataa  )
														UpperCamelCase__				     =			pipe("""This is a test"""  )
														self.assertEqual(
														    __lowerCAmelCase  , [
														        {
														            """generated_text""": (
														                """This is a test test test test test test test test test test test test test test test test"""
														                """ test"""
														            )
														        }
														    ]  , )






							@require_torch
							@require_torch_gpu
							def 				_lowerCamelCase		(						self  ):
														import torch

														UpperCamelCase__				     =			pipeline(model="""hf-internal-testing/tiny-random-bloom"""  , device=0  , torch_dtype=torch.floataa  )
														pipe("""This is a test"""  )






							@require_torch
							@require_accelerate
							@require_torch_gpu
							def 				_lowerCamelCase		(						self  ):
														import torch

														UpperCamelCase__				     =			pipeline(model="""hf-internal-testing/tiny-random-bloom"""  , device_map="""auto"""  , torch_dtype=torch.floataa  )
														pipe("""This is a test"""  , do_sample=__lowerCAmelCase  , top_p=0.5  )







							def 				_lowerCamelCase		(						self  ):
														UpperCamelCase__				     =			"""Hello world"""
														UpperCamelCase__				     =			pipeline("""text-generation"""  , model="""hf-internal-testing/tiny-random-gpt2"""  )
														if text_generator.model.framework == "tf":
																					UpperCamelCase__				     =			logging.get_logger("""transformers.generation.tf_utils"""  )
														else:
																					UpperCamelCase__				     =			logging.get_logger("""transformers.generation.utils"""  )
														UpperCamelCase__				     =			"""Both `max_new_tokens`"""  # The beggining of the message to be checked in this test

														# Both are set by the user -> log warning
														with CaptureLogger(__lowerCAmelCase  ) as cl:
																					UpperCamelCase__				     =			text_generator(__lowerCAmelCase  , max_length=10  , max_new_tokens=1  )
														self.assertIn(__lowerCAmelCase  , cl.out  )

														# The user only sets one -> no warning
														with CaptureLogger(__lowerCAmelCase  ) as cl:
																					UpperCamelCase__				     =			text_generator(__lowerCAmelCase  , max_new_tokens=1  )
														self.assertNotIn(__lowerCAmelCase  , cl.out  )

														with CaptureLogger(__lowerCAmelCase  ) as cl:
																					UpperCamelCase__				     =			text_generator(__lowerCAmelCase  , max_length=10  )
														self.assertNotIn(__lowerCAmelCase  , cl.out  )




